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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InfluentialBiPSim: a flexible and generic stochastic simulator for polymerization processes
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Distributed learning and prediction modelling in radiation oncology
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Benchmarking kinetic models of Escherichia coli metabolismPreprint
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“A plethora of metabolic phenotype prediction methods have been developed ranging from purely statistical machine learning approaches (1,2) to mechanistic simulators of virtual cells (3) .”
BackgroundSimulation of biochemical systems using constraint-based methods and complex networks
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The Matter/Life Nexus in Biological Cells
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COMBINE archive: One File To Share Them All.
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Thermodynamically consistent, reduced models of gene regulatory networks
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Can life be engineered? Epistemological roots and blind spots of Synthetic Biology
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Complexities in Quantitative Systems Analysis of Signaling Networks
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Taming the complexity of large models
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Stochastic Simulations of Cellular Processes: From Single Cells to Colonies
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Creating Accurate Models of Life: Merging Biology and Computer Science
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The evolution of early cellular systems viewed through the lens of biological interactions
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“…assembly (Gibson et al., 2008), transformation of a cell with a chemical genome (Gibson et al., 2010), Frontiers in Microbiology | www.frontiersin.org October 2015 | Volume 6 | Article 11441 and the development of computational cellular models derived from genomic knowledge (Karr et al., 2012).”
BackgroundInterpretation and Integration of 13C-Fluxomics Data
T, R. B. S. (2015) · Journal of Postgenomics Drug & Biomarker Development · doi:10.4172/2153-0769.1000138 · Cited by 2
The study of the dependence of the human heart rate from the frequency of controlled breathing
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The Promise and Challenge of Digital Biology
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“While a number of simulations of cells have been developed over the last two decades [9– 11], so far only one specifically driven by a digitized DNA genome from a real cell [12], Mycoplasma genitalium, has been published.”
BackgroundBioinformatics and Computational Biology in Toxicology: Gateways for Precision Medicine
Ramos, K. S., Martin, M., Ramos, I. N., & Rempała, G. A. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.99176-1 · Cited by 2
Eukaryo: An Agent-based, Interactive Simulation of a Eukaryotic Cell
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Simulation step size analysis of a whole-cell computational model of bacteria
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Bringing the parts together: Steps towards an in-silico protocell
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A review of simulation and modeling approaches in microbiology
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Computational Modeling of Multidrug-Resistant Bacteria
da Silva, F. A. B., Filho, F. M., Merigueti, T. C., Giannini, T., Brum, R. C., de Faria, L. M., do Nascimento, A. P. B., Belloze, K. T., Silva, F. P., Albano, R. M., et al. (2018) · Computational biology · doi:10.1007/978-3-319-74974-7_11 · Cited by 2
Topological and kinetic determinants of the modal matrices of dynamic models of metabolism
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“In recent years, kinetic models of metabolism have become increasingly detailed, comprehensive, and consistent with the underlying biochemistry and genetics [1–6].”
Complexity-Aware Simple Modeling
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Derivative-Free Optimization of Rate Parameters of Capsid Assembly Models from Bulk in Vitro Data
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“Systems biologists have developed ever more complex and comprehensive simulationmodels of biological systems (see [2] for a recent review), culminating in such recent landmarks as the simulation of whole cells [3].”
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Furthering genome design using models and algorithms
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Self-citationUnderstanding the Genotype-Phenotype Map: Contrasting Mathematical Models
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An Ontology to Structure Biological Data: The Contribution of Mathematical Models
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Integrative modeling of the cell
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“…the Student’s t-test and Whole-cell mathematical model Cellular phenotypes arising from individual molecules and their interactions of life cycle Karr et al. 2012 [22] Large-scale mechanistic model of Escherichia coli Protein half-lives for the specific genes Macklin et al. 2020…”
BackgroundMethodInfluentialQuestions, data and models underpinning metabolic engineering
van Rosmalen, R. P., dos Santos, V. A. P. M., & Diez, M. S. (2022) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.998048 · Cited by 2
“One of the more famous examples of multi-scale modelling is the work by Karr et al. (2012), who created a whole-cell”
BackgroundA coarse-grain model for cellular growth accounting for ribosome synthesis
d’Angelo, M., Palumbo, P. J., Busti, S., & Vanoni, M. (2022) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc51059.2022.9993154 · Cited by 2
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MethodRobust Approaches to Generating Reliable Predictive Models in Systems Biology
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Towards Design of Self‐Organizing Biomimetic Systems
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Systems Analysis for Peptide Systems Chemistry
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“Moreover, models describing both chemical and physical events have been constructed in applications ranging from chemical vapor deposition [2] to peptide catalytic networks [3], and even to whole cells [4].”
BackgroundThe Use of In Silico Genome-Scale Models for the Rational Design of Minimal Cells
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Scalable computation and analysis of elementary flux modes in metabolic networks
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Heterogeneous multi-scale framework for cancer systems models and clinical applicationsPreprint
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MethodSelf-citationMachine Learning Applications for Therapeutic Tasks with Genomics DataPreprint
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“Karr et al.120 devised the first whole-cell approach to predict cell growth fromgenotype using a set of differential equations.”
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Bio-Based Commodity Chemicals Via Genetically Engineered Microbes
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Proven Distributed Memory Parallelization of Particle Methods
Pahlke, J., & Sbalzarini, I. F. (2024) · ACM Transactions on Parallel Computing · doi:10.1145/3696189 · Cited by 1
“High-performance computing (HPC) is becoming increasingly important in research, especially in the life sciences [19, 32], where many physical experiments are not feasible due to ethical reasons or technical limitations in control and observation.”
Hernández, M., Covaleda-Cortés, G., Montesinos, L., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26031150 · Cited by 1
“Cell growth assays indicated a lack of efficacy against diverse plant microbial pathogens, suggesting the redirection of future research on its potential roles towards insect deterrence or other defensive/regulatory roles.”
Thomas, U. G. (2025) · GEN Biotechnology · doi:10.1089/genbio.2025.0007 · Cited by 1
Exponential growth conditions for a coarse-grain model of ribosome synthesis
Palumbo, P. J., Papa, F., Busti, S., et al. (2024) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc56724.2024.10886718 · Cited by 1
“…a whole-cell model, with efforts devoted to building up as many biological functions as possible as modular devices (see, e.g. the seminal paper [6] where a whole-cell model has been realized for the very simple microorganism of M. genitalium , or more recent results for the prokaryotic model…”
Cell Mapping Toolkit: an end-to-end pipeline for mapping subcellular organization
Lenkiewicz, J., Churas, C., Hu, M., Qian, G., Jain, M., Levinson, M. A., Manir, M. S. A., Qin, Y., Fong, D., Ono, K., et al. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf205 · Cited by 1
“A fundamental goal in biology is mapping protein assemblies and their spatial distribution within cells, with downstream applications including understanding disease phenotypes, revealing drug targets, and interpreting genetics (Karr et al. 2012, Johnson et al. 2023, Cesnik et al. 2024).”
Li, L. X., Aguilar, B. H., Gennari, J. H., & Qin, G. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06212-2 · Cited by 1
“Yet, WCMs are currently limited to a few organisms such as M. genitalium [14] and E. coli [15], and integrating heterogeneous data remains a major challenge [16].”
NIELSEN, J. K. (2025) · Frontiers of Chemical Science and Engineering · doi:10.1007/s11705-025-2597-3 · Cited by 1
Discreteness, computation, and rules of life for self-organization dynamics beyond equilibrium
Youk, H. (2025) · Newton · doi:10.1016/j.newton.2025.100358 · Cited by 1
“This framework has achieved notable successes: it explained E. coli chemotaxis 36 , cAMP signaling in Dictyostelium discoideum 37 , and biofilm dynamics 38 , and by extension inspired Whole-Cell Models (WCMs) that simulate every molecule in a bacterium or synthetic minimal cell 39,40 .”
A Brief Introduction to the Convergence of Artificial Intelligence and Biotechnology
Marcellin, M. C., Pescaroli, G., Schmidt, M., Trump, B. D., Tubbs, T. B., Villegas-Torres, M. F., Lambert, J. H., Haggenmiller, C., Blume, Y. B., Kamrud, A. J., et al. (2026) · NATO science for peace and security series. A, Chemistry and biology · doi:10.1007/978-3-032-05246-9_2 · Cited by 2
Molecular systems, human noncoding sequence variants, and blood pressure
Qiu, Q., & Liang, M. (2026) · Physiological Reviews · doi:10.1152/physrev.00029.2025 · Cited by 3
Asche, S., Bautista, C., Blanco, C., Boulesteix, D., Champagne-Ruel, A., Mathis, C., Markovitch, O., Peng, Z., Dass, A. V., Adams, A., et al. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103211 · Cited by 2
(2026) · Nature Biotechnology · doi:10.1038/s41587-026-03110-7 · Cited by 1
Li, S., Wang, Y., Shu, Z., et al. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014160 · Cited by 1
Nutrition as a systems regulator of brain aging trajectories
Müller, L., Benedetto, S. D., & Müller, V. (2026) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2026.1825800 · Cited by 1
“…cellular phenotypes, and neural circuit dynamics may reveal emergent properties that cannot be inferred from reductionist analyses of isolated pathways (Aminian-Dehkordi et al., 2025; Bauer and Thiele, 2018; De Domenico, 2018; Di Benedetto et al., 2019; Karr et al., 2012; Müller et al., 2025a,d).”
Yang, J. H., & Bashor, C. J. (2026) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2026.115883 · Cited by 3
Virtual cell: Current perspectives and future prospects
Li, J., Zhang, H., Zhang, W., Li, N., Zhao, F., & Shen, G. (2026) · Journal of International Medical Research · doi:10.1177/03000605261425080 · Cited by 1
“10,68”
Artificial intelligence derived virtual oncology
Lyu, C., Yao, D., Yin, F., Liu, Z., Liu, B., Ma, H., & Sun, T. (2026) · The Innovation Oncology · doi:10.59717/j.xinn-oncol.2026.100028 · Cited by 1
Hu, C., Zhang, C., Lu, X., Zhang, X., Pan, S., Xu, H., Lok, S. C., Zhang, M., & Wong, I. N. (2026) · Chinese Medical Journal BioMed · doi:10.65457/cmjbm-2026-0006 · Cited by 1
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 ›
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