SGER: Biologically Inspired Computation to Understand Regulatory Gene Networks

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We seek to initiate an exploratory research program aimed at creating design tools for designing, optimizing, simulating and testing biological circuits built out of basic machineries of biology; specifically, mechanisms composed of DNA, RNA and protein interactions. These circuits are dispersed, asynchronous but are easily replicable as their basic components are created out of those elements that already occur in nature and can be synthesized, controlled and degraded using the same principles. The basic biological principles that they utilize are: polymerase-induced replication, transcription controlled by transcription activation, translation into proteins, interference by small RNA, and modulation via extra-chromosomal plasmids. Because of the underlying design principles, biological circuits created in this manner can be easily transfected into simple prokaryotic organism such as E. coli, eukaryotic organisms such as yeast as well as cell lines. We hope to gain a better understanding of the biological mechanisms of interest by studying the interaction between the designed biological circuit, whose operation is known, modeled and already studied, and the unknown function of the genes of the host organism. In this manner, the proposed research program extends the current functional genomics methods based on knock-in mutants, knock-out mutants, genes perturbed by extra-chromosomal plasmids and interference through RNAi.Thus ultimately the proposed research is aimed at understanding gene networks through computational and mathematical methods using both forward engineering and reverse engineering paradigms. For forward engineering, we directly address various topics dealing with genetic circuits - in particular, we focus on creating novel artificial circuits out of regulatory genes, RNAs, and proteins. For the reverse engineering, in the context of our other related research, we have also addressed the problem of inferring gene networks with techniques analyzing time-course data in abundance data, such as transcriptome data, but not excluding proteome, or metabolome data. Furthermore, it should be apparent that in the near future these methods could play an important role in gene therapy, genetic modification of organisms or even large-scale systems to solve challenging computational problems.The major computational challenge is the development of novel design principles for delay-independent asynchronous circuits using simple building blocks such as combinatorial logic-gates (AND, NOT, XOR, etc.), arbiter circuits (Muller C-circuit) and memory elements (FIFO queue elements).