Mohammad Mazharul Islam

TL;DR: d-OptCom is introduced, an extension of the OptCom procedure, for the dynamic metabolic modeling of microbial communities that enables capturing the temporal dynamics of biomass concentration of the community members and extracellular concentration of their shared metabolites, while integrating species- and community-level fitness functions.

TL;DR: The elucidation of metabolite exchange among the three members of the rumen microbiome shows how their genomic differences and interactions with the viral strains shape up a highly sophisticated metabolic interplay and explains how such interactions across kingdoms can cause metabolic and compositional shifts in the community and affect the health, nutrition, and pathophysiology of the ruminant animal.

TL;DR: This chapter highlights a number of such frameworks developed in recent years in order to generate testable hypotheses (in terms of genetic interventions), thus addressing the challenges in metabolic engineering.

TL;DR: In this paper, an updated and enhanced genome-scale metabolic model of Staphylococcus aureus has been presented, combining genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models.

TL;DR: Kinetic models of metabolism are predictors of temporal metabolic behavior of living organisms that integrate multi-omics datasets with reaction network models to interpret reaction rates, kinetic parameters, and enzyme levels.