Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

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Microbial communities are increasingly recognized as key drivers in animal health, agricultural productivity, industrial operations, and ecological systems. The abundance of chemical interactions in these complex communities, however, can complicate or evade experimental studies, which hinders basic understanding and limits efforts to rationally design communities for applications in the aforementioned fields. Numerous computational approaches have been proposed to deduce these metabolic interactions -- notably including flux balance analysis (FBA) and systems of ordinary differential equations (ODEs) -- yet, these methods either fail to capture the dynamic phenotype expression of community members or lack the abstractions required to fit or explain the diverse experimental omics data that can be acquired today. We therefore developed a dynamic model (CommPhitting) that deduces phenotype abundances and growth kinetics for each community member, concurrent with metabolic concentrations, by coupling flux profiles for each phenotype with experimental growth and -omics data of the community. These data are captured as variables and coefficients within a mixed integer linear optimization problem (MILP) designed to represent the associated biological processes. This problem finds the globally optimized fit to all experimental data of a trial, thereby most accurately computing aspects of the community: (1) species and phenotype abundances over time; (2) a linearized growth kinetic constant for each phenotype; and (3) metabolite concentrations over time. We exemplify CommPhitting by applying it to study batch growth of an idealized two-member community of the model organisms (Escherichia coli and Pseudomonas flourescens) that exhibits cross-feeding in maltose media. Measurements of this community from our accompanying experimental studies -- including total biomass, species biomass, and metabolite abundances over time -- were parameterized into a CommPhitting simulation. The resultant kinetics constants and biomass proportions for each member phenotype would be difficult to ascertain experimentally, yet are important for understanding community responses to environmental perturbations and therefore engineering applications: e.g. for bioproduction. We believe that CommPhitting -- which is generalized for a diversity of data types and formats, and is further available and amply documented as a Python API -- will augment basic understanding of microbial communities and will accelerate the engineering of synthetic communities for diverse applications in medicine, agriculture, industry, and ecology.

Understanding cellular engagement with its environment is essential to control and monitor metabolism. Molecular Communication theory (MC) offers a computational means to identify environmental perturbations that direct or signify cellular behaviors by quantifying the information about a molecular environment that is transmitted through a metabolic system. We developed an model that integrates conventional flux balance analysis metabolic modeling (FBA) and MC, and additionally defined several channels of metabolic communication through environmental information transfers through a metabolism, to uniquely blend mechanistic biology and information theory to understand how substrate consumption is captured reaction activity, metabolite excretion, and biomass growth. The information flow in bits calculated through this workflow further determines the upper limit of information flow -- the maximal effect of environmental perturbations on cellular metabolism and behaviors -- since FBA simulates maximal efficiency of the metabolic system. We exemplify this method on two intestinal symbionts -- Bacteroides thetaiotaomicron and Methanobrevibacter smithii -- and visually consolidated the results into constellation diagrams that facilitate interpretation towards actionable design of controllable biological systems. The unique confluence of metabolic modeling and information theory in this model advances basic understanding of cellular metabolism and has applied value for the Internet of Bio-Nano Things, synthetic biology, microbial ecology, and autonomous laboratories, where the calculable control of cellular behaviors from environmental perturbations is imperative.