BioSimulators: a central registry of simulation engines and services for recommending specific tools

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

A Computational Model of Adaptive Sensory Processing in the Electroreception of Mormyrid Electric Fish

Electroreception is a sensory modality found in some fish, which enables them to sense the environment through the detection of electric fields. Biological experimentation on this ability has built an intricate framework that has identified many of the components involved in electroreception\u27s production, but lack the framework for bringing the details back together into a system-level model of how they operate together. This thesis builds and tests a computational model of the Electrosensory Lateral Line Lobe (ELL) in mormyrid electric fish in an attempt to bring some of electroreception\u27s structural details together to help explain its function. The ELL is a brain region that functions as a primary processing area of electroreception. It acts as an adaptive filter that learns to predict reoccurring stimuli and removes them from its sensory stream, passing only novel inputs to other brain regions for further processing. By creating a model of the ELL, the relevant components which underlie the ELL\u27s functional, electrophysiological patterns can be identified and scientific hypotheses regarding their behavior can be tested. Systems science\u27s approach is adopted to identify the ELL\u27s relevant components and bring them together into a unified conceptual framework. The methodological framework of computational neuroscience is used to create a computational model of this structure of relevant components and to simulate their interactions. Individual activation tendencies of the different included cell types are modeled with dynamical systems equations and are interconnected according to the connectivity of the real ELL. Several of the ELL\u27s input patterns are modeled and incorporated in the model. The computational approach claims that if all of the relevant components of a system are captured and interconnected accurately in a computer program, then when provided with accurate representations of the inputs a simulation should produce functional patterns similar to those of the real system. These simulated patterns generated by the ELL model are compared to recordings from real mormyrid ELLs and their correspondences validate or nullify the model\u27s integrity. By building a computation model that can capture the relevant components of the ELL\u27s structure and through simulation reproduces its function, a systems-level understanding begins to emerge and leads to a description of how the ELL\u27s structure, along with relevant inputs, generate its function. The model can be manipulated more easily than a biological ELL, and allows us to test hypotheses regarding how changes in the structures affect the function, and how different inputs propagate through the structure in a way that produces complex functional patterns

The foundation of agency: An exploration of how minimal organisms emerge from and adapt to their environments

Agents are of central importance to cognitive science, but research usually takes them as pre-given and proceeds to study some of their particular aspects, often without awareness of or a definite answer to the question, “what is an agent?” The conceptual framework of autopoiesis and enaction provides a foundation that defines agents as emergent individuals that act in an environment to fulfill their physiological needs. To establish this definition in concrete examples, this dissertation introduces computational models and analyses that demonstrate several properties of agents. It examines an artificial chemistry that supports the emergence of minimal protocells. These protocells have a metabolism made of autocatalytic components, and an external boundary that self-assembles and encapsulates the metabolism, keeping it from diffusing into the environment. Metabolism and boundary are mutually enabling processes, which together counter the effects of diffusion and decay. When their symbiosis is broken, the protocell disintegrates. Systematic analysis reveals the rich consequences of protocellular organizations. Ontogenies are mapped as network structures, with the networks' nodes as reachable protocell morphologies and its edges as the transitions between morphologies. Analyses of these networks reveal properties such as irreversibility (some changes cannot be reversed under any circumstance) and branching (unfolding down one ontogeny excludes morphologies accessible by other ontogeny). Viability is quantified as expected lifespan, and measured across different protocell configurations. This provides a basis for measuring agents' basic goal of adaptivity — to increase their viability in a given environment through internal restructuring or environmental change. The cellular Potts model (CPM) framework is examined to study structural coupling (the bi-directional interactions between an agent and environment). The network-based methodology for analyzing ontogenies is extended to incorporate a local environmental state and is demonstrated in a CPM. This reveals several interesting features, such as a divergence in the space of possible ontogenies when placed in different environments, and that niche construction can increase an individual's viability

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

BioSimulators: a central registry of simulation engines and services for recommending specific tools.

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