Collaborative Research: Integrating Multiple Analyses to Understand Gene Regulatory Networks

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Gene regulatory networks play a vital role in nearly every process of life as they are responsible for receiving environmental stimuli and translating these into activity inside the cell. In this project, the PIs will develop new strategies for computer-based modeling, and hence understanding and predicting, behavior of complex gene regulatory networks. The computer programs (algorithms) that will be developed represent a novel strategy integrating scientific discovery with computational innovation. Although the algorithm is being used to explore the gene regulatory networks of a fungal cell's response to stress it should lead to principles applicable to other biological systems. The model will be validated by comparing model predictions with data collected from experiments and this data will then be used to further refine the model. This project also includes initiatives to advance undergraduate education by employing an interdisciplinary problem-based learning approach that will be comprised of multi-state teams that also involve graduate students. The goal of this project is to develop a new approach for modeling gene regulatory networks. The PIs will test the hypothesis that initial experimental characterization of a network subset will permit identification of the biomolecular constituents and their connectivity, thus establishing network topology. System wide time-course measurements can then be used to refine this network into a reaction kinetic model capable of making accurate system predictions. The cell wall integrity signaling pathway of the experimentally tractable model fungus Aspergillus nidulans will serve as a model. This pathway responds to cell wall damage by activating repair mechanisms that restore cell integrity. Because protein kinases play a pivotal role in mediating cellular regulatory activities, the PIs will focus on a subset of kinases and the discovery of their associated substrates to initially assemble a rudimentary network. Subsequently the system will be experimentally perturbed for measuring its dynamic response using a robust transcriptomic, proteomic and phosphoproteomic platform. Using this data, the PIs will take a two-step approach to developing the dynamic system of coupled ordinary differential equations able to describe dynamic behavior of a model gene regulatory network. First, an ensemble approach of approximate models will be tested and refined. In the second step, the ensemble will act as the seed population for use in an evolutionary algorithm to generate a more refined and accurate model. The PIs will then validate the model by iterative comparisons of in silico predictions with experimental results.This award is co-funded by programs in Systems and Synthetic Biology (Directorate for Biological Sciences) and Biotechnology and Biochemical Bioengineering (Directorate for Engineering).