The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index; 5 of the 28 sources its claims cite were checked for support.
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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Claims the paper makes
5 of 28 cited sources checked · 5 supported · 0 flagged
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Prediction of DNA binding protein interactions
What the sources say
- Supportedcites Vora et al., 2009 source
In Results, under “Discovery of Extended Protein Occupancy Domains (EPODs)” (page unavailable), Vora et al. report that highly expressed domains have extremely high RNA polymerase occupancy and are enriched for tRNAs. The same section links these domains to highly expressed genes including ribosomal RNAs. This supports the cited experimental correspondence; the prediction itself belongs to the citing paper. (LLM, conf=0.86)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“These results support the assertion that large-scale modeling can be used to support biological discovery (Kitano, 2002; Brenner, 2010).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
What the sources say
- Not checked — no full text of the sourcecites Kitano, 2002 source
- Supportedcites Brenner, 2010 source
The concluding passage in “Sequences and consequences” (page unavailable) proposes Cellmap as a computational model that, when complete, would allow researchers to formulate and test biological hypotheses. This supports the cited assertion that modeling can aid biological discovery, although Brenner distinguishes his proposed approach from inferring models directly from large datasets. (LLM, conf=0.88)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model training and parameter reconciliation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication rates.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > Abstract
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model training and parameter reconciliation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model training and parameter reconciliation
What the sources say
- Not checked — no full text of the sourcecites Morowitz et al., 1962 source
- Not matchedcites Morowitz, 1992
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model training and parameter reconciliation
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- Probably not a substantial claim. 7
- It may state two separate claims as one. 26
- Borderline: one of the tests for a substantial claim is unclear. 1
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites Davidson et al., 2002 source
- Supportedcites Orth et al., 2010 source
The Orth et al. citation is attached to constraint-based modeling, not Boolean network modeling. In “Flux balance analysis is based on constraints,” the paper contrasts FBA with models requiring many difficult-to-measure kinetic parameters; “From constraints to optimizing a phenotype” states that FBA requires no kinetic parameters. (LLM, conf=0.93)
- Supportedcites Thiele et al., 2009 source
The Introduction states that, unlike traditional approaches, constraint-based modeling requires few, if any, parameters. Thiele et al. is cited for constraint-based modeling in the sentence; the separate Boolean-network citation is to Davidson et al. (LLM, conf=0.93)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model validation against independent experimental data
What the sources say
- Not checked — no full text of the sourcecites Sundararaj et al., 2004 source
- Supportedcites Bennett et al., 2009 source
The Results section (“Composition of the core E. coli metabolome”) reports absolute concentrations for 103 metabolites, confirming that Bennett et al. is the cited high-throughput E. coli measurement dataset. The 70% comparison with predicted concentrations is the citing paper’s own analysis (Figure 2F), not a result attributed to Bennett et al. (LLM, conf=0.90)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our model predicts that over 30,000 collisions occur on average per cell cycle, leading to the displacement of 0.93 proteins per second.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Prediction of DNA binding protein interactions
Causal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear? Based: unclearBorderline substantial claim. It is unclear whether the paper’s conclusions depend on it. It is unclear why the paper says it holds. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The most debilitating disruptions involved metabolic genes and resulted in the inability to produce any of the major cell mass components.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Determining the molecular pathologies of single-gene disruption phenotypes
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Prediction of DNA binding protein interactions
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Prediction of DNA binding protein interactions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Identification of metabolism as an emergent cell cycle regulator
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, the predicted DNA polymerase chromosomal occupancy is significantly lower and biased toward the terC (see below for further discussion).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Prediction of DNA binding protein interactions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Prediction of DNA binding protein interactions
What the sources say
- Not checked — no full text of the sourcecites Harada et al., 1999 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The whole-cell model was based on a detailed reconstruction of M. genitalium developed from over 900 primary sources, reviews, books, and databases.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > EXPERIMENTAL PROCEDURES > Reconstruction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial 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.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Our model is based on a synthesis of over 900 publications and includes more than 1,900 experimentally observed parameters.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model training and parameter reconciliation
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- 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.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites Fraser et al., 1995 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“First, until recently not enough has been known about the individual molecules and their interactions to completely model any one organism.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The thyA gene product catalyzes dTMP production and can be complemented by the tdk gene product.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“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).”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
What the sources say
- Not checked — no full text of the sourcecites de Kok et al., 1998 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Identification of metabolism as an emergent cell cycle regulator
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Prediction of DNA binding protein interactions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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)”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
What the sources say
- Not checked — no full text of the sourcecites Guell et al., 2009 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Since previous studies have shown that many M. genitalium genes are multi-functional (Pollack et al., 2002; Cordwell et al., 1997)”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
What the sources say
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Taken together, the data suggested that disrupting the lpdA gene had a severe, but non-critical impact on cell growth.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Model-driven biological discovery
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This occurs because cells that require extra time to initiate replication also build up a large dNTP surplus, leading to faster replication.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > RESULTS > Identification of metabolism as an emergent cell cycle regulator
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- 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.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A Whole-Cell Computational Model Predicts Phenotype from Genotype > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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.”
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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
“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 548
“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.”
MethodCytosystems dynamics in self-organization of tissue architecture
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“On the heels of these developments, a whole-cell model (WCM) of the human pathogen Mycoplasma genitalium appeared (Karr et al, 2012).”
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ResultMapping transcriptomic vector fields of single cells
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“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…”
Emergence of robust growth laws from optimal regulation of ribosome synthesis
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“…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).”
Background‘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 448
“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 435
“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 431
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BackgroundMechanistic links between cellular trade-offs, gene expression, and growth
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“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
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“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
“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 388
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Metabolic engineering in methanotrophic bacteria.
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Genome‐scale engineering for systems and synthetic biology
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“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 321
“, 2018), curated knowledge bases (Karr et al., 2012; Keseler et al., 2017; Monk et al., 2017), and screening innovations (French et al.”
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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
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“Whole-cell models of the human pathogen Mycoplasma genitalium 115 , Saccharomyces cerevisiae 116 and E. coli 117 have been developed.”
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Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets
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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 279
“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
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“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 249
“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., Amin, S. A., Bates, N. R., Bertrand, E. M., Braakman, R., Brown, C. T., Covert, M. W., Doney, S. C., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01090-3 · Cited by 265
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“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 246
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Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe
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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 237
“…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
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
Agent-Based Modeling in Economics and Finance: Past, Present, and Future
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Genomic diversity, lifestyles and evolutionary origins of DPANN archaea
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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 221
“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 209
“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
Challenges, tasks, and opportunities in modeling agent-based complex systems
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“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
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High-Throughput Methods for Combinatorial Drug Discovery
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Modeling cancer metabolism on a genome scale
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“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.”
MethodCancer systems biology: embracing complexity to develop better anticancer therapeutic strategies
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Analysis of omics data with genome-scale models of metabolism
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From word models to executable models of signaling networks using automated assembly
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“…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
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“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
“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 190
“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
“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
“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 175
“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
“…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
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
COBRAme: 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
“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].”
BackgroundTowards 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 195
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
“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 152
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 159
“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
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 194
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
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 152
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
“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 E. coli 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
Self-citationcellPACK: a virtual mesoscope to model and visualize structural systems biology
Johnson, G. T., Autin, L., Al‐Alusi, M. A., Goodsell, D. S., Sanner, M. F., & Olson, A. J. (2014) · Nature Methods · doi:10.1038/nmeth.3204 · Cited by 151
“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 163
“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 152
“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 151
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 148
“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 144
“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 489
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
“, 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 146
“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 144
Kim, M., Rai, N., Zorraquino, V., & Tagkopoulos, I. (2016) · Nature Communications · doi:10.1038/ncomms13090 · Cited by 141
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
“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 137
“, 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 133
Metadata
- 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.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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