Applications of convex optimization in metabolic network reconstruction
Mojtaba Tefagh - Assistant Professor, Sharif University of Technology
Sun, 14-Mar-2021 / 19:00 / Link:
Video Slides Poster
More than two decades ago, the first genome-scale metabolic network reconstruction of the cellular metabolism of an organism was published, shortly after the first genome was sequenced. From that time on, the ever-increasing advances in the high-throughput omics technologies have allowed for the comprehensive reconstructions of exponentially growing sizes.
However, the vast amount of data can be a two-edged sword which makes many essential tasks computationally intractable. To overcome the demands of systems biology, even while they are outpacing Moore's law, faster computational techniques are needed to enable the current methods to scale up to match the progress of data generation in a prospective manner.
In this talk, we go over several different areas of systems biology from flux balance analysis to context-specific reconstruction and propose efficient computational methods for several tasks separately. Then we work towards a more holistic approach and discuss the idea of generalizing the multiple-measurement vectors problem from linear systems to linear programs, which is the problem of !Optimizer 2021. We will conclude by elaborating on how this approach can be immediately applied to a well-known computationally-heavy problem in metabolic network reconstruction.
Since joining Stanford University, M. Tefagh has been involved with studies related to applications of convex optimization in systems biology. In 2019, M. Tefagh wrote his dissertation on "applications of convex optimization in metabolic network analysis" under the supervision of Professor Stephen P. Boyd. Since 2020, M. Tefagh has been working as an assistant professor at Sharif University of Technology and as a lead researcher at Sharif Optimization and Applications Laboratory.