Optimality of extracellular enzyme production and activity in dynamic flux balance modeling
Quintin, M.; Dukovski, I.; Bhatnagar, J.; Segre, D.
Show abstract
In microbial communities, many vital metabolic functions, including the degradation of cellulose, proteins and other complex macromolecules, are carried out by costly, extracellularly secreted enzymes. While significant effort has been dedicated to analyzing genome-scale metabolic networks for individual microbes and communities, little is known about the interplay between global allocation of metabolic resources in the cell and extracellular enzyme secretion and activity. Here we introduce a method for modeling the secretion and catalytic functions of extracellular enzymes using dynamic flux balance analysis. This new addition, implemented within COMETS (Computation Of Microbial Ecosystems in Time and Space), simulates the costly production and secretion of enzymes and their diffusion and activity throughout the environment, independent of the producing organism. After tuning our model based on data for a Saccharomyces cerevisiae strain engineered to produce exogenous cellulases, we explored the dynamics of the system at different cellulose concentrations and enzyme production rates. We found that there are distinct rates of constitutive enzyme secretion which maximize either growth rate or biomass yield. These optimal rates are strongly dependent on enzyme kinetic properties and environmental conditions, including the amount of cellulose substrate available. Our framework will facilitate the development of more realistic simulations of microbial community dynamics within environments rich in complex macromolecules, with applications in the study of soil and plant-associated ecosystems, and other natural and engineered microbiomes. ImportanceMany organisms - including soil, marine and human-associated bacteria and fungi - perform part of their metabolic functions outside of the boundary of the cell, through the secretion of extracellular enzymes that can diffuse and facilitate reactions independently of the organism that produced them. In order to better understand and predict microbial ecosystems, it would be helpful to create mathematical models incorporating these extracellular reactions within simulations of metabolism at the whole-cell level. In this paper we demonstrate the implementation of such a methodology and apply it to study a cellulase-secreting yeast. This work will be useful for a number of microbial ecology applications, including modeling of microbiome dynamics, engineering of bioproducts (e.g. biofuels) from plant biomass through synthetic communities or modified organisms, and testing of basic ecological hypotheses about the balance between cost and benefits of the production of common goods in microbial communities.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition 97%
- Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs 97%
- Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth 97%
Similar papers in this journal
- Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts 97%
- The effects of model complexity and size on metabolic flux distribution and control. Case study in E. coli. 95%
- PEMPS: A Phylogenetic Software Tool to Model the Evolution of Metabolic Pathways 94%
Similar papers in this journal
- Unveiling abundance-dependent metabolic phenotypes of microbial communities 96%
- Cell growth model with stochastic gene expression helps understand the growth advantage of metabolic exchange and auxotrophy 95%
- Kinetics-based Inference of Environment-Dependent Microbial Interactions and Their Dynamic Variation 94%
Similar papers in this journal
- Model-guided gene circuit design for engineering genetically stable cell populations in diverse applications 96%
- Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis. 96%
- Modular Dynamic Biomolecular Modelling with Bond Graphs: The Unification of Stoichiometry, Thermodynamics, Kinetics and Data. 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.