Computational Approaches for Defining the Regulation of Drosophila Circadian Rhythmicity and Predicting the Escherichia coli Proteome
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The ever increasing ability to collect large biological datasets can quickly make analysis by traditional conceptual models intractable. Computational biology, as part of the systems biology paradigm, attempts to exploit this opportunity by providing novel hypotheses and proposing future experimentation. Here we present two computational biology efforts complementing the experimental studies of Drosophila circadian rhythmicity and the cell-wide regulation of protein synthesis in Escherichia coli. To investigate open questions of circadian regulation and communication, we developed a detailed mathematical model of Drosophila circadian rhythms. Using this model we investigate both the regulation of the critical PERIOD-TIMELESS negative feedback loop and the possible communication underlying the adaptation of locomotor activity to seasonal changes in day length. The model results suggest novel interactions to be tested experimentally, advancing our understanding and control of these biological rhythms. In the second part of our effort, we extended our previously developed model of protein synthesis to predict the genome-wide proteome from transcript expression data. The model results are consistent with the measured transcriptomic and proteomic changes of E. coli over-expressing rhsA. Data not well-fit by the model are consistent with post-transcriptional regulation resulting from rhsA over-expression. From another perspective, the spread in the experimental data is not well-described by the model results suggesting regulation based solely on codon bias may be insufficient to describe the observed global changes in protein synthesis. Proposing a mechanism underlying the complex and nonlinear protein-mRNA relationship, the model results show significant predictive capabilities while suggesting a path to future model development. The combined results of both efforts show the potential impact of using mathematics and computers to solve complex biological problems.