COBRA Optimization Methods for Analysis of Genome-scale Models and in silico Strain Design
Payam Setoodeh - Assistant Professor, Shiraz University
Sun, 12-Sep-2021 / 11:00 / Link:
Video Slides Poster


Genome-scale metabolic network reconstructions (GENREs) that are created based on genomic and bibliomic data, signify the genotype-phenotype relationships of the organisms of interest. They have been widely used to study the physiological properties of organisms and systems-based analyses of metabolism. Genome-scale models (GEMs) as operative mathematical representations of GENREs are crucial implements in systems biology. These constraint-based models (CBMs) have been successfully employed to systematically analyze the metabolism in a large number of organisms. They are consequently applicable for improvements in cell functionalities, design of new strains with desired biological capabilities (such as biosynthesis of targeted compounds), determination of optimal engineering strategies based on whole-cell activities, and genome-minimizing. Integrated computational approaches, which employ in silico analyses, play vital roles in systems biology and systems metabolic engineering. The biased COBRA (COnstraint-Based Reconstruction and Analysis) methods are a main class of computational techniques in which constraint-based optimization problems are defined and applied in order to take advantage of the synergistic effects of a variety of basic elements (such as genes, gene-products, metabolites) into consideration to evaluate the cells' potentials and make model-driven discoveries. Accordingly, a number of combinatorial, single-level and bi-level optimization approaches have been developed and widely applied (such as FBA and FVA, OptKnock, OptGene, GDLS, OptReg and OptForce). These techniques are categorized in two main groups: top-down and bottom-up. The current presentation aims to introduce some of these suitable algorithms emphasizing the importance of constraint-based optimization in the field of systems biology and in silico strain design.


Payam Setoodeh received the BSc, M.Sc. and Ph.D. degrees in Chemical Engineering from Shiraz University, Shiraz, Iran. He is currently an Assistant Professor with the School of Chemical, Petroleum and Gas Engineering, Shiraz University. His research interests include modeling, simulation and optimization of chemical processes and bioprocesses, systems biology and systems metabolic engineering as well as green processes and environmental biotechnology. He is currently interested and involved in a couple of related multidisciplinary and transdisciplinary research projects.