Large scale complex biopolymerization networks: Modeling and analysis of system behavior and dynamics

The genetic information in the DNA is transcribed to mRNA which is further translated to proteins that form the building blocks of life. Translation or protein synthesis is thus a central cellular process in every living organism. Translation and its regulation lead to several interesting and important biological phenomena like circadian rhythms and cell cycle oscillations, and several common antibiotics work by inhibiting translation. Hence, understanding the protein synthesis machinery from a mechanistic and systems perspective is a very important problem in molecular and systems biology. With the recent advances in genomics, microarray technology, and proteomics, it is now possible to measure simultaneously the changes in the levels of every mRNA and protein species in a cell subject to an environmental and/or genetic perturbation. Mathematical modeling and computational studies are the keys to the integration and interpretation of quantitative information from such large networks.; We have developed genome-scale mechanistic models for the translation machinery, which account for all the expressed genes in a cell and all the proteins they code for. Such a global model is essential because translation is a competitive process where the mRNAs in the cell compete for other cellular components like ribosomes and transfer RNAs. However, the large size of the translation networks presented a significant computational challenge, and to this end we have developed efficient algorithmic frameworks to study the responses of translation network to perturbations.; Utilizing our frameworks, we have analyzed the translation machinery of a model organism to quantify its response to perturbation in mRNA levels and the system kinetics. An mRNA, protein feedback repressed system has been studied to identify the parameters and conditions than lead to oscillatory dynamics. High throughput data from S. cerevisiae has been used to estimate the kinetic parameters of its protein synthesis machinery and provide insights into the regulation of its protein expression. Our results have implications in design of rational protein production systems, wherein quantitative knowledge of responses of protein expression to changes in the cellular environment can be used to optimize a cellular system towards the production of a protein of interest.