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The homeostatic framework has dominated our understanding of cellular physiology. We question whether homeostasis alone adequately explains microbial responses to environmental stimuli, and explore the capacity of intracellular networks for predictive behavior in a fashion similar to metazoan nervous systems. We show that in silico biochemical networks,(More)
We present a methodology that aims to elucidate regulatory mechanisms by grouping together genes which share the same regulatory network. In our method, we use multi-state partition functions and thermodynamic models to derive six distinct correlation classes that correspond to various protein-protein and protein-DNA interactions. We then introduce a novel(More)
The trend of opening government data, in order to be used for scientific, commercial and political purposes, has resulted in the development of numerous e-infrastructures providing public sector information (PSI). The big investments that have been made in this direction necessitate a deeper understanding and assessment of the value they produce. This paper(More)
One of the central challenges in computational biology is the development of predictive multi-scale models that can capture the diverse layers of cellular organization. Even more scarce are models that encode biophysical phenomena together with evolutionary forces, in order to provide insight on the effect of adaptation at a systems-level. Over the last(More)
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