INESC-ID   Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa
technology from seed


Knowledge Discovery and Bioinformatics
Inesc-ID Lisboa

In silico Metabolic Engineering

03/12/2010 - 14:00
03/12/2010 - 15:00

Metabolic Engineering (ME) deals with designing organisms with enhanced capabilities regarding the productivities of desired compounds. This field has received increasing attention within the last few years due to the extraordinary growth in the adoption of white or industrial biotechnological processes for the production of bulk chemicals, pharmaceuticals, food ingredients and enzymes, among other products. Many different approaches have been used to aid in ME efforts that take available models of metabolism together with mathematical tools and/ or experimental data to identify metabolic bottlenecks or targets for genetic engineering. Our conceptual framework in the development of tools for in silico ME relies on three layers: accurate mathematical models (stoichiometric models, regulatory networks, dynamic models), good simulation methods (e.g. steady state simulations with flux balance analysis, Boolean network simulation, numerical integration of ODEs) and robust optimization algorithms. This framework gave rise to the OptFlux platform, an open-source, user-friendly and modular software aimed at being the reference computational platform for ME applications. Indeed, the rational design of microbial strains has been limited to the developers of the techniques, since a platform that provides a user friendly interface to perform such tasks was not yet available. OptFlux aims to change this situation, by providing the following features: freely available, open-source, user-friendly, modular and compatible with standards such as the Systems Biology Markup Language (SBML) and the layout information of CellDesigner. The main methods allow the simulation of both wild-type and mutant organisms (using Flux Balance Analysis or other methods) and optimization tasks, i.e., the identification of ME targets can be performed with metaheuristics such as Evolutionary Algorithms, Simulated Anneali