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Optimization Challenges in Protein and Metabolic Engineering

In this talk, we will discuss the application of optimization in protein and metabolic engineering. In the first part, the problem of protein design will be expressed and solved as an integer optimization problem. The set of discrete design choices in protein design entails the identification of what residue to place at each site for the protein to assume the targeted geometry/structure. The minimization of the potential energy function along with structural restrains drives the structure identification. In the second part of the talk we will present how bilevel optimization can be used to guide genetic manipulations in microbial production systems. Specifically, the inner problem maximizes a cellular fitness/objective function to identify the distribution of fluxes in the metabolic network whereas the outer problem pinpoints which reactions/genes to eliminate to maximize the production of the targeted compound. Computational predictions and experimental verifications will be described.
Type of Seminar:
Public Seminar
Prof. Costas Maranas
Dept Chemical Engr., 112A Fenske Lab., University Park, Pennsylvania State University, Pittsburg PA 16802, USA
Nov 22, 2004   16:15

University of Zurich, Room K02-F-172
Contact Person:

Prof. M. Morari
No downloadable files available.
Biographical Sketch:
Costas D. Maranas (b. 1967), Professor, Department of Chemical Engineering, The Pennsylvania State University, BS, Chemical Engineering, Aristotle University, Greeece, (1990); MA, Chemical Engineering, Princeton University (1992); Ph.D. in Chemical Engineering, Princeton University (1995); Allan P. Colburn Award for Excellence in Publication (2002), Web Editor of Metabolic Engineering, NSF Panel Metabolic Engineering (2002), DOE Genomes to Life Panel (2002), NSF BES Career Panel (2001); Research interests: Modeling and optimization of directed evolution protocols for protein engineering, analysis and optimization of metabolic networks and biopathways, inference of gene regulatory networks, signaling networks, real options based optimization of product and R&D pipelines, optimization theory and algorithms.