OptMDFpathway: Identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli.

Oliver Hädicke,Axel Von Kamp,Timur Aydogan,Steffen Klamt,Herbert Sauro

doi:10.1371/journal.pcbi.1006492

Abstract

Constraint-based modeling techniques have become a standard tool for the in silico analysis of metabolic networks. To further improve their accuracy, recent methodological developments focused on integration of thermodynamic information in metabolic models to assess the feasibility of flux distributions by thermodynamic driving forces. Here we present OptMDFpathway, a method that extends the recently proposed framework of Max-min Driving Force (MDF) for thermodynamic pathway analysis. Given a metabolic network model, OptMDFpathway identifies both the optimal MDF for a desired phenotypic behavior as well as the respective pathway itself that supports the optimal driving force. OptMDFpathway is formulated as a mixed-integer linear program and is applicable to genome-scale metabolic networks. As an important theoretical result, we also show that there exists always at least one elementary mode in the network that reaches the maximal MDF. We employed our new approach to systematically identify all substrate-product combinations in Escherichia coli where product synthesis allows for concomitant net CO2 assimilation via thermodynamically feasible pathways. Although biomass synthesis cannot be coupled to net CO2 fixation in E. coli we found that as many as 145 of the 949 cytosolic carbon metabolites contained in the genome-scale model iJO1366 enable net CO2 incorporation along thermodynamically feasible pathways with glycerol as substrate and 34 with glucose. The most promising products in terms of carbon assimilation yield and thermodynamic driving forces are orotate, aspartate and the C4-metabolites of the tricarboxylic acid cycle. We also identified thermodynamic bottlenecks frequently limiting the maximal driving force of the CO2-fixing pathways. Our results indicate that heterotrophic organisms like E. coli hold a possibly underestimated potential for CO2 assimilation which may complement existing biotechnological approaches for capturing CO2. Furthermore, we envision that the developed OptMDFpathway approach can be used for many other applications within the framework of constrained-based modeling and for rational design of metabolic networks.

Highlights

The reconstruction of organism-specific genome-scale metabolic models and their in silico analysis by techniques of constraint-based modeling has become a key to understand structure, function, and capabilities of metabolic networks [1,2,3]
While those problems can be solved with established methods of constraint-based modeling, no algorithm is currently available for genomescale models to identify the pathway that has the highest possible thermodynamic driving force among all solutions with predefined stoichiometric properties
While E. coli cannot assimilate CO2 into biomass, net CO2 fixation can take place along linear pathways from substrate to product and we show that thermodynamically feasible pathways with net CO2 assimilation exist for 145 (34) products when choosing glycerol as substrate

Summary

Introduction

The reconstruction of organism-specific genome-scale metabolic models and their in silico analysis by techniques of constraint-based modeling has become a key to understand structure, function, and capabilities of metabolic networks [1,2,3]. The MDF method together with a pathway identification procedure was used to identify thermodynamically feasible synthetic pathways that were assembled with enzymes from different organisms [11]. The latter as well as the OptMDFpathway: Computing metabolic pathways with maximal thermodynamic driving force original approach for MDF computation presented in [29] necessitate that a specific pathway is given a priori. One approach could be to enumerate the complete set of elementary modes (or other pathway vectors) followed by a subsequent computation of their respective MDF values This approach is limited to medium-size network and can not be used for genome-scale models

Methods

Results

Discussion

Conclusion