Experimental Design for inferring model structure and parameters from flux data

Experimental Design for inferring model structure and parameters from flux data

Partners: MIT (Bruce Tidor)
MIT-Portugal program

Abstract

This project aims are to develop and apply a set of mathematical and computational techniques to the problem of designing efficient and informative experiments for the identification of kinetic models representing metabolic reactions. For that purpose, a case-study microorganism will be selected for which sufficient information is available to perform the validation of the proposed methodologies and for which a dynamic model of the central carbon metabolism is being re-constructed by the BioPSE group.

The kinetic models obtained using the methodologies proposed will allow a better understanding and representation of the dynamic behavior of microorganisms with industrial interest, therefore bringing major benefits to the field of systems biology. Systems biology is expected to bring major benefits to industrial biotechnology especially in the development of efficient cell factories, by speeding up the development process, and ensuring that new products can be brought to the market faster or that there can be a faster improvement of existing bioprocesses.

Therefore, the ultimate aims of this project are associated with aiding efforts to the optimization of industrial biotechnology processes like the production of bulk chemicals like succinate or lactate, biofuels like bioethanol and specialty chemicals like vitamins and antibiotics.