(605a) Simulation of Single Microbes and Microbial Communities in Dynamic Environments | AIChE

(605a) Simulation of Single Microbes and Microbial Communities in Dynamic Environments


We live in a world dominated by microbes that play a crucial role in natural and biotechnological processes. Microbes typically coexist in complex microbial communities and live in dynamic environments that affect their growth. Understanding the interplay between microbes and their environments can be key for predicting microbial function and designing microbial processes. From a modeling perspective, the growth of single microbes and microbial communities in dynamic environments has been successfully simulated by dynamic Flux Balance Analysis (dFBA). This method combines differential equations with optimization problems and has been solved with various approaches in the past. However, most of these approaches are computationally expensive and difficult to scale, which hinders their use in metabolic engineering applications. In this work, we present a reformulation of the dFBA problem into a system of Differential-Algebraic Equations (DAEs) that can be solved with direct collocation and an interior point reformulation method. We provide a computationally efficient implementation in the Julia programming language that can be used in simulations with genome-scale metabolic models and can be scaled-up for microbial communities. More specifically, for microbial communities of up to three species, the presented dFBA approach outperforms existing approaches by offering almost half solution time. We also demonstrate the ability of the method to incorporate higher level objectives, such as product maximization, and its potential for strain design by predicting knockdowns and knockouts. Based on simulations involving E. coli strains, bacterial species from the human gut microbiome, and bacterial members of the dechlorinating ACT3 community, we demonstrate that the proposed method can have significant impact in metabolic engineering applications and in bioprocess optimal control.