Genome-wide Bypass Network of Essential Gene Mutation
Animesh Ray, in collaboration with the University of Rochester Medical School and the University of Toronto
A central goal of drug development for cancer therapy is maximizing the lethal effects of drugs on diseased cells, while minimizing the deleterious effects of the same drugs on normal cells. Genetic networks which underlie these processes have evolved robustness to perturbation through redundancy of genes, gene functions, and alternative rewiring of gene networks, and may networks engage genes into promiscuous functions for survival under selection pressure. To begin to understand the range of evolutionary robustness built into the genome, we for the first time have systematically screened genomic open reading frames for their ability to suppress when induced for high-level expression the lethal effects of conditional alleles of over 108 essential genes, and discovered 847 dosage suppressors of 56 gene mutants. A total of 700 essential gene mutant nodes and their suppressors, with 847 directed edges, constitute a dosage suppressor network with one large connected component containing 689 nodes. Integration of this dosage suppressor network with a comprehensive protein-protein interaction network, and with a version of the global gene regulatory network obtained from expression level correlation and transcription factor binding data, illuminate modular organization of genes and their products, and implicate bypass pathways of essential genes.
Genes That Induce Chromosome Instability by Gain-of-function:
Chromosome instability is a hallmark of cancer. We are conducting a medium throughput screen for genes that cause high frequency chromosome instability when these genes are over-expressed. Previous studies had identified approximately 50 such genes based on the screening of cDNA libraries derived from cells growing in enriched media, thus failing screen at least a third of the genome if not more. Our screen uses a genome-derived Open Reading Frame library, where (nearly) every gene in the genome is under control of a galactose inducible promoter. From this screen at least 24 additional (novel) genes have been confirmed. We expect to confirm at least double that number when we complete the screen. We anticipate that by using this information, it would be possible to identify human homologues, some of which might cause chromosome instability in cancer cells.
Evolutionary Games and Genome Evolution:
Animesh Ray, Chris Adami, Arend Hintze
We are addressing whether evolutionary robustness through genetic bypass systems can be understood through the abstract model of Stochastic Evolutionary Game Theory (SEGT). SEGT is a mathematical formalism that explores how competing individuals, who have various properties that are stochastically determined, interact over generations to produce evolved systems. To that end, we are using a hybrid of experimental and theoretical approaches. Experimental approaches include (a) allowing strains containing specific lethal mutations to evolve survival strategies, identifying these strategies and comparing the evolved strategies with those already identified as possible through directed genetic approaches; (b) establishing an experimental system of a theoretically well-studied stochastic evolutionary game model (stochastic version of the “hawk-dove” game) through synthetic biology approaches using the so called “killer toxin” system of yeast.
Mathematical and Computational Modeling in Systems Biology
Animesh Ray, Ali Nadim
We apply methods from the theory of nonlinear dynamical systems to model the interactions of genes and proteins within a cell. Both deterministic and stochastic methods are used to examine models that arise based on biological knowledge of the complex feedback systems within such networks.
Structure and Origin of Functional Modularity in Cellular Biology
Animesh Ray, Arend Hintze, Chris Adami
Biological function is the complex consequence of the action of a large number of molecules that interact in many different ways. Elucidating the contribution of each molecule to a particular function would seem hopeless, had evolution not shaped the interaction of molecules in such a way that they form functional units, or building blocks, of the organisms function. These building blocks can be called modules, whose interactions, interconnections, and fault-tolerance can be investigated from a higher-level point of view, allowing for a synthetic view of biological systems. We are taking an integrated computational and experimental approach to determine the extent of modularity in the gene-interaction network, the properties of modules, their contribution to robustness and fault-tolerance, their origin and evolution, and how existing and emerging criteria of module definition and function affect the ability to predict biological function.