Roger L. Chang

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MOTIVATION Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems. RESULT In this paper, we present a framework to identify modules within biological networks. In this approach, the concept of degree is(More)
Growth is a fundamental process of life. Growth requirements are well-characterized experimentally for many microbes; however, we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high-level behavior of the cell as a whole. Here, we construct an ME-Model for Escherichia(More)
Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species(More)
The BioHealthBase Bioinformatics Resource Center (BRC) (http://www.biohealthbase.org) is a public bioinformatics database and analysis resource for the study of specific biodefense and public health pathogens-Influenza virus, Francisella tularensis, Mycobacterium tuberculosis, Microsporidia species and ricin toxin. The BioHealthBase serves as an extensive(More)
Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein(More)
Enzymes are thought to have evolved highly specific catalytic activities from promiscuous ancestral proteins. By analyzing a genome-scale model of Escherichia coli metabolism, we found that 37% of its enzymes act on a variety of substrates and catalyze 65% of the known metabolic reactions. However, it is not apparent why these generalist enzymes remain.(More)
Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating(More)
Ali Ebrahim, Eivind Almaas, Eugen Bauer, Aarash Bordbar, Anthony P Burgard, Roger L Chang, Andreas Dräger, Iman Famili, Adam M Feist, Ronan MT Fleming, Stephen S Fong, Vassily Hatzimanikatis, Markus J Herrgård, Allen Holder, Michael Hucka, Daniel Hyduke, Neema Jamshidi, Sang Yup Lee, Nicolas Le Novère, Joshua A Lerman, Nathan E Lewis, Ding Ma, Radhakrishnan(More)
The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to(More)
Circadian oscillators are posttranslationally regulated and affect gene expression in autotrophic cyanobacteria. Oscillations are controlled by phosphorylation of the KaiC protein, which is modulated by the KaiA and KaiB proteins. However, it remains unclear how time information is transmitted to transcriptional output. We show reconstruction of the KaiABC(More)