Integrated genomic and proteomic analysis of a systematically perturbed metabolic network pdf
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- Integrated genomic and proteomic analyses of a systematically perturbed metabolic network
- Inventories to insights
- Systems Biology: The Next Frontier for Bioinformatics
With the completion of the genomic sequencing of a number of species, including that of humans, much attention is currently focused on how the information in these sequences might be interpreted in terms of the structure, function, and control of biologic systems and processes. Quantitative proteome analysis, the global analysis of protein expression, is increasingly being used as a method to study steady-state and perturbation-induced changes in protein profiles. The rationale for quantitative proteome analysis is described, along with a new technology for high throughput quantitative profiling of proteins in complex mixtures and its current status with selected applications.
Integrated genomic and proteomic analyses of a systematically perturbed metabolic network
John D. Aitchison, Timothy Galitski; Inventories to insights. J Cell Biol 12 May ; 3 : — We have entered the cell, the Mansion of our birth and started the inventory of our acquired wealth. Never before have Albert Claude's words been truer. Cell biologists now have at their disposal the entire inventory of genes in many organisms, and technologies that can enable the global interrogation of macromolecules and the structures they form.
Vladimir A. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving a quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, b development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and c applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.
Inventories to insights
Metrics details. Predicting cellular responses to perturbations is an important task in systems biology. We report a new approach, RELATCH, which uses flux and gene expression data from a reference state to predict metabolic responses in a genetically or environmentally perturbed state. Using the concept of relative optimality, which considers relative flux changes from a reference state, we hypothesize a relative metabolic flux pattern is maintained from one state to another, and that cells adapt to perturbations using metabolic and regulatory reprogramming to preserve this relative flux pattern. This constraint-based approach will have broad utility where predictions of metabolic responses are needed. Computational modeling of metabolic networks has been useful in studying microbial metabolism and developing tools for many applications. Among different computational approaches, constraint-based models utilize genome-scale metabolic networks to predict metabolic flux distributions in microbial cells, and they have been used to guide metabolic engineering [ 1 ], drug discovery [ 2 ], and adaptive evolution [ 3 ] studies.
Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis FBA , a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations. A central challenge in the development of systems biology is the integration of high-throughput data to generate predictive computational models. Genomics provides data on a cell's DNA sequence, transcriptomics on the mRNA expression of cells, proteomics on a cell's protein composition, and metabolomics on a cell's metabolite abundance. Computational methods are needed to reduce this dimensionality across the wide spectrum of omics data to improve understanding of the underlying biological processes Cakir et al.
Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network. May ; Science ()
Systems Biology: The Next Frontier for Bioinformatics
Cancer Chemoprevention pp Cite as. However, the effort made to elucidate this index is not likely to be rewarded by any real clinical impact, as the function of proteins is closely tied to their cellular, tissue, and physiological context. The ultimate goal of clinical proteomics the translational subdiscipline of the larger field is really twofold. First, characterize information flow through protein networks—which are deranged as a cause or consequence of disease processes as they exist, not in cell culture or animal models systems, but in the tissue microenvironment of the host—and how that information content changes during therapeutic intervention; second, develop biomarker profiling technologies to detect disease earlier and treat it more effectively.
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