Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks

<p>Abstract</p> <p>Background</p> <p>In recent years, constrained optimization – usually referred to as flux balance analysis (FBA) – has become a widely applied method for the computation of stationary fluxes in large-scale metabolic networks. The striking advantage of FBA as compared to kinetic modeling is that it basically requires only knowledge of the stoichiometry of the network. On the other hand, results of FBA are to a large degree hypothetical because the method relies on plausible but hardly provable optimality principles that are thought to govern metabolic flux distributions.</p> <p>Results</p> <p>To augment the reliability of FBA-based flux calculations we propose an additional side constraint which assures thermodynamic realizability, i.e. that the flux directions are consistent with the corresponding changes of Gibb's free energies. The latter depend on metabolite levels for which plausible ranges can be inferred from experimental data. Computationally, our method results in the solution of a mixed integer linear optimization problem with quadratic scoring function. An optimal flux distribution together with a metabolite profile is determined which assures thermodynamic realizability with minimal deviations of metabolite levels from their expected values. We applied our novel approach to two exemplary metabolic networks of different complexity, the metabolic core network of erythrocytes (30 reactions) and the metabolic network iJR904 of <it>Escherichia coli </it>(931 reactions). Our calculations show that increasing network complexity entails increasing sensitivity of predicted flux distributions to variations of standard Gibb's free energy changes and metabolite concentration ranges. We demonstrate the usefulness of our method for assessing critical concentrations of external metabolites preventing attainment of a metabolic steady state.</p> <p>Conclusion</p> <p>Our method incorporates the thermodynamic link between flux directions and metabolite concentrations into a practical computational algorithm. The weakness of conventional FBA to rely on intuitive assumptions about the reversibility of biochemical reactions is overcome. This enables the computation of reliable flux distributions even under extreme conditions of the network (e.g. enzyme inhibition, depletion of substrates or accumulation of end products) where metabolite concentrations may be drastically altered.</p

A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks

The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total) in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample ($10^6$) of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility.Comment: 11 pages, 6 figures, 1 table; for associated supporting material see http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.100256

Application of a Microcalorimetric Method for Determining Drug Susceptibility in Mycobacterium Species

Mycobacterium tuberculosis is a global public health concern, particularly with the emergence of drug-resistant strains. Immediate identification of drug-resistant strains is crucial to administering appropriate treatment before the bacteria are allowed to spread. However, developing countries, which are most affected by drug resistance, are struggling to combat the disease without the facilities or funds for expensive diagnostics. Recent studies have emphasized the suitability of isothermal microcalorimetry (IMC) for the rapid detection of mycobacteria. In this study, we investigate its suitability for rapid and reliable M. tuberculosis drug susceptibility testing. Specifically, IMC was used to determine the MICs of three drugs, namely, isoniazid, ethambutol, and moxifloxacin, against three mycobacteria, namely, Mycobacterium smegmatis, Mycobacterium avium, and Mycobacterium tuberculosis. The Richards growth model was used to calculate growth parameters, namely, the maximum bacterial growth rate and the lag phase duration from integrated heat flow-versus-time results. For example, MICs of isoniazid, ethambutol, and moxifloxacin were determined to be 1.00, 8.00, and 0.25 μg/ml, respectively. IMC, as described here, could be used not just in industrialized countries but also in developing countries because inexpensive and sensitive microcalorimeters are now available