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Biomedical subjects

Matthew A Wright

Publications and source records attributed to Matthew A Wright.

3 recordsLinked to original sources
PubMed2003

On the complete determination of biological systems.

The nascent field of systems biology ambitiously proposes to integrate information from large-scale biology projects to create computational models that are, in some sense, complete. However, the details of what would constitute a complete systems-level model of an organism are far from clear. To provide a framework for this difficult question it is useful to define a model as a set of rules that maps a set of inputs (e.g. descriptions of the cell's environment) to a set of outputs (e.g. the concentrations of all its RNAs and proteins). We show how the properties of a model affect the required experimental sampling and estimate the number of experiments needed to "complete" a particular model. Based on these estimates, we suggest that the complete determination of a biological system is a concrete, achievable goal.

Cell Physiological Phenomena↗
PubMed2003

From annotated genomes to metabolic flux models and kinetic parameter fitting.

Significant advances in system-level modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steady-state models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of whole-cell kinetic parameters.

Algorithms↗