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Between the systemic properties of robustness and adaptability arising from the interplay of metabolic network structure and changing environment.Keywords: Flux-based methods, Genome-scale metabolic network, Network optimization, AdaptationBackgroundThe steady-state metabolism of microorganisms has evolved to optimize growth under ambient conditions [1]. However, under suboptimal conditions or upon perturbation, organisms must maintain homeostasis and adapt their modes of operation to ensure viability [2]. Maintenance of homeostasis has already been addressed in the Staurosporine context of studying system's robustness [3,4]. The underlying mechanisms stabilize a cellular function under*Correspondence: nikoloski@mpimp-golm.mpg.de 1 Systems Biology and Mathematical Modeling Group, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam, Germany Full list of author information is available at the end of the articlechanging conditions and often involve feedback control [5,6]. In turn, adaptability refers to adjustment in systemic properties (e.g., utilization of available nutrients) in order to facilitate the transition between conditions. The two properties--robustness and adaptability--do not exclude each other since both arise from the necessity of an organism to cope with its environment. While robustness has been widely studied [4,7], (metabolic) adaptability has not been systematically investigated, largely due to the lack of a precise formulation and its global effects on the organism. Therefore, any approach to capture and analyze adaptation-related processes requires the consideration of a comprehensive??2012 Topfer et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.?Topfer et al. BMC Systems Biology 2012, 6:148 http://www.biomedcentral.com/1752-0509/6/Page 2 ofnetwork of metabolic pathways in order to capture the complex interplay of network constituents. Several approaches that integrate data with graphtheoretic methods have been applied to obtain subnetworks engaged under different conditions. For instance, [8] uses transcriptomics data in combination with protein-protein interaction networks to identify active subnetworks that show levels in differential expression for particular subsets of conditions. However, graphtheoretic approaches neglect the stoichiometry of the considered biochemical reactions. Thus, it is difficult to PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/16989806 relate the findings from these approaches to network functionality and growth. With the increasing availability and quality of genomescale metabolic models and high-throughput data, constraint-based methods that integrate these data have found broad applications. For instance, a genome-scale metabolic model has been coupled with transcriptomics data, based on Boolean logic, to improve flux predictions [9]. Thereby, a flux is constrained to zero, if the respective transcript has not been observed. Another attempt employs transcriptomics and proteomics data to derive tissue-specific metabolic activity [10] and is based on a trivalued logic to maximize the number of reactions in the network that are consistent with the expression data. To overcome the issue of selecting an arbitrary threshold value in considering a gene "on" or "off ", a method, referred to.

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