Speaker: Michael Martin
Date: January, 16 2015
Location: NS 112
Computer models of life forms are quantitatively predictive of metabolic parameters throughout an organism’s entire life cycle . While to date, whole cell models mostly include rudimentary bacteria with very small genomes (525 genes), the field is rapidly moving to more sophisticated eukaryotic systems (20,000 genes in the human genome). Eventually models of human cells will help to elucidate cancer mechanisms, differentiation of stem cells into somatic cells, and the process of aging. The gaps in our mechanistic understanding of the molecular interactions within cells are being filled by higher fidelity computational cell models.
In this presentation, the basics of computational cell biology are presented along with a summary of key physical processes within the cell. Of relevance is the fact that protein-protein and enzyme-substrate interactions may be mathematically represented via the “Law of Mass Action” to construct sets of linear differential equations that describe reaction rates and formation of protein complexes. Using mass action methods, examples of reaction networks are presented within the Virtual Cell simulation package.
The proposed thesis topic focuses on computational modeling efforts to capture the epigenetic regulation of an ancient (typically dormant) retro-viral genetic element, named LINE-1 (long interspersed nuclear element 1). This remnant of viral infection has managed to survive within the human genome and copied itself thousands of times to now represent 17% of human DNA – part of what was once labelled junk DNA. When activated, the proteins encoded by LINE-1 function to produce copies of itself (along with other similar genetic elements) that can then be reinserted into the genome. Thus, activation of LINE-1 is associated with genomic instability and tumorigenesis. Initial results are presented for simulation of the retinoblastoma pathway and its association with the activation of LINE-1.
1. Jonathan R. Karr, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival, Nacyra Assad-Garcia, John I. Glass, Markus W. Covert. A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell, 2012; 150 (2): 389 DOI: 10.1016/j.cell.2012.05.044