Understanding microbial metabolism using computational methods at different levels of abstraction

Abstract

Understanding microbial metabolism and its effect on the surrounding environment represents a critical challenge in microbial ecology, requiring sophisticated computational approaches that can bridge molecular-level interactions with complex ecosystem dynamics. This research presents a comprehensive suite of computational methods that advance our ability to model and predict microbial system behaviors at multiple levels of complexity. This work introduces four computational distinct approaches that address key limitations in existing microbiome modeling techniques: • A bottom-up cellular metabolism model that enables predicting cellular phenotype using a simplified kinetic model for a replicating bacterial cell • SPAM-DFBA, a novel approach integrating dynamic flux balance analysis with reinforcement learning • ADToolbox, a metagenome-informed tool for modeling anaerobic digestion processes • A bioinformatics approach for analyzing complex host-microbiota interactions in mice with colorectal cancer By systematically exploring the trade-offs between model complexity and predictive power, this research expands the analytical toolkit available to microbiome researchers. The methodological progression demonstrates how computational techniques can overcome significant challenges in metabolic modeling, including limited kinetic parameters and biochemical knowledge gaps. Key contributions include: • Novel optimization techniques for simulating cellular metabolism • Enhanced modeling approaches for heterogeneous bacterial communities • Integration of metagenomic data into predictive computational frameworks These advances represent a significant step forward in our ability to understand, predict, and potentially manipulate microbial systems across diverse contexts, from industrial biotechnology to human health applications.