Bacteria are not simply passive consumers of nutrients or merely steady-state systems. Rather bacteria are active participants in their environments, collecting information from their surroundings, processing and using that information to adapt their behavior and optimize survival. The bacterial regulome is the set of physical interactions that link environmental information to the expression of genes by way of networks of sensors, transporters, signal cascades, and transcription factors. Optimizing biological systems for industrial bioprocesses requires a deep understanding of the bacterial regulatory networks and the ability to rationally engineer them to produce desired bacterial outputs. When the information processing and environmental sensing capabilities of living bacteria are welded to predictive computational design of regulatory networks, then bacteria become fully programmable biosystems that are capable of novel bioprocesses that are unachievable by other chemical means. Modeling regulatory networks using optimization criteria drawn from Information Theory is a unique modeling application in our research laboratory. The ability to reliably engineer the regulatory systems of microorganisms enables optimization of fermentation titers, rates, and yields by controlling the timing of metabolic pathways, monitoring of fermentation titers and rates by microorganisms to maximize yield, or to minimize toxicity by regulating production and/or transport of desired bioproducts. Generation of regulatory models is a multi-omic process, requiring close collaborations between laboratory experimentalists and computational modelers to generate models with useful predictive capabilities. Requires large, transcriptomics datasets at minimum (preferably with genetic, environmental, and temporal components)
References and Additional Information:
P Larsen, E Almasri, G Chen, Y Dai (2007). A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments. BMC bioinformatics 8 (1), 317
PE Larsen, LJ Cseke, RM Miller, FR Collart (2014). Modeling forest ecosystem responses to elevated carbon dioxide and ozone using artificial neural networks. Journal of theoretical biology 359, 61-71
PE Larsen, A Sreedasyam, G Trivedi, S Desai, Y Dai, LJ Cseke (2016). Multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction. Frontiers in plant science 6, 1061.
Argonne National Laboratory