Dr. Vassily Hatzimanikatis is currently Associate Professor of Chemical Engineering and Bioengineering at Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland. Vassily received a PhD and a MS in Chemical Engineering from the California Institute of Technology, and his Diploma in Chemical Engineering from the University of Patras, in Greece.  After the completion of his doctoral studies, he held a research group leader position at the Swiss Federal Institute of Technology in Zurich (ETHZ), Switzerland.  Prior to joining EPFL, Vassily worked for three years in DuPont, Cargill, and Cargill Dow, and he has been assistant professor at Northwestern University, at Illinois, USA.

Vassily’s research interests are in the areas of computational systems biology, biotechnology, and complexity.  He is associate editor of the journals Biotechnology & Bioengineering and Metabolic Engineering, and he serves on the editorial advisory board of the journals Bioprocess and Biosystems Engineering, Journal of Chemical Technology and Biotechnology, and Industrial Biotechnology. He has written over 50 technical publications and he is co-inventor in three patents and patent applications.

Vassily is a fellow of the American Institute for Medical and Biological Engineering, he was a DuPont Young Professor, and he has also received the Jay Bailey Young Investigator Award in Metabolic Engineering.

Abstract

The Process Systems Engineering of Cellular Processes

Vassily Hatzimanikatis,* Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland and Liqing Wang, Northwestern University, Evanston, IL 60208

Current knowledge of biological systems is composed by a vast set of data that accumulate at an increasing rate. Advances in analytical methods and development of sophisticated techniques and instrumentation have provided the tools that allow us to know more than what we can understand. However, it is well understood that living organisms are characterized by high complexity. This complexity increases from unicellular organisms to isolated tissue cells and multicellular structures, such as tissues and organs. The development of tools and frameworks that will organize the available biological knowledge and will help in the analysis, understanding, and redesign of biological systems is of immediate importance. Systems engineers, faced with the same problem since the development of the field, have been successful in employing mathematical and computational methods for the development of new products and improved processes.

We discuss a framework that addresses the problem of choosing targets for improving the performance of industrial biocatalysts. This is a very difficult problem due to the complex reaction networks that determine the performance of an organism and the incomplete information about these networks. Borrowing concepts and methods from control theory and uncertainty analysis, we have developed decision-making methods tailored to biochemical reaction networks.

Keywords

Metabolic engineering, (log)linear model, metabolic control analysis, central carbon metabolism.