Multiscale Quantitative Systems Biology. In vitro and Ex vivo Studies: Using a modular approach, my lab is developing novel multi-scale in vitro and in silico models of host-pathogen systems, with a focus on bacterial infections that may lead to latent and reactivated diseases. We focus on quantifying the dynamics and impact of host-induced stress on the metabolic and genetic response of Mycobacterium tuberculosis and Francisella tularensis (FT), a potential biothreat agent. In silico Studies: We use multi-omic empirical data from in vitro, ex vivo, and in vivo studies to drive theoretical model development at the intracellular, extracellular, and cellular scales. We have integrated BioXyce, a large scale biological circuity simulation platform, into our recently developed multiscale agent-based platform (co-developed with Sandia Nat’l Labs). Studies of microbial communities: We are applying our multiscale methods to mechanistically model microbial cooperativity and the use of quorum sensing to modulate Escherichia coli biofilm formation.
BioXyce - a large-scale biological network simulator to model microenvironment dynamics. Recognizing the similarity between large-scale simulation of biochemical systems and simulation of large, integrated electronic circuits, [May and Schiek] co-developed BioXyce using tools originally created for electrical circuit simulation. The novelty and significance of our work was in the ability to define engineering parallels to biological processes, which enabled the simulation of biochemical dynamics on a massively parallel platform. We have used the BioXyce platform to model Mtb metabolic fitness during oxidative stress, a factor in TB persistence and host development of latent TB infection. The platform is extendable to model non-bacterial systems.
Computational Biosensors & Biological Coding Theory (BCT) . The theoretical work in BCT (by May, Bitzer, and Vouk) laid a foundation that continues to be developed by various researchers in the field of biological communication and information theory as a way to model molecular scale information exchange, which has implications on the basic science of molecular interactions. By generalizing the theoretical framework for biological error control coding theory, we (May) developed methods for design of deoxyribozyme-based computational biosensors. Compared to traditional microarray-based systems, computational biosensors are a paradigm shift in that, as demonstrated in collaboration with the Brozik Lab (Sandia Nat’l Labs), they enable the concurrent detection and classification of pathogens and genetically modified organisms within the biological substrate.
Systems Chemical Biology. Building on the BioXyce platform, in collaboration with the Oprea lab (UNM-HSC) and the Tropsha Groups (UNC – Chapel Hill) [May] co-developed the systems chemical biology (SCB) platform. Systems chemical biology is a method for evaluating the effect of small molecule therapeutics at the level of the entire biological system. SCB provides a tool that can be used to support reverse pharmacology, and inform novel therapeutic strategies that target both pathogen and host. We successfully demonstrated the feasibility of using the SCB platform to analyze the outcome of multi-substrate inhibition of the Mtb latency-associated glyoxylate pathway during oxidative stress.
FUNDING SUPPORT
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