A Whole-Cell Computational Model Predicts Phenotype from Genotype
Open accessAbstract
SUMMARY Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication rates. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.
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- Findingcitation
Predicted high-occupancy RNA polymerase regions correspond to highly transcribed rRNAs and tRNAs.
“Consistent with a recent experimental study by Vora et al., 2009, the predicted high-occupancy RNA polymerase regions correspond to highly transcribed rRNAs and tRNAs.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Vora et al., 2009 source
- Interpretationown data
The results support the assertion that large-scale modeling can aid biological discovery.
“These results support the assertion that large-scale modeling can be used to support biological discovery (Kitano, 2002; Brenner, 2010).”
Quote in the paperSubstantial claim1 of 2 sources checked - Findingown data
Model calculations matched the observed doubling time.
“We found that the model calculations were consistent with the observed doubling time (Figure 2A and 2B), cellular chemical composition (Figure 2C), replication of major cell mass fractions (Figure 2D), and gene expression (R2 = 0.68; Figure S1A).”
Quote in the paperSubstantial claim - Findingown data
The authors built a whole-cell computational model of the M. genitalium life cycle that incorporates all molecular components and their interactions.
“We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
- Findingown data
The model predicts an inverse relation between the length of DNA replication initiation and replication rate.
“including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication rates.”
Quote in the paperNot a substantial claim
- Findingown data
Model calculations matched gene expression with R2 = 0.68.
“We found that the model calculations were consistent with the observed doubling time (Figure 2A and 2B), cellular chemical composition (Figure 2C), replication of major cell mass fractions (Figure 2D), and gene expression (R2 = 0.68; Figure S1A).”
Quote in the paperSubstantial claim - Findingnone given
The model covers every annotated gene function.
“Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data.”
Quote in the paperSubstantial claim - Findingcitation
The measured DNA content per cell is less than one third of the calculated chromosome mass.
“the experimentally measured DNA content per cell (Morowitz et al., 1962; Morowitz, 1992) represents less than one third of the calculated mass of the mycoplasma chromosome.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Morowitz et al., 1962 source
- cites Morowitz, 1992
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingown data
Experiments guided by model predictions found kinetic parameters and biological functions not previously detected.
“In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions.”
Quote in the paperSubstantial claim - Findingown data
Model calculations matched replication of major cell mass fractions.
“We found that the model calculations were consistent with the observed doubling time (Figure 2A and 2B), cellular chemical composition (Figure 2C), replication of major cell mass fractions (Figure 2D), and gene expression (R2 = 0.68; Figure S1A).”
Quote in the paperSubstantial claim - Findingown data
Model calculations matched cellular chemical composition.
“We found that the model calculations were consistent with the observed doubling time (Figure 2A and 2B), cellular chemical composition (Figure 2C), replication of major cell mass fractions (Figure 2D), and gene expression (R2 = 0.68; Figure S1A).”
Quote in the paperSubstantial claim - Findingreasoning
The replication phase duration in individual cells relates more closely to free dNTP content at replication start than to dNTP content at cell cycle start.
“Accordingly, the duration of the replication phase in individual cells is more closely related to the free dNTP content at the start of replication than to the dNTP content at the start of the cell cycle (Figure 4D).”
Quote in the paperSubstantial claim - Findingown data
Four gene disruption strains had growth rates quantitatively different from model predictions.
“Four gene disruption strains exhibited growth rates that were quantitatively different than those predicted by the model (Figure 7A)”
Quote in the paperSubstantial claim - Findingown data
A third class of lethal disruptions impaired cell cycle processes, with normal growth rates and metabolism but inability to complete the cell cycle.
“A third class of lethal gene disruptions impaired cell cycle processes. For these, the model predicted normal growth rates and metabolism, but incapacity to complete the cell cycle.”
Quote in the paperSubstantial claim - Findingown data
To reproduce the observed growth rate without lpdA, the hypothetical Nox-dependent reaction would need a kcat of about 50 s−1, roughly 5% of the enzyme's maximum throughput.
“Our model suggests that to reproduce the observed growth rate in the absence of lpdA, the hypothetical Nox-dependent reaction would require a kcat of approximately 50 s−1 (Figure 7E), which represents only approximately 5% of the maximum throughput of this enzyme.”
Quote in the paperSubstantial claim - Findingown data
ATP and GTP usage varied little except in the slowest simulations.
“Notably, the overall usage of ATP and GTP did not vary considerably in all but the very slowest of our simulations (Figure 5B)”
Quote in the paperSubstantial claim - Findingown data
The flux of protein-protein collisions correlates strongly with DNA-bound protein density.
“Further categorization of the predicted collisions by chromosomal location indicates that the flux of protein-protein collisions correlates strongly with DNA-bound protein density (Figure 3F)”
Quote in the paperNot a substantial claim - Findingown data
Cell-to-cell variation was greater for replication initiation (64.3%) and replication (38.5%) durations than for cytokinesis (4.4%) or the overall cell cycle (9.4%).
“We found that there was relatively more cell-to-cell variation in the durations of the replication initiation (64.3%) and replication (38.5%) stages than in cytokinesis (4.4%) or the overall cell cycle (9.4%; Figure 4A).”
Quote in the paperSubstantial claim - Findingown data
The newly predicted kcat values agree with previously reported values.
“Significantly, these newly predicted kcat values are consistent with previously reported values.”
Quote in the paperNot a substantial claim - Findingown data
ATP and GTP usage is dominated by mRNA and protein production.
“We then considered the processes that use ATP and GTP, and found that usage is dominated by production of mRNA and protein (Figure 5C).”
Quote in the paperNot a substantial claim - Findingown data
The model matches previously observed gene essentiality with 79% accuracy (P < 10−7).
“The model accounts for previously observed gene essentiality with 79% accuracy (P < 10−7; Glass et al., 2006; Figure 6A).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Glass et al., 2006 source
- Findingcitation
Predicted mRNA and protein level distributions agree with recent single-cell measurements.
“The mRNA and protein level distributions predicted by our model are also consistent with recently reported single-cell measurements (Figure 2H, compare to Taniguichi et al., 2010).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Taniguichi et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingreasoning
Initiation and replication phase durations are inversely related in single cells, so longer initiation times lead to shorter replication times.
“Specifically, the duration of the initiation and replication phases are inversely related to each other in single cells (Figure 4E), such that longer initiation times led to shorter replication times.”
Quote in the paperSubstantial claim - Findingown data
The next most debilitating disruptions affected synthesis of a specific cell mass component such as RNA or protein, causing initial near-normal growth followed by decline due to diminishing protein content.
“The next most debilitating gene disruptions impacted the synthesis of a specific cell mass component such as RNA or protein. Interestingly, in these cases the model predicted an initial phase of near-normal growth followed by decreasing growth due to diminishing protein content.”
Quote in the paperSubstantial claim - Findingown data
Non-viable strains failed to adequately perform one or more major functions.
“As shown in Figure 6B, the non-viable strains were unable to adequately perform one or more of these major functions.”
Quote in the paperSubstantial claim - Findingown data
There is a correlation (R2 = 0.49) between predicted replication initiation duration and the initial number of free DnaA monomers.
“We found a correlation (R2 = 0.49) between the predicted duration of replication initiation and the initial number of free DnaA monomers (Figure 4C)”
Quote in the paperNot a substantial claim - Findingown data
In some cases, specific protein levels took more than one generation to fall to lethal levels.
“In some cases (Figure 6B, fifth column), the time required for the levels of specific proteins to fall to lethal levels was greater than one generation (Figure 6C and 6D).”
Quote in the paperNot a substantial claim - Findingown data
No growth was detected for the lpdA disruption with the colorimetric assay.
“However, we did not detect growth using our colorimetric assay (Figure 7B), a discrepancy that warranted further investigation.”
Quote in the paperSubstantial claim - Findingown data
A Pdp kcat range was found for which wild type and deoD disruption strains have the same growth rate.
“We identified a Pdp kcat range for which the wild type and deoD gene disruption strains produce the same growth rate (Figure 7G).”
Quote in the paperSubstantial claim - Findingcitation
The model predicts burst-like protein synthesis caused by intermittent mRNA expression locally and stochastic protein degradation globally affecting free amino acid availability for translation.
“Our model also predicts “burst-like” protein synthesis due to the local effect of intermittent mRNA expression and the global effect of stochastic protein degradation on the availability of free amino acids for translation, comparable to recent reports by Yu et al., 2006 and So et al., 2011 (Figure 2G).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Yu et al., 2006
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites So et al., 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingown data
The model predicts glycolysis flux is more than 100-fold greater than flux through pentose phosphate and lipid biosynthesis pathways.
“the model predicts that the flux through glycolysis is >100-fold more than that through the pentose phosphate and lipid biosynthesis pathways (Figure 2E).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Yus et al., 2009 source
- Findingown data
RNA polymerase binds 90% of the chromosome within the first 49 min.
“RNA polymerase contributes the most to chromosomal exploration, binding 90% of the chromosome within the first 49 min of the cell cycle.”
Quote in the paperSubstantial claim - Findingown data
ATP and GTP are synthesized at rates over 1,000-fold higher than FAD(H2), NAD(H), and NADP(H).
“we investigated the synthesis dynamics of the high energy intermediates ATP, GTP, FAD(H2), NAD(H), and NADP(H), and found that ATP and GTP are synthesized at rates over 1,000-fold higher than the others (Figure 5A).”
Quote in the paperNot a substantial claim - Findingown data
An alternative doubling-time method gave a value 40% lower than wild type for the lpdA disruption.
“An alternative method to determine the doubling time yielded a value that was 40% lower than the wild type (Table S1).”
Quote in the paperSubstantial claim - Findingown data
Only a small range of Tdk kcat values both lowered thyA strain growth to observed levels and remained consistent with wild type growth.
“Only a small range of the kcat values both reduced the thyA strain growth rate to the experimentally observed levels and was also consistent with the wild type growth rate.”
Quote in the paperSubstantial claim - Findingown data
Two-thirds of predictions matched measured growth rates.
“We found that two-thirds of the predictions were consistent with the measured growth rates.”
Quote in the paperSubstantial claim - Findingown data
The Nox sequence is far more similar to LpdA than any other gene product, with 61% coverage, 25% identity, and an expectation value below 10−6.
“We found that the Nox sequence was far more similar to the LpdA sequence than any other gene product in the genome, with 61% coverage, 25% identity, and an expectation value less than 10−6 (Figure 7C).”
Quote in the paperSubstantial claim - Findingown data
There is a 44% discrepancy between total energy usage and production.
“We also found a large (44%) discrepancy between total energy usage and production (Figure 5D).”
Quote in the paperNot a substantial claim - Findingown data
The model predicts DNA replication proceeds at two distinct rates during the cell cycle.
“Instead, the model predicts that DNA replication proceeds at two distinct rates during the cell cycle.”
Quote in the paperSubstantial claim - Findingown data
Remaining lethal strains grew so slowly relative to wild type that they were considered non-viable.
“The remaining lethal gene disruption strains grew so slowly compared to wild type that they were considered non-viable (Figure 6B, Figure S2).”
Quote in the paperNot a substantial claim - Findingreasoning
Much of the data used to build and validate the model came from other organisms.
“As a result, much of the data used to build and validate the model was obtained from other organisms.”
Quote in the paperSubstantial claim- Rests on an assumption: Data and parameters obtained from other organisms are applicable enough to M. genitalium to build and validate the model.
- Premisereasoning
No single computational method can explain complex phenotypes from molecular components and interactions.
“The second limiting factor has been that no single computational method is sufficient to explain complex phenotypes in terms of molecular components and their interactions.”
Quote in the paperSubstantial claim - Study design and conductown data
The model reproduces key features of the training data.
“Once the model was implemented and all parameters reconciled, we verified that the model recapitulates key features of our training data.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
- Study design and conductown data
The model was checked against a wide range of data.
“Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data.”
Quote in the paperSubstantial claim - Premisecitation
Early ODE-based models of cellular physiology were limited by difficulty in obtaining required parameters.
“The first approaches to modeling cellular physiology, based on ordinary differential equations (ODEs) (Atlas et al., 2008; Browning et al., 2004; Castellanos et al., 2004; Castellanos et al., 2007; Domach et al., 1984; Tomita et al., 1999), were limited by the difficulty in obtaining the necessary model parameters.”
Quote in the paperNot a substantial claim0 of 3 sources checked - Interpretationreasoning
Combining diverse mathematical approaches in an integrative model allowed different cellular processes and experimental measurements to be included together.
“An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
- Interpretationown data
Whole-cell models of this comprehensive kind can help drive biological discovery.
“We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.”
Quote in the paperNot a substantial claim - Study design and conductown data
The model was validated against independent datasets not used in its construction, covering metabolomics, transcriptomics, proteomics, and scales from single cells to populations.
“Next, we validated the model against a broad range of independent datasets that were not used to construct the model and which encompass multiple biological functions – metabolomics, transcriptomics, and proteomics – and scales, from single cells to populations.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
- Premisecitation
The lpdA gene was originally reported as non-essential.
“The lpdA gene was originally determined to be non-essential (Glass et al., 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Glass et al., 2006 source
- Interpretationreasoning
This interplay buffers against high variability in initiation duration, reducing variability in cell cycle length.
“This interplay buffers against the high variability in the duration of replication initiation, giving rise to substantially less variability in the length of the cell cycle.”
Quote in the paperSubstantial claim - Interpretationreasoning
The model can classify cellular phenotypes by their underlying molecular interactions.
“We conclude that the model can be used to classify cellular phenotypes by their underlying molecular interactions.”
Quote in the paperSubstantial claim - Interpretationreasoning
Substrate promiscuity of Nox likely allows the lpdA disruption strain to survive.
“We therefore concluded that substrate promiscuity of Nox is likely to enable the lpdA disruption strain to survive.”
Quote in the paperSubstantial claim - Premisecitation
Replication initiation involves DnaA monomers binding and unbinding stochastically and cooperatively to form a multimeric complex at the origin.
“replication initiation occurs as DnaA protein monomers bind or unbind stochastically and cooperatively to form a multimeric complex at the replication origin (Figure 4B, top) (Browning et al., 2004).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Browning et al., 2004 source
- Interpretationown data
The model's prediction estimates the total energy cost of uncoupling between catabolism and anabolism.
“Others have noted an uncoupling between catabolism and anabolism, attributing the difference to factors such as varying maintenance costs or energy spilling via futile cycles (Russell et al., 1995), and the model’s prediction estimates the total energy cost of such uncoupling.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Russell et al., 1995 source
- Premisecitation
The nox gene product NADH oxidase has been shown to oxidize NAD.
“Furthermore, the nox gene product, NADH oxidase, has been shown to oxidize NAD (Schmidt et al., 1986).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Schmidt et al., 1986 source
- Interpretationown data
Replication initiation duration depends on more than initial conditions.
“however, the somewhat low correlation indicated that the duration depends on more than the initial conditions.”
Quote in the paperSubstantial claim - Interpretationreasoning
The viability of the lpdA disruption suggests another enzyme with lower catalytic efficiency could catalyze the reaction.
“The viability of the lpdA disruption strain suggests that this reaction could be catalyzed by another enzyme with a lower catalytic efficiency.”
Quote in the paperSubstantial claim - Interpretationreasoning
The model suggests a novel hypothesis of emergent cell-cycle duration control independent of genetic regulation.
“The whole-cell model therefore presents a novel hypothesis of an emergent control of cell-cycle duration that is independent of genetic regulation.”
Quote in the paperSubstantial claim - Interpretationreasoning
The model accurately reproduces a broad set of experimental data.
“Our model accurately recapitulates a broad set of experimental data, provides insight into several biological processes for which experimental assessment is not readily feasible, and enables the rapid identification of novel gene functions as well as specific cellular parameters.”
Quote in the paperSubstantial claim - Interpretationreasoning
The model provides insight into biological processes that are not readily experimentally assessable.
“Our model accurately recapitulates a broad set of experimental data, provides insight into several biological processes for which experimental assessment is not readily feasible, and enables the rapid identification of novel gene functions as well as specific cellular parameters.”
Quote in the paperSubstantial claim - Interpretationreasoning
Variability in replication initiation duration comes from both initial conditions and the simulation itself.
“This indicates that the variability in replication initiation duration depends not only on variability in initial conditions, but also in the simulation itself.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Taken together, the model reproduces experimental data across multiple biological functions and scales.
“Taking all of these specific tests of the model's predictions together, we concluded that our model recapitulates experimental data across multiple biological functions and scales.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
- Classified as probably not a substantial claim. 7
- May combine two claims. 26
- Borderline: one of the tests for a substantial claim is unclear. 1
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLater methods needing fewer parameters include Boolean network and constraint-based modeling.
“Subsequently, alternative approaches were developed that require fewer parameters, including Boolean network modeling (Davidson et al., 2002) and constraint-based modeling (Orth et al., 2010; Thiele et al., 2009).”
Quote in the paperNot a substantial claim2 of 3 sources checkedMay combine two claims.
Findingown dataPredicted metabolite concentrations are within an order of magnitude of E. coli measurements for 100% of metabolites in one dataset and 70% in a newer high-throughput study.
“the predicted metabolite concentrations are within an order of magnitude of concentrations measured in Escherichia coli for 100% of the metabolites in one compilation of data (Sundararaj et al., 2004) and for 70% in a more recent high-throughput study (Bennett et al., 2009; Figure 2F).”
Quote in the paperSubstantial claim1 of 2 sources checkedBorderline on S3 (load-bearing), S4 (based): unclear from the text. May combine two claims.
Findingnone givenThe model predicts over 30,000 protein-protein collisions per cell cycle on average, displacing 0.93 proteins per second.
“Our model predicts that over 30,000 collisions occur on average per cell cycle, leading to the displacement of 0.93 proteins per second.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe most debilitating disruptions involved metabolic genes and prevented production of any major cell mass components.
“The most debilitating disruptions involved metabolic genes and resulted in the inability to produce any of the major cell mass components.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMost collisions are caused by RNA polymerase (84%) and DNA polymerase (8%), and most commonly displace SMC proteins (70%) or single-stranded binding proteins (6%).
“the majority of collisions are caused by RNA polymerase (84%) and DNA polymerase (8%), most commonly resulting in the displacement of Structural Maintenance of Chromosome (SMC) proteins (70%), or single-stranded binding proteins (6%) (Figure 3E and Table S2F).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe model predicts that 50% of the chromosome is bound by at least one protein within 6 min and 90% within 20 min.
“The model further predicts that the chromosome is explored very rapidly, with 50% of the chromosome having been bound by at least one protein within the first 6 min of the cell cycle, and 90% within the first 20 min (Figure 3B).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataReducing Tdk kcat in the model lowered the predicted thyA strain growth rate but also affected wild type growth rate.
“Reducing the Tdk kcat in the model did indeed reduce the predicted growth rate of the thyA strain, but it also affected the wild type growth rate (Figure 7F).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataGrowth rates of twelve single-gene disruption strains were measured experimentally; ten were correctly predicted viable and two incorrectly predicted nonviable.
“we experimentally measured the growth rates of twelve single-gene disruption strains – ten of which were correctly predicted to be viable and two of which that were incorrectly predicted to be nonviable – for comparison to our models' predictions (Figure 7A).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataStochastic transcription and translation create variability in new DnaA monomer numbers over time and in DnaA binding and unbinding events.
“we observed that the stochastic aspect of the transcription and translation sub-models creates variability in the number of new DnaA monomers produced over time, as well as the DnaA binding and unbinding events themselves.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataPredicted DNA polymerase chromosomal occupancy is significantly lower and biased toward terC.
“In contrast, the predicted DNA polymerase chromosomal occupancy is significantly lower and biased toward the terC (see below for further discussion).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFor Tdk and Pdp, original values came from distantly related organisms, while newly predicted kcat values agree with reports from more closely related species.
“For Tdk and Pdp, these values corresponded to distantly related organisms; however, the newly predicted kcat values are consistent with reports from more closely related species (Figure 7H).”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationThe model predicts 90% of genes are expressed within the first 143 min, with transcription lagging RNA polymerase exploration because non-specific RNA polymerase-DNA interactions contribute to RNA polymerase diffusion.
“On average, this results in expression of 90% of genes within the first 143 min (Figure 3C), with transcription lagging RNA polymerase exploration due to the significant contribution of non-specific RNA polymerase-DNA interactions to RNA polymerase diffusion (Harada et al., 1999).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Harada et al., 1999 source
Classified as probably not a substantial claim.
Study design and conductnone givenThe model rested on a detailed M. genitalium reconstruction assembled from more than 900 primary sources, reviews, books, and databases.
“The whole-cell model was based on a detailed reconstruction of M. genitalium developed from over 900 primary sources, reviews, books, and databases.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
May combine two claims.
Study design and conductnone givenThe model synthesizes over 900 publications and contains more than 1,900 experimentally observed parameters.
“Our model is based on a synthesis of over 900 publications and includes more than 1,900 experimentally observed parameters.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
May combine two claims.
PremisereasoningThe assumptions behind Boolean and constraint-based methods do not hold for all cellular processes and conditions, so a whole-cell model based only on either is impractical.
“However, the underlying assumptions of these methods do not apply to all cellular processes and conditions, and building a whole-cell model entirely based on either method is therefore impractical.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationM. genitalium is a human urogenital parasite with 525 genes in its genome.
“Here, we present a “whole-cell” model of the bacterium Mycoplasma genitalium, a human urogenital parasite whose genome contains 525 genes (Fraser et al., 1995).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Fraser et al., 1995 source
Classified as probably not a substantial claim.
Premisenone givenBefore recent advances, knowledge of individual molecules and their interactions was insufficient to model any organism completely.
“First, until recently not enough has been known about the individual molecules and their interactions to completely model any one organism.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataIn the lpdA, deoD, and thyA cases, resolving model-experiment discrepancies led to further analysis that resolved the discrepancy and gave new insight into M. genitalium biology.
“In each of these three cases (lpdA, deoD, thyA), identifying a discrepancy between model predictions and experimental measurements led to further analysis which resolved the discrepancy, and also provided novel insight into M. genitalium biology (Figure 7I).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenThe thyA gene product catalyzes dTMP production and can be complemented by the tdk gene product.
“The thyA gene product catalyzes dTMP production and can be complemented by the tdk gene product.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe lpdA gene product is part of the pyruvate dehydrogenase complex, which transfers electrons to NAD as part of the overall pyruvate dehydrogenase reaction.
“The lpdA gene product is part of the pyruvate dehydrogenase complex, which catalyzes the transfer of electrons to NAD as a subset of the overall pyruvate dehydrogenase chemical reaction (de Kok et al., 1998).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites de Kok et al., 1998 source
May combine two claims.
Interpretationown dataReplication initially proceeds quickly because dNTPs are abundant; after the dNTP pool is exhausted, replication slows to the rate of dNTP synthesis.
“Initially replication proceeds quickly, due to the free dNTP content in the cell (Figure 4B, bottom). When DNA polymerase initially binds to the replication origin, dNTPs are abundant and replication can proceed unimpeded. When the dNTP pool is exhausted, however, the rate of replication slows to the rate of dNTP synthesis.”
Quote in the paperSubstantial claimMay combine two claims.
Premisenone givenDeoD converts deoxyadenosine to adenine and D-ribose-1-phosphate; the pdp gene product can also produce these products from deoxyuridine.
“DeoD catalyzes the conversion of deoxyadenosine to adenine and D-ribose-1-phosphate; these products can also be produced by the pdp gene product from deoxyuridine.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe whole-cell model can predict instantaneous chromosomal occupancy and the temporal dynamics and interactions of every DNA-binding protein at genomic scale and single-cell resolution.
“the whole-cell model can predict both the instantaneous protein chromosomal occupancy as well as the temporal dynamics and interactions of every DNA-binding protein at the genomic scale at single-cell resolution.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe nox locus lies in a sub-operon containing two other pyruvate dehydrogenase genes and is coexpressed with pdhA.
“Moreover, the nox locus falls in a sub-operon that contains two other pyruvate dehydrogenase genes and has been shown to be coexpressed with pdhA (Guell et al., 2009) (Figure 7D)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Guell et al., 2009 source
Classified as probably not a substantial claim.
PremisecitationMany M. genitalium genes are multi-functional.
“Since previous studies have shown that many M. genitalium genes are multi-functional (Pollack et al., 2002; Cordwell et al., 1997)”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
Interpretationown dataDisrupting lpdA had a severe but non-critical effect on cell growth.
“Taken together, the data suggested that disrupting the lpdA gene had a severe, but non-critical impact on cell growth.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningCells needing extra time to initiate replication accumulate a large dNTP surplus, which speeds replication.
“This occurs because cells that require extra time to initiate replication also build up a large dNTP surplus, leading to faster replication.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe authors developed a comprehensive whole-cell model that accounts for all annotated M. genitalium gene functions and explains emergent behaviors through molecular interactions.
“We have developed a comprehensive, whole-cell model that accounts for all of the annotated gene functions identified in M. genitalium and explains a variety of emergent behaviors in terms of molecular interactions.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
Classified as probably not a substantial claim. May combine two claims.
Premisenone givenM. genitalium is difficult to study experimentally because of resistance to most antibiotics, lack of a chemically defined medium, and a cell size requiring advanced microscopy.
“Resistance to most antibiotics, the lack of a chemically defined medium, and a cell size that requires advanced microscopy techniques for visualization, all greatly limit the range of experimental techniques available to study this organism.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe model enables rapid identification of novel gene functions and specific cellular parameters.
“Our model accurately recapitulates a broad set of experimental data, provides insight into several biological processes for which experimental assessment is not readily feasible, and enables the rapid identification of novel gene functions as well as specific cellular parameters.”
Quote in the paperSubstantial claim
The paper relies on this without support.
Biological systems are modular, so cells can be modeled by dividing them into functional processes, modeling each process independently on a short time scale, and integrating the sub-models at longer time scales.
“Because biological systems are modular, cells can be modeled by (1) dividing cells into functional processes, (2) independently modeling each process on a short time scale, and (3) integrating process sub-models at longer time scales.”
The authors state this as an assumption.
The cellular process sub-models are approximately independent of one another on short time scales of less than one second.
“We began with the assumption that the sub-models are approximately independent on short time scales (less than one second in this work).”
The paper relies on this without support.
Data and parameters obtained from other organisms are applicable enough to M. genitalium to build and validate the model.
“As a result, much of the data used to build and validate the model was obtained from other organisms.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing cell models are often rule-based and combine assumptions about the underlying biological mechanisms with parameters fit from observational data.
“Existing cell models are often rule-based and combine assumptions about the underlying biological mechanisms with parameters fit from observational data.”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [8]
The Introduction discusses Boolean and constraint-based cell models and their underlying assumptions. Results, under “Model training and parameter reconciliation,” reports more than 1,900 experimentally observed parameters in the authors’ model. The paper does not establish that existing cell models are often rule-based or that their parameters are generally fitted to observational data; most parameters in this model were implemented as originally reported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredGenome-wide models have been developed to represent other bacterial organisms, including Escherichia coli.
“Since this pioneering work, genome-wide models have been developed to represent other bacterial organisms, including Escherichia coli.8,20–22”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [8]
The Abstract identifies this paper as a whole-cell model of Mycoplasma genitalium, supporting its role as the pioneering work referenced in the sentence. The Discussion says that modeling E. coli would be needed for comprehensive validation; it does not report a developed genome-wide E. coli model or establish the later developments attributed to the grouped citations. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe first whole-cell model was developed in 2012, representing all 482 genes and molecular functions known for Mycobacterium genitalium.
“The first whole-cell model was developed in 2012, representing all 482 genes and molecular functions known for an organism: the bacteria Mycobacterium genitalium.8”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [8]
The Experimental Procedures (Reconstruction; page unavailable) describe a whole-cell model of M. genitalium and functional annotation of each gene. But the Results (single-gene disruption phenotypes; page unavailable) describe 525 possible single-gene disruptions, not 482, and the organism is Mycoplasma genitalium, not Mycobacterium genitalium. (LLM, conf=0.90)
Experimental — accuracy not yet measuredNo one organism has a complete set of experimentally determined kinetic parameters, chemical composition, and physical characteristics, so the model presented here, as well as other whole-cell models, has depended on the transferability of quantities among related organisms.
“Unfortunately, no one organism has a complete set of experimentally determined kinetic parameters, chemical composition, and physical characteristics, so the model we present here, as well as other whole-cell models, has depended on the transferability of quantities among related organisms.1”
From Bringing the genetically minimal cell to life on a computer in 4D (2026) · cites this paper as [3]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredExisting cell models generally rely on explicitly defined mathematical or computational approaches, such as differential equations, stochastic simulations, or agent-based models.
“They generally rely on explicitly defined mathematical or computational approaches, such as differential equations,7–9 stochastic simulations,10,11 or agent-based models.12,13”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [8]
There have been significant strides in computational modeling of cell states.
“there have been significant strides in computational modeling of cell states”
From Bringing the genetically minimal cell to life on a computer in 4D (2026) · cites this paper as [3]
Self-replicating models and whole-cell models are either mostly qualitative in nature or require an excessive number of parameters that are not currently available.
“The latter two approaches either are mostly qualitative in nature or require an excessive number of parameters that are not currently available.”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Karr et al, 2012
Recent research clearly shows the feasibility of comprehensive large-scale whole-cell computational modelling.
“recent research [10] clearly shows the feasibility of comprehensive large-scale whole-cell computational modelling.”
From Reverse engineering and identification in systems biology: strategies, perspectives and challenges (2014) · cites this paper as [10]
Yeast has a similar intrinsic sensing mechanism to balance growth and stress resistance.
“Recently it was also shown that yeast has a similar intrinsic sensing mechanism to balance growth and stress resistance [160,161].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [161]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Shorter doubling time conflicts with the reported growth impairment
MinorA doubling time 40% lower than wild type indicates faster growth, whereas the authors interpret the result as a severe impairment of growth. The direction of the reported change conflicts with that conclusion.
“An alternative method to determine the doubling time yielded a value that was 40% lower than the wild type (Table S1).”
“Taken together, the data suggested that disrupting the lpdA gene had a severe, but non-critical impact on cell growth.”
Tdk adjustment is attributed to different disruption strains
MinorThe stated hypothesis concerns the tdk disruption strain, but the next sentence says the adjustment confirmed it by reducing growth of the thyA strain. The strain named in the predicted outcome does not match the one in the reported outcome.
“We therefore hypothesized that by reducing the kcat value for Tdk in the model we would see a reduction in the growth rate of the tdk disruption strain.”
“Reducing the Tdk kcat in the model did indeed reduce the predicted growth rate of the thyA strain, but it also affected the wild type growth rate (Figure 7F).”
Sub-model time scale is both below and equal to one second
MinorThe results describe the short time scale used in this work as less than one second, while the methods say each process was modeled independently on a one-second time scale.
“We began with the assumption that the sub-models are approximately independent on short time scales (less than one second in this work).”
“We divided M. genitalium into the 28 functional processes illustrated in Figure 1, and modeled each process independently on a 1 s time scale using different mathematics and different experimental data.”
Cites
Systems biology: a brief overview
Kitano, H. (2002) · Science · doi:10.1126/science.1069492 · Cited by 4,265 · cited 1× · see citing works
“These results support the assertion that large-scale modeling can be used to guide biological discovery (Kitano, 2002; Brenner, 2010).”
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903 · cited 2× · see citing works
“Subsequently, alternative approaches were developed that require fewer parameters, including Boolean network modeling (Davidson et al., 2002) and constraint-based modeling (Orth et al., 2010; Thiele et al., 2009).”
Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome
Gibson, D. G., Glass, J. I., Lartigue, C., Noskov, V. N., Chuang, R.-Y., Algire, M. A., Benders, G. A., Montague, M. G., Ma, L., Moodie, M. M., et al. (2010) · Science · doi:10.1126/science.1190719 · Cited by 2,638 · see citing works
Self-citationThe Minimal Gene Complement of <i>Mycoplasma genitalium</i>
Fraser, C. M., Gocayne, J. D., White, O., Adams, M. D., Clayton, R. A., Fleischmann, R. D., Bult, C. J., Kerlavage, A. R., Sutton, G., Kelley, J. M., et al. (1995) · Science · doi:10.1126/science.270.5235.397 · Cited by 2,545 · cited 2× · see citing works
“Here, we present a ‘‘whole-cell’’ model of the bacterium Mycoplasma genitalium , a human urogenital parasite whose genome contains 525 genes (Fraser et al., 1995).”
Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., Emili, A., & Xie, X. S. (2010) · Science · doi:10.1126/science.1188308 · Cited by 1,982 · cited 2× · see citing works
“We found that the model calculations were consistent with the observed doubling time (Figures 2A and 2B), cellular chemical composition (Figure 2C), replication of major cell mass fractions (Figure 2D), and gene expression ( R 2 = 0.68; Figure S1A).”
ResultInfluentialAbsolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli
Bennett, B. D., Kimball, E. H., Gao, M., Osterhout, R., Van Dien, S. J., & Rabinowitz, J. D. (2009) · Nature Chemical Biology · doi:10.1038/nchembio.186 · Cited by 1,830 · cited 3× · see citing works
“(F) Ratios of observed (Sundararaj et al., 2004; Bennett et al., 2009) and average predicted concentrations of 39 metabolites.”
A genomic regulatory network for development
Davidson, E. H., Rast, J. P., Oliveri, P., Ransick, A., Calestani, C., Yuh, C.-H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., et al. (2002) · Science · doi:10.1126/science.1069883 · Cited by 1,567 · cited 2× · see citing works
“Subsequently, alternative approaches were developed that require fewer parameters, including Boolean network modeling (Davidson et al., 2002) and constraint-based modeling (Orth et al., 2010; Thiele et al., 2009).”
Complete Chemical Synthesis, Assembly, and Cloning of a <i>Mycoplasma genitalium</i> Genome
Gibson, D. G., Benders, G. A., Andrews-Pfannkoch, C., Denisova, E. A., Baden-Tillson, H., Zaveri, J., Stockwell, T. B., Brownley, A., Thomas, D. W., Algire, M. A., et al. (2008) · Science · doi:10.1126/science.1151721 · Cited by 1,243 · see citing works
Self-citationA comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011
Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M., & Palsson, B. Ø. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.65 · Cited by 1,006 · cited 1× · see citing works
“Models of biological processes have been increasing in complexity and scope (Covert et al., 2004; Orth et al., 2011; Thiele et al., 2009), but with efforts at increased inclusiveness of genes, parameters, and molecular functions come a number of challenges.”
Energetics of bacterial growth: balance of anabolic and catabolic reactions
Russell, J. B., & Cook, G. M. (1995) · Microbiological Reviews · doi:10.1128/mr.59.1.48-62.1995 · Cited by 927 · see citing works
Probing Gene Expression in Live Cells, One Protein Molecule at a Time
Yu, J., Xiao, J., Ren, X., Lao, K., & Xie, X. S. (2006) · Science · doi:10.1126/science.1119623 · Cited by 918 · cited 2× · see citing works
“We found that the model calculations were consistent with the observed doubling time (Figures 2A and 2B), cellular chemical composition (Figure 2C), replication of major cell mass fractions (Figure 2D), and gene expression ( R 2 = 0.68; Figure S1A).”
InfluentialEssential genes of a minimal bacterium
Glass, J. I., Assad-Garcia, N., Alperovich, N., Yooseph, S., Lewis, M. R., Maruf, M., Hutchison, C. A., Smith, H. O., & Venter, J. C. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0510013103 · Cited by 912 · cited 5× · see citing works
“Bacterial Culture M. genitalium wild-type and mutant strains with single-gene disruptions by transposon insertion (Glass et al., 2006) were grown in Spiroplasma SP-4 culture media at 37 C and 5% CO 2 .”
InfluentialSelf-citationIntegrating high-throughput and computational data elucidates bacterial networks
Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J., & Palsson, B. O. (2004) · Nature · doi:10.1038/nature02456 · Cited by 792 · cited 3× · see citing works
“Although we and others had previously developed methods to integrate ODEs with Boolean, probabilistic, and constraint-based submodels (Covert et al., 2001, 2004, 2008; Chandrasekaran and Price, 2010), the current effort involved so many different cellular functions and mathematical representations…”
Self-citationE-CELL: software environment for whole-cell simulation
Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T. S., Matsuzaki, Y., Miyoshi, F., Saito, K., Tanida, S., Yugi, K., Venter, J. C., et al. (1999) · Bioinformatics · doi:10.1093/bioinformatics/15.1.72 · Cited by 764 · cited 1× · see citing works
“The first approaches to modeling cellular physiology, based on ordinary differential equations (ODEs) (Atlas et al., 2008; Browning et al., 2004; Castellanos et al., 2004, 2007; Domach et al., 1984; Tomita et al., 1999), were limited by the difficulty in obtaining the necessary model parameters.”
Genome Transplantation in Bacteria: Changing One Species to Another
Lartigue, C., Glass, J. I., Alperovich, N., Pieper, R., Parmar, P. P., Hutchison, C. A., Smith, H. O., & Venter, J. C. (2007) · Science · doi:10.1126/science.1144622 · Cited by 485 · see citing works
Self-citationProteome organization in a genome-reduced bacterium
Kühner, S., van Noort, V., Betts, M. J., Leo-Macias, A., Batisse, C., Rode, M., Yamada, T., Maier, T., Bader, S., Beltran-Alvarez, P., et al. (2009) · Science · doi:10.1126/science.1176343 · Cited by 471 · cited 1× · see citing works
“…with relatively small genomes that includes several pathogens, have recently been the subject of an exhaustive experimental effort by a European consortium to determine the transcriptome (Gu¨ell et al., 2009), proteome (Ku¨hner et al., 2009), and metabolome (Yus et al., 2009) of these organisms.”
BackgroundRegulation of Gene Expression in Flux Balance Models of Metabolism
Covert, M. W., Schilling, C. H., & Palsson, B. Ø. (2001) · Journal of Theoretical Biology · doi:10.1006/jtbi.2001.2405 · Cited by 431 · cited 2× · see citing works
“Although we and others had previously developed methods to integrate ODEs with Boolean, probabilistic, and constraint-based submodels (Covert et al., 2001, 2004, 2008; Chandrasekaran and Price, 2010), the current effort involved so many different cellular functions and mathematical representations…”
Self-citationTranscriptome complexity in a genome-reduced bacterium
Güell, M., van Noort, V., Yus, E., Chen, W.-H., Leigh-Bell, J., Michalodimitrakis, K., Yamada, T., Arumugam, M., Doerks, T., Kühner, S., et al. (2009) · Science · doi:10.1126/science.1176951 · Cited by 418 · cited 1× · see citing works
“…with relatively small genomes that includes several pathogens, have recently been the subject of an exhaustive experimental effort by a European consortium to determine the transcriptome (Gu¨ell et al., 2009), proteome (Ku¨hner et al., 2009), and metabolome (Yus et al., 2009) of these organisms.”
BackgroundChandrasekaran, S., & Price, N. D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1005139107 · Cited by 399 · cited 2× · see citing works
“…had previously developed methods to integrate ODEs with Boolean, probabilistic, and constraint-based submodels (Covert et al., 2001, 2004, 2008; Chandrasekaran and Price, 2010), the current effort involved so many different cellular functions and mathematical representations that a more…”
General properties of transcriptional time series in Escherichia coli
So, L., Ghosh, A., Zong, C. C., Sepúlveda, L. A., Segev, R., & Golding, I. (2011) · Nature Genetics · doi:10.1038/ng.821 · Cited by 384 · see citing works
Creating Bacterial Strains from Genomes That Have Been Cloned and Engineered in Yeast
Lartigue, C., Vashee, S., Algire, M. A., Chuang, R.-Y., Benders, G. A., Ma, L., Noskov, V. N., Denisova, E. A., Gibson, D. G., Assad-Garcia, N., et al. (2009) · Science · doi:10.1126/science.1173759 · Cited by 332 · see citing works
Self-citationImpact of genome reduction on bacterial metabolism and its regulation
Yus, E., Maier, T., Michalodimitrakis, K., van Noort, V., Yamada, T., Chen, W.-H., Wodke, J. A. H., Güell, M., Martínez, S., Bourgeois, R., et al. (2009) · Science · doi:10.1126/science.1177263 · Cited by 327 · cited 4× · see citing works
“We found that the model calculations were consistent with the observed doubling time (Figures 2A and 2B), cellular chemical composition (Figure 2C), replication of major cell mass fractions (Figure 2D), and gene expression ( R 2 = 0.68; Figure S1A).”
InfluentialIntegrating metabolic, transcriptional regulatory and signal transduction models inEscherichia coli
Covert, M. W., Xiao, N., Chen, T. J., & Karr, J. R. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn352 · Cited by 316 · cited 2× · see citing works
“Although we and others had previously developed methods to integrate ODEs with Boolean, probabilistic, and constraint-based submodels (Covert et al., 2001, 2004, 2008; Chandrasekaran and Price, 2010), the current effort involved so many different cellular functions and mathematical representations…”
Self-citationFrom in vivo to in silico biology and back
Di Ventura, B., Lemerle, C., Michalodimitrakis, K., & Serrano, L. (2006) · Nature · doi:10.1038/nature05127 · Cited by 290 · cited 2× · see citing works
“Computer models that can account for the integrated function of every gene in a cell have the potential to revolutionize biology and medicine, as they increasingly contribute to how we understand, discover, and design biological systems (Di Ventura et al., 2006).”
InfluentialSingle-molecule imaging of RNA polymerase-DNA interactions in real time
Harada, Y., Funatsu, T., Murakami, K., Nonoyama, Y., Ishihama, A., & Yanagida, T. (1999) · Biophysical Journal · doi:10.1016/s0006-3495(99)77237-1 · Cited by 278 · cited 2× · see citing works
“On average, this results in expression of 90% of genes within the first 143 min (Figure 3C), with transcription lagging RNA polymerase exploration due to the significant contribution of nonspe-cific RNA polymerase-DNA interactions to RNA polymerase diffusion (Harada et al., 1999).”
Brenner, S. (2010) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2009.0221 · Cited by 244 · cited 1× · see citing works
“These results support the assertion that large-scale modeling can be used to guide biological discovery (Kitano, 2002; Brenner, 2010).”
Beginnings of cellular life: Metabolism recapitulates biogenesis
Mason, S. (1993) · Trends in Biochemical Sciences · doi:10.1016/0968-0004(93)90195-s · Cited by 234 · cited 1× · see citing works
“However, several other parameters were carefully reconciled; for example, the experimentally measured DNA content per cell (Morowitz et al., 1962; Morowitz, 1992) represents less than one-third of the calculated mass of the myco-plasma chromosome.”
Sundararaj, S. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh108 · Cited by 211 · cited 4× · see citing works
“(F) Ratios of observed (Sundararaj et al., 2004; Bennett et al., 2009) and average predicted concentrations of 39 metabolites.”
Influential(1992) · Medical Entomology and Zoology · Cited by 204 · see citing works
Thiele, I., Jamshidi, N., Fleming, R. M. T., & Palsson, B. Ø. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000312 · Cited by 182 · cited 2× · see citing works
“Subsequently, alternative approaches were developed that require fewer parameters, including Boolean network modeling (Davidson et al., 2002) and constraint-based modeling (Orth et al., 2010; Thiele et al., 2009).”
BackgroundMethodThe pyruvate dehydrogenase multi-enzyme complex from Gram-negative bacteria
de Kok, A., Hengeveld, A. F., Martin, A., & Westphal, A. H. (1998) · Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology · doi:10.1016/s0167-4838(98)00079-x · Cited by 174 · cited 1× · see citing works
“The lpdA gene product is part of the pyruvate dehydrogenase complex, which catalyzes the transfer of electrons to nicotinamide adenine dinucleotide (NAD) as a subset of the overall pyruvate dehydrogenase chemical reaction (de Kok et al., 1998).”
BackgroundComputer model for glucose‐limited growth of a single cell of Escherichia coli B/r‐A
Domach, M. M., Leung, S. K., Cahn, R. E., et al. (1984) · Biotechnology and Bioengineering · doi:10.1002/bit.260260925 · Cited by 157 · see citing works
Direct restart of a replication fork stalled by a head-on RNA polymerase
Pomerantz, R. T., & O’Donnell, M. (2010) · Science · doi:10.1126/science.1179595 · Cited by 150 · cited 1× · see citing works
“Previous researchers have studied the collisions of pairs of specific proteins (Pomerantz and O’Donnell, 2010), but experimentally determining the collisions among all pairs of DNA-binding proteins at the genomic scale at single-cell resolution is currently infeasible.”
A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189
Suthers, P. F., Dasika, M. S., Kumar, V. S., Denisov, G., Glass, J. I., & Maranas, C. D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000285 · Cited by 144 · cited 2× · see citing works
“For example, metabolism was modeled using flux-balance analysis (Suthers et al., 2009), whereas (A) Diagram schematically depicts the 28 sub-models as colored words—grouped by category as metabolic (orange), RNA (green), protein (blue), and DNA (red)—in the context of a single M. genitalium cell…”
Self-citationProtein occupancy landscape of a bacterial genome
Vora, T., Hottes, A. K., & Tavazoie, S. (2009) · Molecular Cell · doi:10.1016/j.molcel.2009.06.035 · Cited by 126 · cited 3× · see citing works
“We found that the model calculations were consistent with the observed doubling time (Figures 2A and 2B), cellular chemical composition (Figure 2C), replication of major cell mass fractions (Figure 2D), and gene expression ( R 2 = 0.68; Figure S1A).”
InfluentialIsolation and properties of an H2O-forming NADH oxidase from Streptococcus faecalis
SCHMIDT, H.-L., STOCKLEIN, W., DANZER, J., KIRCH, P., & LIMBACH, B. (1986) · European Journal of Biochemistry · doi:10.1111/j.1432-1033.1986.tb09560.x · Cited by 102 · cited 4× · see citing works
“The lpdA gene product is part of the pyruvate dehydrogenase complex, which catalyzes the transfer of electrons to nicotinamide adenine dinucleotide (NAD) as a subset of the overall pyruvate dehydrogenase chemical reaction (de Kok et al., 1998).”
InfluentialRepair and Transcription: Collision or collusion?
Hanawalt, P. C., Donahue, B. A., & Sweder, K. S. (1994) · Current Biology · doi:10.1016/s0960-9822(00)00112-3 · Cited by 98 · see citing works
Stability, Flexibility, and Dynamic Interactions of Colliding RNA Polymerase II Elongation Complexes
Saeki, H., & Svejstrup, J. Q. (2009) · Molecular Cell · doi:10.1016/j.molcel.2009.06.009 · Cited by 92 · see citing works
Transcription profiles of the bacterium Mycoplasma pneumoniae grown at different temperatures
Weiner, J., III (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg841 · Cited by 89 · see citing works
Pollack, J. D., Myers, M. A., Dandekar, T., & Herrmann, R. (2002) · OMICS: A Journal of Integrative Biology · doi:10.1089/15362310260256909 · Cited by 66 · cited 2× · see citing works
“genitalium genes are multifunctional (Pollack et al., 2002; Cordwell et al., 1997), we searched the genome for candidates encoding an alternative NAD electron transfer pathway.”
The chemical composition and submicroscopic morphology of Mycoplasma gallisepticum, avian PPLO 5969
Morowitz, H. J., Tourtellotte, M. E., Guild, W. R., Castro, E., Woese, C., & Cleverdon, R. C. (1962) · Journal of Molecular Biology · doi:10.1016/s0022-2836(62)80041-2 · Cited by 64 · cited 1× · see citing works
“However, several other parameters were carefully reconciled; for example, the experimentally measured DNA content per cell (Morowitz et al., 1962; Morowitz, 1992) represents less than one-third of the calculated mass of the myco-plasma chromosome.”
A modular minimal cell model: Purine and pyrimidine transport and metabolism
Castellanos, M., Wilson, D. B., & Shuler, M. L. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400962101 · Cited by 56 · cited 1× · see citing works
“The first approaches to modeling cellular physiology, based on ordinary differential equations (ODEs) (Atlas et al., 2008; Browning et al., 2004; Castellanos et al., 2004, 2007; Domach et al., 1984; Tomita et al., 1999), were limited by the difficulty in obtaining the necessary model parameters.”
Spatial Distribution and Diffusive Motion of RNA Polymerase in Live Escherichia coli
Bratton, B. P., Mooney, R. A., & Weisshaar, J. C. (2011) · Journal of Bacteriology · doi:10.1128/jb.00198-11 · Cited by 50 · cited 2× · see citing works
“…on the genomic distribution of DNA-binding proteins (Vora et al., 2009) or on the detailed diffusion dynamics of specific DNA-binding proteins (Bratton et al., 2011), the whole-cell model can predict both the instantaneous protein chromosomal occupancy as well as the temporal dynamics and…”
Malate/lactate dehydrogenase in mollicutes: evidence for a multienzyme protein
Cordwell, S. J., Basseal, D. J., Pollack, J. D., & Humphery-Smith, I. (1997) · Gene · doi:10.1016/s0378-1119(97)00063-2 · Cited by 42 · see citing works
Atlas, J. C., Nikolaev, E. V., Browning, S. T., & Shuler, M. L. (2008) · IET Systems Biology · doi:10.1049/iet-syb:20070079 · Cited by 31 · cited 2× · see citing works
“The first approaches to modeling cellular physiology, based on ordinary differential equations (ODEs) (Atlas et al., 2008; Browning et al., 2004; Castellanos et al., 2004, 2007; Domach et al., 1984; Tomita et al., 1999), were limited by the difficulty in obtaining the necessary model parameters.”
Browning, S. T., Castellanos, M., & Shuler, M. L. (2004) · Biotechnology and Bioengineering · doi:10.1002/bit.20223 · Cited by 26 · cited 4× · see citing works
“variability in the initiation and replication phases; and (2) why is the overall cell-cycle duration less varied than either of these phases? With respect to the first question, replication initiation occurs as DnaA protein monomers bind or unbind stochastically and cooperatively to form a multimeric complex at the re…”
InfluentialA genomically/chemically complete module for synthesis of lipid membrane in a minimal cell
Castellanos, M., Kushiro, K., Lai, S. K., & Shuler, M. L. (2006) · Biotechnology and Bioengineering · doi:10.1002/bit.21251 · Cited by 16 · cited 1× · see citing works
“The first approaches to modeling cellular physiology, based on ordinary differential equations (ODEs) (Atlas et al., 2008; Browning et al., 2004; Castellanos et al., 2004, 2007; Domach et al., 1984; Tomita et al., 1999), were limited by the difficulty in obtaining the necessary model parameters.”
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Bassett, D. S., & Sporns, O. (2017) · Nature Neuroscience · doi:10.1038/nn.4502 · Cited by 2,398 · see citing works
Design and synthesis of a minimal bacterial genome
Hutchison, C. A., Chuang, R.-Y., Noskov, V. N., Assad-Garcia, N., Deerinck, T. J., Ellisman, M. H., Gill, J., Kannan, K., Karas, B. J., Ma, L., et al. (2016) · Science · doi:10.1126/science.aad6253 · Cited by 1,500 · see citing works
Self-citationNext-Generation Machine Learning for Biological Networks
Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018) · Cell · doi:10.1016/j.cell.2018.05.015 · Cited by 974 · see citing works
The systemic immune response to trauma: an overview of pathophysiology and treatment
Lord, J. M., Midwinter, M. J., Chen, Y., Belli, A., Brohi, K., Kovacs, E. J., Koenderman, L., Kubes, P., & Lilford, R. J. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60687-5 · Cited by 856 · see citing works
Constraint-based models predict metabolic and associated cellular functions
Bordbar, A., Monk, J. M., King, Z. A., & Palsson, B. Ø. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3643 · Cited by 818 · see citing works
“Trends in current literature 89,91,96 indicate that other cellular processes may be modelled using alternative frameworks that are better suited for a particular biological phenomena.”
BackgroundAdaptive laboratory evolution—Principles and applications for biotechnology
Dragosits, M., & Mattanovich, D. (2013) · Microbial Cell Factories · doi:10.1186/1475-2859-12-64 · Cited by 827 · see citing works
“Full-scale models of microorganisms are available [185,186] and we can simulate microbial growth.”
Using Genome-scale Models to Predict Biological Capabilities
O’Brien, E. J., Monk, J. M., & Palsson, B. Ø. (2015) · Cell · doi:10.1016/j.cell.2015.05.019 · Cited by 709 · see citing works
“In addition to DNA replication and cell division (Karr et al., 2012), much of the remaining proteome mass involves cellular stress responses (e.g., pH, osmolarity, osmotic); like with temperature, GEM-PRO will aid in modeling these cellular stresses.”
Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications
Wu, G., Yan, Q., Jones, J. A., Tang, Y., Fong, S. S., & Koffas, M. A. G. (2016) · Trends in biotechnology · doi:10.1016/j.tibtech.2016.02.010 · Cited by 669 · see citing works
“In addition to GSMs, a whole-cell model ofMycoplasma genitalium has been generated using 28 modularized subsystems [65].”
MethodCoordination of microbial metabolism
Chubukov, V., Gerosa, L., Kochanowski, K., & Sauer, U. (2014) · Nature Reviews Microbiology · doi:10.1038/nrmicro3238 · Cited by 606 · see citing works
Systems strategies for developing industrial microbial strains
Lee, S. Y., & Kim, H. U. (2015) · Nature Biotechnology · doi:10.1038/nbt.3365 · Cited by 589 · see citing works
Bernal, M. A., Bordage, M., Brown, J. M. C., Davídková, M., Delage, E., Bitar, Z. E., Enger, S. A., Francis, Z., Guatelli, S., Ivanchenko, V., et al. (2015) · Physica Medica · doi:10.1016/j.ejmp.2015.10.087 · Cited by 538 · see citing works
“An emergent modeling approach, referred to as “whole cell modeling”, has been concretized with the first modeling of the human parasite “Mycoplasma genitalium” [78].”
MethodSánchez, B. J., Zhang, C., Nilsson, A., Lahtvee, P., Kerkhoven, E. J., & Nielsen, J. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167411 · Cited by 466 · see citing works
“Alternative approaches such as resource balance analysis (Goelzer et al, 2015), self-replicating models (Molenaar et al, 2009; Berkhout et al, 2013), and whole-cell models (Karr et al, 2012) have also been developed to account for protein limitations.”
MethodJewett, A. I., Stelter, D., Lambert, J., Saladi, S. M., Roscioni, O. M., Ricci, M., Autin, L., Maritan, M., Bashusqeh, S. M., Keyes, T., et al. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.166841 · Cited by 531 · see citing works
“In collaboration with the Covert laboratory, we are generating models of Mycoplasma genitalium cells based on discrete time points from WholeCell simulations [37] which can be simulated in LAMMPS.”
MethodEmergence of robust growth laws from optimal regulation of ribosome synthesis
Scott, M., Klumpp, S., Mateescu, E. M., & Hwa, T. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145379 · Cited by 443 · see citing works
“…have been elucidated over the past 50 years (White et al, 2011), and it is clear that at the molecular level, synthesis, degradation and regulation are implemented via complex interconnected networks, governed by kinetics that depend nonlinearly on reactant concentrations (Karr et al, 2012).”
BackgroundCytosystems dynamics in self-organization of tissue architecture
Sasai, Y. (2013) · Nature · doi:10.1038/nature11859 · Cited by 475 · see citing works
Ko, Y., Kim, J. W., Lee, J. A., Han, T., Kim, G. B., Park, J. E., & Lee, S. Y. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00155d · Cited by 475 · see citing works
Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction
O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., & Palsson, B. Ø. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.52 · Cited by 471 · see citing works
“On the heels of these developments, a whole-cell model (WCM) of the human pathogen Mycoplasma genitalium appeared (Karr et al, 2012).”
Using deep learning to model the hierarchical structure and function of a cell
Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T. (2018) · Nature Methods · doi:10.1038/nmeth.4627 · Cited by 435 · see citing works
“In contrast to other mechanistic models that have attempted large-scale genotype–phenotype prediction 13,36 , the framework of hierarchical neural networks is very general and expressive, such that a large class of biological structures and functions can be represented.”
ResultMapping transcriptomic vector fields of single cells
Qiu, X., Zhang, Y., Martin-Rufino, J. D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A. N., Hein, M. Y., Min, K. H., Wang, L., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.045 · Cited by 436 · see citing works
“Although efforts have been made to perform whole-cell simulations of bacteria (Karr et al., 2012; Macklin et al., 2020), it remains a grand challenge to reconstruct the vector field (a vector-valued function that assigns a vector, such as the transcriptomic velocity vector, to any point in the state space, such as the…”
‘Big data’, Hadoop and cloud computing in genomics
O’Driscoll, A., Daugelaite, J., & Sleator, R. D. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.07.001 · Cited by 432 · see citing works
“An example of such an approach is the recent publication of a whole-cell computational model of the uropathogenic bacterium Mycoplasma genitalium [74].”
BackgroundQuantitative and logic modelling of molecular and gene networks
Novère, N. G. L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3885 · Cited by 422 · see citing works
“This model used a mix of ODEs, stochastic processes and flux balance analysis to simulate 28 modules representing gene networks, signalling and metabolism (4) .”
BackgroundMethodMolecular dynamics simulations of large macromolecular complexes
Perilla, J. R., Goh, B. C., Cassidy, C. K., Liu, B., Bernardi, R. C., Rudack, T., Yu, H., Wu, Z., & Schulten, K. J. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.03.007 · Cited by 423 · see citing works
Machado, D., & Herrgård, M. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003580 · Cited by 403 · see citing works
“These types of data can also be used to parameterize next generation of whole-cell models that explicitly represent proteins and transcripts in addition to metabolic fluxes [58,59] Finally, we would like to acknowledge the authors who published their source code with the respective articles.”
BackgroundMechanistic links between cellular trade-offs, gene expression, and growth
Weiße, A. Y., Oyarzún, D. A., Vincent Danos, A., & Swain, P. S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1416533112 · Cited by 390 · see citing works
Digital twin in healthcare: Recent updates and challenges
Sun, T., He, X., & Li, Z. (2023) · Digital Health · doi:10.1177/20552076221149651 · Cited by 421 · see citing works
“In a whole-cell project, specific types of cell and organism were modelled to better understand the molecular basis of diseases.(27) Therefore, faster solving surrogates of these models may be developed in the form of machine learning models to solve problems in real-time.”
BackgroundHow to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393 · see citing works
“8 20–22 Despite their widespread use in modeling biological systems, approaches to date fall short of capturing many aspects of the operations of both bacterial and more complex systems, such as human cells.”
In vitro neurons learn and exhibit sentience when embodied in a simulated game-world
Kagan, B. J., Kitchen, A. C., Tran, N. T., Habibollahi, F., Khajehnejad, M., Parker, B. J., Bhat, A., Rollo, B., Razi, A., & Friston, K. J. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.001 · Cited by 400 · see citing works
“This is something that is long sought after and extends beyond purely in silico models or predictions of molecular pathways alone (Karr et al., 2012; Whittington et al., 2020; Yu et al., 2018).”
Penas, D. R., González, P., Egea, J. A., Doallo, R., & Banga, J. R. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1452-4 · Cited by 385 · see citing works
“Recent efforts have been focused on scaling-up the development of dynamic (kinetic) models [19–25], with the ultimate goal of obtaining whole-cell models [26, 27].”
BackgroundA brief history of bioinformatics
Gauthier, J., Vincent, A. T., Charette, S. J., & Derôme, N. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby063 · Cited by 388 · see citing works
Metabolic engineering in methanotrophic bacteria.
Kalyuzhnaya, M., Puri, A. W., & Lidstrom, M. (2015) · Metabolic Engineering · doi:10.1016/j.ymben.2015.03.010 · Cited by 307 · see citing works
Genome‐scale engineering for systems and synthetic biology
Esvelt, K. M., & Wang, H. H. (2013) · Molecular Systems Biology · doi:10.1038/msb.2012.66 · Cited by 319 · see citing works
“To surmount this problem, Karr et al (2012) partitioned Mycoplasma into 28 distinct modules, modeled each using the most appropriate representation, and integrated the results to describe the entire cell.”
BackgroundMethodInfluentialA White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action
Yang, J. H., Wright, S. N., Hamblin, M., McCloskey, D., Alcantar, M. A., Schrübbers, L. C., Lopatkin, A. J., Satish, S., Nili, A., Palsson, B. Ø., et al. (2019) · Cell · doi:10.1016/j.cell.2019.04.016 · Cited by 317 · see citing works
“, 2018), curated knowledge bases (Karr et al., 2012; Keseler et al., 2017; Monk et al., 2017), and screening innovations (French et al.”
Engineering microbial consortia by division of labor
Roell, G. W., Zha, J., Carr, R. R., Koffas, M. A. G., Fong, S. S., & Tang, Y. (2019) · Microbial Cell Factories · doi:10.1186/s12934-019-1083-3 · Cited by 318 · see citing works
Machine learning for metabolic engineering: A review
Lawson, C. E., Martí, J. M., Radivojević, T., Jonnalagadda, S. V. R., Gentz, R., Hillson, N. J., Peisert, S., Kim, J., Simmons, B. A., Petzold, C. J., et al. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.10.005 · Cited by 321 · see citing works
Yu, I., Mori, T., Ando, T., Harada, R., Jung, J., Sugita, Y., & Feig, M. (2016) · eLife · doi:10.7554/elife.19274 · Cited by 238 · see citing works
“…but the full physicochemical nature of cellular environments with attractive and repulsive interactions, solvation effects and co-solvents apparently leads to more varied effects (Monteith et al., 2015; Harada et al., 2013, 2012; Feig and Sugita, 2012; Kim and Mittal, 2013; Tanizaki et al., 2008).”
BackgroundDavidi, D., Noor, E., Liebermeister, W., Bar-Even, A., Flamholz, A., Tummler, K., Barenholz, U., Goldenfeld, M., Shlomi, T., & Milo, R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1514240113 · Cited by 266 · see citing works
Reconstructing organisms in silico: genome-scale models and their emerging applications
Fang, X., Lloyd, C. J., & Palsson, B. O. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-00440-4 · Cited by 292 · see citing works
“Whole-cell models of the human pathogen Mycoplasma genitalium 115 , Saccharomyces cerevisiae 116 and E. coli 117 have been developed.”
BackgroundMethodYeast Systems Biology: Model Organism and Cell Factory
Nielsen, J. (2019) · Biotechnology Journal · doi:10.1002/biot.201800421 · Cited by 283 · see citing works
Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets
Leung, M. K. K., Delong, A., Alipanahi, B., & Frey, B. J. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2494198 · Cited by 276 · see citing works
“[176], [177] is widely regarded as the first whole-cell computational model with any reasonable degree of predictive accuracy.”
BackgroundStructural Identifiability of Dynamic Systems Biology Models
Villaverde, A. F., Barreiro, A., & Papachristodoulou, A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005153 · Cited by 278 · see citing works
“This is a major challenge in systems biology, as the models constructed are increasingly complex, large [11], and more difficult to identify [8].”
BackgroundLive-cell mass profiling: an emerging approach in quantitative biophysics
Zangle, T. A., & Teitell, M. A. (2014) · Nature Methods · doi:10.1038/nmeth.3175 · Cited by 260 · see citing works
“Finally, recent work on whole-cell modeling used cell dry mass as a global readout over the cell cycle 72 , suggesting that cell mass measurements may play a role in validating computational modeling efforts.”
Overloaded and stressed: whole-cell considerations for bacterial synthetic biology
Borkowski, O., Ceroni, F., Stan, G.-B. V., & Ellis, T. (2016) · Current Opinion in Microbiology · doi:10.1016/j.mib.2016.07.009 · Cited by 251 · see citing works
“This work is now leading to the development of new models able to more 158 comprehensively capture global physiological changes and parameters [22], different metabolic 159 needs [37] and the impact of foreign DNA constructs on the gene expression machinery [38].”
BackgroundMicrobial metabolites in the marine carbon cycle
Moran, M. A., Kujawinski, E. B., Schroer, W. F., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01090-3 · Cited by 254 · see citing works
Self-citationNielsen, J. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044757 · Cited by 252 · see citing works
“The most notable of these attempts are (a) the so-called Cornell model for E. coli, developed by Mike Schuler (18) in the 1970s, which described cellular functions by 14 (later expanded to 20) key variables; (b) the ECELL model, also for E. coli, developed by Masaru Tomita (19) in the 1990s, which allows the user to s…”
BackgroundA machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
Costello, Z., & Martín, H. G. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0054-3 · Cited by 238 · see citing works
Recent advances in systems metabolic engineering tools and strategies
Chae, T. U., Choi, S. Y., Kim, J. W., Ko, Y., & Lee, S. Y. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.06.007 · Cited by 246 · see citing works
Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe
Heinken, A., Khan, M. T., Paglia, G., Rodionov, D. A., Harmsen, H. J. M., & Thiele, I. (2014) · Journal of Bacteriology · doi:10.1128/jb.01780-14 · Cited by 236 · see citing works
Integration of clinical data with a genome‐scale metabolic model of the human adipocyte
Mardinoğlu, A., Ågren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., Walley, A. J., Froguel, P., Carlsson, L. M. S., Uhlén, M., et al. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.5 · Cited by 239 · see citing works
“…of novel therapeutic targets for obesity through combination of our model with high throughput patient data. iAdipocytes1809 can also be used as a scaffold for a comprehensive whole adipocyte model that accounts for all of the annotated gene functions identified in adipocytes (Karr et al, 2012).”
BackgroundReverse engineering and identification in systems biology: strategies, perspectives and challenges
Villaverde, A. F., & Banga, J. R. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0505 · Cited by 229 · see citing works
Synthetic and systems biology for microbial production of commodity chemicals
Chubukov, V., Mukhopadhyay, A., Petzold, C. J., Keasling, J. D., & Martín, H. G. (2016) · npj Systems Biology and Applications · doi:10.1038/npjsba.2016.9 · Cited by 235 · see citing works
Agent-Based Modeling in Economics and Finance: Past, Present, and Future
Axtell, R. L., & Farmer, J. D. (2025) · Journal of Economic Literature · doi:10.1257/jel.20221319 · Cited by 244 · see citing works
Genomic diversity, lifestyles and evolutionary origins of DPANN archaea
Dombrowski, N., Lee, J.-H., Williams, T. A., Offre, P., & Spang, A. (2019) · FEMS Microbiology Letters · doi:10.1093/femsle/fnz008 · Cited by 230 · see citing works
Synthetic Ecology of Microbes: Mathematical Models and Applications
Zomorrodi, A. R., & Segrè, D. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.10.019 · Cited by 212 · see citing works
“For instance, recent advances in whole-cell modeling [228] and integrated metabolic-expression (ME) models [229] have enabled capturing several ecologically relevant biological processes that are not addressed by stoichiometric-based metabolic models such as gene expression, regulation and signal transduction.”
BackgroundPersonalized whole‐body models integrate metabolism, physiology, and the gut microbiome
Thiele, I., Sahoo, S., Heinken, A., Hertel, J., Heirendt, L., Aurich, M. K., & Fleming, R. M. T. (2020) · Molecular Systems Biology · doi:10.15252/msb.20198982 · Cited by 215 · see citing works
“Using metabolic models as a framework for integration of other types of models has been already demonstrated, most notably in a model of Mycoplasma genitalium (Karr et al, 2012).”
MethodGenome modeling and design across all domains of life with Evo 2Preprint
Brixi, G., Durrant, M. G., Ku, J., Poli, M., Brockman, G., Chang, D., Gonzalez, G. A., King, S. B., Li, D. B., Merchant, A. T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.18.638918 · Cited by 218 · see citing works
Challenges, tasks, and opportunities in modeling agent-based complex systems
An, L., Grimm, V., Sullivan, A., Turner, B. L., Malleson, N., Heppenstall, A. J., Vincenot, C. E., Robinson, D. T., Ye, X., Liu, J., et al. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109685 · Cited by 214 · see citing works
“In biology, cell simulation has included thousands of genes and millions of molecules (Karr et al., 2012).”
BackgroundEssential metabolism for a minimal cell
Breuer, M., Earnest, E. E., Merryman, C., Wise, K. S., Sun, L., Lynott, M. R., Hutchison, C. A., Smith, H. O., Lapek, J. D., Gonzalez, D. J., et al. (2019) · eLife · doi:10.7554/elife.36842 · Cited by 190 · see citing works
Self-citationThe second decade of synthetic biology: 2010–2020
Meng, F., & Ellis, T. (2020) · Nature Communications · doi:10.1038/s41467-020-19092-2 · Cited by 199 · see citing works
High-Throughput Methods for Combinatorial Drug Discovery
Sun, X., Vilar, S., & Tatonetti, N. P. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3006667 · Cited by 200 · see citing works
Chen, X., Gao, C., Guo, L., Hu, G., Luo, Q., Liu, J., Nielsen, J., Chen, J., & Liu, L. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00804 · Cited by 193 · see citing works
Digital Quantification of Proteins and mRNA in Single Mammalian Cells
Albayrak, C., Jordi, C. A., Zechner, C., Lin, J., Bichsel, C. A., Khammash, M., & Tay, S. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.02.030 · Cited by 176 · see citing works
Ernst, M., Silva, D. B., Silva, R., Vêncio, R. Z. N., & Lopes, N. P. (2014) · Natural Product Reports · doi:10.1039/c3np70086k · Cited by 187 · see citing works
Modeling cancer metabolism on a genome scale
Yizhak, K., Chaneton, B., Gottlieb, E., & Ruppin, E. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145307 · Cited by 182 · see citing works
“Importantly, GSMM is a stepping stone for whole-cell modeling, and this vision, which was already firstly realized by Karr et al (2012) in bacteria, should inspire us to aim at modeling the entire cellular dynamics of different cancer cells.”
MethodCOBRAme: A computational framework for genome-scale models of metabolism and gene expression
Lloyd, C. J., Ebrahim, A., Yang, L., King, Z. A., Catoiu, E., O’Brien, E. J., Liu, J. K., & Palsson, B. O. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006302 · Cited by 167 · see citing works
“Therefore, a focus of development in the field of genome-scale models has been to increase the scope and capabilities of genome-scale models [5].”
BackgroundCancer systems biology: embracing complexity to develop better anticancer therapeutic strategies
Du, W., & Elemento, O. (2014) · Oncogene · doi:10.1038/onc.2014.291 · Cited by 182 · see citing works
Analysis of omics data with genome-scale models of metabolism
Hyduke, D. R., Lewis, N. E., & Palsson, B. Ø. (2012) · Molecular BioSystems · doi:10.1039/c2mb25453k · Cited by 181 · see citing works
From word models to executable models of signaling networks using automated assembly
Gyori, B., Bachman, J. A., Subramanian, K., Muhlich, J., Galescu, L., & Sorger, P. (2017) · bioRxiv · doi:10.15252/msb.20177651 · Cited by 174 · see citing works
“…DNA damage (Purvis et al , 2012), drug sensitivity and disease progression (Lindner et al , 2013; Fey et al , 2015), bacterial cell physiology (Karr et al , 2012), the responses of ERK kinase (Chen et al , 2009) and the NF-j B transcription factor (Hoffmann et al , 2002) to environmental…”
BackgroundRobust and efficient parameter estimation in dynamic models of biological systems
Gábor, A., & Banga, J. R. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0219-2 · Cited by 171 · see citing works
“Other recent efforts have been focused on scaling-up, i.e. on the development and exploitation of large-scale (genome-scale) kinetic models [25], and ultimately, whole-cell models [26, 27].”
BackgroundFundamental behaviors emerge from simulations of a living minimal cell
Thornburg, Z. R., Bianchi, D. M., Brier, T. A., Gilbert, B. R., Earnest, E. E., Melo, M. C. R., Safronova, N., Sáenz, J. P., Cook, A. T., Wise, K. S., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.025 · Cited by 165 · see citing works
“coli (Karr et al., 2012; Macklin et al., 2020), where the subsystems were treated in terms of ordinary differential equations, flux balance analysis, and stochastic simulations.”
Self-citationFrom cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems
Garza, D. R., & Dutilh, B. E. (2015) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-015-2004-1 · Cited by 183 · see citing works
“This model describes a single organism and reconstructs several patterns of the bacterial cell cycle that are consistent with measurements in vitro [165].”
Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism
Singharoy, A., Maffeo, C., Delgado‐Magnero, K. H., Swainsbury, D. J. K., Şener, M., Kleinekathöfer, U., Vant, J., Nguyen, J. T., Hitchcock, A., Isralewitz, B., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.021 · Cited by 175 · see citing works
“Bioinformatics and systems biology approaches integrating libraries of existing experimental data have attempted to predict phenotypes from genotypes (Karr et al., 2012).”
BackgroundPath2Models: large-scale generation of computational models from biochemical pathway maps
Büchel, F., Rodríguez, N., Swainston, N., Wrzodek, C., Czauderna, T., Keller, R., Mittag, F., Schubert, M., Glont, M., Golebiewski, M., et al. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-116 · Cited by 173 · see citing works
“Readymade models will also be accurate starting points for the development of mechanistic models of whole cell models [66] where manual reconstruction is hardly an option.”
BackgroundEvolutionary Convergence of Pathway-Specific Enzyme Expression Stoichiometry
Lalanne, J., Taggart, J., Guo, M. S., Herzel, L., Schieler, A., & Li, G. (2018) · Cell · doi:10.1016/j.cell.2018.03.007 · Cited by 166 · see citing works
“More generally, our results signal a severely underdeveloped field of pathway optimization under cellular constraints, which will require a holistic view of the cell (Karr et al., 2012; Scott et al., 2010) and precise measurements of biosynthetic activities.”
BackgroundMolecular dynamics simulation of an entire cell
Stevens, J. A., Grünewald, F., van Tilburg, P. A. M., König, M., Gilbert, B. R., Brier, T. A., Thornburg, Z. R., Luthey-Schulten, Z., & Marrink, S. J. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1106495 · Cited by 156 · see citing works
“…cell-scale structures such as cellPACK (Johnson et al., 2015; Maritan et al., 2022), genome-scale well-stirred reaction models for metabolism (Karr et al., 2012; Macklin et al., 2014; Karr et al., 2015; Breuer et al., 2019; Macklin et al., 2020), or mesoscale kinetic models that attempt to…”
BackgroundCOMBINE archive and OMEX format: one file to share all information to reproduce a modeling project
Bergmann, F. T., Adams, R., Moodie, S., Cooper, J., Glont, M., Golebiewski, M., Hucka, M., Laibe, C., Miller, A. K., Nickerson, D. P., et al. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0369-z · Cited by 173 · see citing works
Quantitative prediction of genome-wide resource allocation in bacteria
Goelzer, A., Muntel, J., Chubukov, V., Jules, M., Prestel, E., Nölker, R., Mariadassou, M., Aymerich, S., Hecker, M., Noirot, P., et al. (2015) · Metabolic Engineering · doi:10.1016/j.ymben.2015.10.003 · Cited by 140 · see citing works
Towards real-world complexity: an introduction to multiplex networks
Lee, K.-M., Min, B., & Goh, K.-I. (2015) · The European Physical Journal B · doi:10.1140/epjb/e2015-50742-1 · Cited by 192 · see citing works
Visible Machine Learning for Biomedicine
Yu, M. K., Ma, J., Fisher, J., Kreisberg, J. F., Raphael, B. J., & Ideker, T. (2018) · Cell · doi:10.1016/j.cell.2018.05.056 · Cited by 162 · see citing works
“emerging (Karr et al., 2012), an idea that fits naturally with deep (multi-layered) neural networks.”
BackgroundlibRoadRunner: a high performance SBML simulation and analysis library
Somogyi, E., Bouteiller, J. C., Glazier, J. A., König, M., Medley, K., Swat, M., & Sauro, H. M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv363 · Cited by 150 · see citing works
Elucidation of complexity and prediction of interactions in microbial communities
Zúñiga, C., Zaramela, L. S., & Zengler, K. (2017) · Microbial Biotechnology · doi:10.1111/1751-7915.12855 · Cited by 152 · see citing works
“Another hybrid model is known as whole-cell computational model (Karr et al. , 2012), which was used to predict certain stages of interactions between the human host and Mycoplasma geni-tallium .”
BackgroundMethodNext-generation genome-scale models for metabolic engineering
King, Z. A., Lloyd, C. J., Feist, A. M., & Palsson, B. O. (2015) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.12.016 · Cited by 157 · see citing works
Domenico, M. D., Allegri, L., Caldarelli, G., d’Andrea, V., Camillo, B. D., Rocha, L. M., Rozum, J. C., Sbarbati, R., & Zambelli, F. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01402-3 · Cited by 184 · see citing works
Diffusion-controlled reactions modeling in Geant4-DNA
Karamitros, M., Luan, S., Bernal, M. A., Allison, J. E., Baldacchino, G., Davídková, M., Francis, Z., Friedland, W., Ivanchenko, V., Ivanchenko, A., et al. (2014) · Journal of Computational Physics · doi:10.1016/j.jcp.2014.06.011 · Cited by 155 · see citing works
Torre, A. D. L., Metivier, A., Chu, F., Laurens, L. M. L., Beck, D. A. C., Pienkos, P. T., Lidstrom, M. E., & Kalyuzhnaya, M. (2015) · Microbial Cell Factories · doi:10.1186/s12934-015-0377-3 · Cited by 155 · see citing works
Benchmarking optimization methods for parameter estimation in large kinetic models
Villaverde, A. F., Fröhlich, F., Weindl, D., Hasenauer, J., & Banga, J. R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty736 · Cited by 154 · see citing works
“During the last decade, many efforts have been devoted to developing increasingly detailed and, therefore, larger systems biology models [29,49,51].”
BackgroundSimultaneous cross-evaluation of heterogeneous <i>E. coli</i> datasets via mechanistic simulation
Macklin, D. N., Ahn-Horst, T. A., Choi, H., Ruggero, N. A., Carrera, J., Mason, J. C., Sun, G., Agmon, E., DeFelice, M. M., Maayan, I., et al. (2020) · Science · doi:10.1126/science.aav3751 · Cited by 151 · see citing works
Self-citationcellPACK: A Virtual Mesoscope to Model and Visualize Structural Systems Biology
Johnson, G. T., Autin, L., Al-Alusi, M., Goodsell, D., Sanner, M. F., & Olson, A. (2014) · Nature Methods · doi:10.1038/nmeth.3204 · Cited by 140 · see citing works
“As described in Supplementary Figure 2 , the following levels of data structure interaction are visually separated into separate tabbed panels of the default cellPACK GUI.”
MethodInfluentialAddressing uncertainty in genome-scale metabolic model reconstruction and analysis
Bernstein, D. B., Sulheim, S., Almaas, E., & Segrè, D. (2021) · Genome biology · doi:10.1186/s13059-021-02289-z · Cited by 153 · see citing works
“Finally Finally, whole-cell modeling can be used to simultaneously model multiple processes in the cell and gain comprehensive insight into cellular physiology [210, 211].”
MethodGrand Research Challenges for Sustainable Industrial Biotechnology
Straathof, A. J. J., Wahl, A., Benjamin, K. R., Takors, R., Wierckx, N. J. P., & Noorman, H. J. (2019) · Trends in biotechnology · doi:10.1016/j.tibtech.2019.04.002 · Cited by 147 · see citing works
“Progress in modeling is observed for different levels: (1) hydrodynamics and mixing of large scale-bioreactor in combination with (currently simple) cellular models [57], (2) stoichiometric and kinetic models of metabolism partly including transcription and translation [59], (3) whole cell modeling approaches combinin…”
BackgroundMethodCheng, F., Zhao, J., & Zhao, Z. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv068 · Cited by 148 · see citing works
Graphics processing units in bioinformatics, computational biology and systems biology
Nobile, M. S., Cazzaniga, P., Tangherloni, A., & Besozzi, D. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw058 · Cited by 146 · see citing works
“biological systems—up to whole cells and organs [4, 5]—focusing either on topological properties or flux distributions of large-scale networks, or on the dynamical behavior of their molecular components (e.”
BackgroundThe y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function
Ghatak, S., King, Z. A., Sastry, A. V., & Palsson, B. Ø. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz030 · Cited by 140 · see citing works
“The contribution of any gene function to cellular phenotype can now be codified computationally for various cellular systems, including metabolism (48), cell signaling (49), gene expression (50) and replication (7).”
BackgroundGenome modelling and design across all domains of life with Evo 2
Brixi, G., Durrant, M. G., Ku, J., Naghipourfar, M., Poli, M., Sun, G., Brockman, G., Chang, D., Fanton, A., Gonzalez, G. A., et al. (2026) · Nature · doi:10.1038/s41586-026-10176-5 · Cited by 431 · see citing works
Fröhlich, F., Kessler, T., Weindl, D., Shadrin, A., Schmiester, L., Hache, H., Muradyan, A., Schütte, M., Lim, J.-H., Heinig, M., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.013 · Cited by 143 · see citing works
“, 2016), to provide a basis for a whole-cell computational model of human (cancer) cells and complement work on micro-organisms (Karr et al., 2012).”
BackgroundIntegrative phenomics reveals insight into the structure of phenotypic diversity in budding yeastPreprint
Skelly, D. A., Merrihew, G. E., Riffle, M. E., Connelly, C. F., Kerr, E. O., Johansson, M., Jaschob, D., Graczyk, B., Shulman, N., Wakefield, J., et al. (2013) · Genome Research · doi:10.1101/gr.155762.113 · Cited by 140 · see citing works
“quantitative, systems-level models of bacterial cells (Karr et al. 2012) to similar models of eukaryotes.”
BackgroundIntegration of ‘omics’ data in aging research: from biomarkers to systems biology
Zierer, J., Menni, C., Kastenmüller, G., & Spector, T. D. (2015) · Aging Cell · doi:10.1111/acel.12386 · Cited by 139 · see citing works
Kim, M., Rai, N., Zorraquino, V., & Tagkopoulos, I. (2016) · Nature Communications · doi:10.1038/ncomms13090 · Cited by 137 · see citing works
Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
Gábor, A., Villaverde, A. F., & Banga, J. R. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0428-y · Cited by 131 · see citing works
“More recently, the first steps towards comprehensive whole-cell models have been taken [11], which has great potential for applications e.g. in personalized medicine [12].”
BackgroundCrops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform
Marshall‐Colón, A., Long, S. P., Allen, D. K., Allen, G., Beard, D. A., Beneš, B., von Caemmerer, S., Christensen, A., Cox, D., Hart, J. C., et al. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.00786 · Cited by 131 · see citing works
“, 2012), and in a whole cell model of Mycoplasma genitalium (Karr et al., 2012).”
BackgroundMethodChylek, L. A., Harris, L. A., Tung, C., Faeder, J. R., Lopez, C. F., & Hlavacek, W. S. (2013) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1245 · Cited by 131 · see citing works
- Authors
- Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., & Covert, M. W.
- Affiliations
- Stanford University (US)
- J. Craig Venter Institute (US)
- Corresponding author
- Covert, Markus W.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2012-07
- Volume
- 150
- Issue
- 2
- Pages
- 389-401
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC3413483
- OpenAlex
- W2081781433
- Semantic Scholar
- 97d676e4cc348a078b766783a555de501b20191e
- Cited by
- 1,300
- References
- 47
- Citations by year
- 2026: 84 · 2025: 59 · 2024: 48 · 2023: 52 · 2022: 51 · 2021: 91 · 2020: 75 · 2019: 101 · 2018: 105 · 2017: 104 · 2016: 105 · 2015: 137 · 2014: 119 · 2013: 126 · 2012: 41
- FWCI
- 54.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 384 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP1OD006413)
- U.S. National Library of Medicine (DP1LM011510)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Mycoplasma genitalium, genotype-phenotype correlation, life cycle modeling, DNA replication initiation, gene expression regulation, kinetic parameter estimation
- MeSH terms
- Bacterial Proteins, Cell Cycle, Computer Simulation, DNA-Binding Proteins, Models, Biological, Phenotype, Mycoplasma genitalium, Molecular Sequence Annotation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-09-28.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-09-28.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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