Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

Abstract

This review is a part of the SI ‘Genome-Scale Modeling of Microorganisms in the Real World’. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.

Systematic elucidation of independently modulated genes inLactobacillus plantarumreveals a trade-off between secondary and primary metabolism

“…With regards to the relationship between secondary and primary metabolism, theoretical models such as Grime's competitor-stress-ruderal triangle [40][41], Synthetic Chemostat Model [42] and regulatory proteome allocation model [43] all adopted a resource allocation framework to capture the balance between two branches of cellular metabolism. Through the correlations between the activities of identified IMs, this study provided evidence to the theoretical models for secondary metabolism proposed in previous studies by showing the growth strategy of L. plantarum that adjusts regulatory activities for different metabolic pathways to react to external stress signals (section 3.4).…”

Systematic elucidation of independently modulated genes in Lactiplantibacillus plantarum reveals a trade‐off between secondary and primary metabolism

“…With regard to the relationship between secondary and primary metabolism, theoretical models such as Grime's competitor‐stress‐ruderal triangle (Bruggeman et al., 2023; Grime, 1977), Synthetic Chemostat Model (Panikov, 2021) and regulatory proteome allocation model (Qiu et al., 2023) all adopted a resource allocation framework to capture the balance between two branches of cellular metabolism. Through the correlations between the activities of identified IMs, this study provided evidence to the theoretical models for secondary metabolism proposed in previous studies by showing the growth strategy of L. plantarum that adjusts regulatory activities for different metabolic pathways to react to external stress signals (Section “The Trade‐off between Primary and Secondary Metabolism Revealed by iModulon Activities”).…”

Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities

“…In addition, microbes have been shown to exhibit submaximal growth, which needs to be sufficiently addressed with GEMs. While phenotypic heterogeneity and sub-maximal growth dynamics have been studied in individual GEMs of microbial activity , these two phenomena have not been analyzed for models of microbial interactions 30,[52][53][54][55][56][57][58] . In this work, we demonstrate how pairing disparate existing approaches of flux sampling and modeling of communities pushes the field of metabolic modeling forward.…”