Whole-Colony Modeling of Escherichia coli
Skalnik, C. J.; Agmon, E.; Spangler, R. K.; Talman, L.; Morrison, J. H.; Peirce, S. M.; Covert, M. W.
Show abstract
Bacterial behavior is the outcome of both molecular mechanisms within each cell and interactions between cells in the context of their environment. Whereas whole-cell models simulate a single cells behavior using molecular mechanisms, agent-based models simulate many agents independently acting and interacting to generate complex collective phenomena. To synthesize agent-based and whole-cell modeling, we used a novel model integration software, called Vivarium, to construct an agent-based model of E. coli colonies where each agent is represented by a current source code snapshot from the E. coli Whole-Cell Modeling Project and interacts with other cells in a shared spatial environment. The result is the first "whole-colony" computational model that mechanistically links expression of individual proteins to a population-level phenotype. Simulated colonies exhibit heterogeneous effects on their environments, heterogeneous gene expression, and media-dependent growth. Extending the cellular model with mechanisms of antibiotic susceptibility and resistance, our model also suggested that variation in the expression level of the betalactamase AmpC, and not of the multi-drug efflux pump AcrAB-TolC, was the key mechanistic driver of survival in the presence of nitrocefin. We see this as a significant step forward in the creation of more comprehensive multi-scale models, and it broadens the range of phenomena that can be modeled in mechanistic terms. Author summaryThis work combines several models of molecular and physical processes that impact the physiology and behavior of the common microbe Escherichia coli into a multiscale model. Colonies comprised of multiple individual cells are simulated as they grow and divide--each with complex internal mechanisms, and with physical interactions and molecular diffusion in their environments. The integrative modeling methodology supports the addition of new submodels. The flexibility of this methodology is demonstrated by adding models of antibiotic resistance and simulating the colonys response to antibiotic treatment.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0 96%
- Evaluation of CD8 T cell killing models with computer simulations of 2-photon imaging experiments 95%
- Modular assembly of dynamic models in systems biology 95%
Similar papers in this journal
- Data-driven models reveal mutant cell behaviors important for myxobacterial aggregation 96%
- Cell growth model with stochastic gene expression helps understand the growth advantage of metabolic exchange and auxotrophy 95%
- Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.