Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses.

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

Vivarium: an interface and engine for integrative multiscale modeling in computational biology.

MOTIVATION: This article introduces Vivarium-software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists. RESULTS: Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs and run with Vivarium's discrete-event simulation engine. Vivarium's utility is demonstrated by building composite models that combine several modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics and spatial diffusion. This demonstrates just the beginning of what is possible-Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales. AVAILABILITY AND IMPLEMENTATION: The specific models, simulation pipelines and notebooks developed for this article are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on Python Package Index. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely available Vivarium processes and composites, including the processes used in Section 3. Supplementary Materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modular genetic control of social status in a cichlid fish.

Social hierarchies are ubiquitous in social species and profoundly influence physiology and behavior. Androgens like testosterone have been strongly linked to social status, yet the molecular mechanisms regulating social status are not known. The African cichlid fish Astatotilapia burtoni is a powerful model species for elucidating the role of androgens in social status given their rich social hierarchy and genetic tractability. Dominant A. burtoni males possess large testes and bright coloration and perform aggressive and reproductive behaviors while nondominant males do not. Social status in A. burtoni is in flux, however, as males alter their status depending on the social environment. Due to a teleost-specific whole-genome duplication, A. burtoni possess two androgen receptor (AR) paralogs, ARα and ARß, providing a unique opportunity to disentangle the role of gene duplication in the evolution of social systems. Here, we used CRISPR/Cas9 gene editing to generate AR mutant A. burtoni and performed a suite of experiments to interrogate the mechanistic basis of social dominance. We find that ARß, but not ARα, is required for testes growth and bright coloration, while ARα, but not ARß, is required for the performance of reproductive behavior and aggressive displays. Both receptors are required to reduce flees from females and either AR is sufficient for attacking males. Thus, social status in A. burtoni is inordinately dissociable and under the modular control of two AR paralogs. This type of nonredundancy may be important in facilitating social plasticity in A. burtoni and other species whose social status relies on social experience.