Evaluating the integration of proteomic data for the prediction of intracellula...

• Autor(es): Costa, Rafael S. ; Machado, Daniel ; Ferreira, E. C. ; Rocha, I.
• Data: 2010
• Identificador Persistente: http://hdl.handle.net/1822/22315
• Origem: RepositóriUM - Universidade do Minho
• Assunto(s): E. coli network; Lin-log kinetics; Constraint-based approach; Data integration; Mutants flux prediction
• Tipo de Documento: Documento de conferência
• Idioma: Inglês

Descrição

So far, few large scale kinetic models of metabolic networks have been successfully constructed. The main reasons for this are not only the associated mathematical complexity, but also the large number of unknown kinetic parameters required in the rate equations to define the system. In contrast to kinetic models, the constraint-based modelling approach bypasses these difficulties by using basically only stoichiometric information with certain physicochemical constraints to delimit the solution space without large fitted parameter sets. Although these constraintbased models are highly relevant to predict feasible steady-state fluxes under a diverse range of genetic and environmental conditions, the steady-state assumption may oversimplify cellular behaviour and cannot predict time-course profiles. To overcome these problems, combining these two approaches appears as a reasonable alternative to modelling large-scale metabolic networks. On the other hand, several of the experimental data required for model construction are often rare and in this way it is usually assumed that the enzyme concentrations are constant. In this work, we used a central carbon metabolic network of E. coli to investigate whether including high throughput enzyme concentration data into a kinetic model allows improved predictions of metabolic flux distributions in response to single knockouts perturbations. For this purpose, an E. coli model, based on results obtained from flux balance analysis (FBA) and approximate lin-log kinetics was constructed. The intracellular fluxes distributions, obtained using this model, were compared with published in vivo measurements.