Search

Graphical Abstract Recent results obtained by Chindelevitch et al (2014) are challenged by Ebrahim et al, illustrating several issues in the reproducibility of computational biology methods.

Abstract Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome‐scale metabolic models have shown utility in integrating ‐omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host–pathogen interactions using in silico mass‐balanced, genome‐scale models has not been performed. We, therefore, constructed a cell‐specific alveolar macrophage model, iAB‐AMØ‐1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host–pathogen genome‐scale reconstruction, iAB‐AMØ‐1410‐Mt‐661. The integrated host–pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High‐throughput data from infected macrophages were mapped onto the host–pathogen network and were able to describe three distinct pathological states. Integrated host–pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.

Abstract The biological community is now awash in high‐throughput data sets and is grappling with the challenge of integrating disparate data sets. Such integration has taken the form of statistical analysis of large data sets, or through the bottom–up reconstruction of reaction networks. While progress has been made with statistical and structural methods, large‐scale systems have remained refractory to dynamic model building by traditional approaches. The availability of annotated genomes enabled the reconstruction of genome‐scale networks, and now the availability of high‐throughput metabolomic and fluxomic data along with thermodynamic information opens the possibility to build genome‐scale kinetic models. We describe here a framework for building and analyzing such models. The mathematical analysis challenges are reflected in four foundational properties, (i) the decomposition of the Jacobian matrix into chemical, kinetic and thermodynamic information, (ii) the structural similarity between the stoichiometric matrix and the transpose of the gradient matrix, (iii) the duality transformations enabling either fluxes or concentrations to serve as the independent variables and (iv) the timescale hierarchy in biological networks. Recognition and appreciation of these properties highlight notable and challenging new in silico analysis issues.