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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- 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.”
Citations
Cites
Systems biology: a brief overview
Kitano, H. (2002) · Science · doi:10.1126/science.1069492 · Cited by 4,265 · cited 1×
“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×
“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
Self-citationThe Minimal Gene Complement of Mycoplasma genitalium
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×
“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).”
Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells
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×
“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×
“(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×
“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 Mycoplasma genitalium 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
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×
“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
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×
“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×
“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×
“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×
“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 497
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×
“…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×
“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×
“…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×
“…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 393
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
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×
“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 322 · cited 2×
“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×
“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×
“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×
“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×
“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×
“(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
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×
“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×
“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
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×
“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×
“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×
“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×
“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
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
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
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×
“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×
“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×
“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×
“…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
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×
“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×
“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×
“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.”
Flagged first, then by citations
Cited by
Sorted by citations
Bassett, D. S., & Sporns, O. (2017) · Nature Neuroscience · doi:10.1038/nn.4502 · Cited by 2,398
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
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 1,011
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 887
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 844
“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.”
Background
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
