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Abstract

Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells.

Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language.

Results: Our analysis revealed several challenges to representing WC models using the current standards.

Conclusion: We, therefore, propose several new WC modeling standards, software, and databases.

Significance: We anticipate that these new standards and software will enable more comprehensive models.

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Figures

None
The 2015 Whole-Cell Modeling Summer School in Rostock included the 54 participants listed in Table SI. Photo: University of Rostock IT and Media Center.
Figure 1
Figure 1
Comparison of the original and SBML transcription submodels. (A) The original transcription submodel included two sub-submodels: (1) a Markov model that describes how RNA polymerase switches among freely diffusing, non-specifically bound, and initiating states and (2) an ad hoc stochastic model that describes how RNA polymerase initiates transcription, elongates individual bases by walking along DNA, and terminates transcripts. (B) We created the SBML transcription submodel by simplifying the original submodel. The SBML submodel only represents transcription initiation, elongation, and termination; lumps the initiation, elongation, and termination of each RNA species into a single reaction; and does not explicitly represent DNA-protein binding. (C) An equivalent population-based ad hoc stochastic simulation algorithm for the original submodel. The original submodel was implemented using a more efficient particle-based algorithm. To facilitate comparison with the population-based SBML version, we have described an equivalent population-based algorithm. (D) We also improved the SBML submodel by replacing the ad hoc stochastic simulation algorithm with the Gillespie algorithm. (E) Statistics of the original and improved transcription submodels in population-based representations.
Figure 2
Figure 2
WC modeling workflow. Researchers will (1) assemble data into pathway/genome databases, (2) use these databases to construct models, (3) identify and verify models, (4) use multi-algorithm simulators to conduct in silico experiments, and (5) analyze these experiments to discover biology.
See this image and copyright information in PMC

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