Predictive modeling of chemical-perturbed regulatory networks in systems toxicology
Proper regulation of cell signaling is a fundamental aspect of correct biological function, with dysfunction lying at the core of a majority of human disease. This signaling relies on a highly interconnected molecular network for the transmission of information from the cell exterior to the interior, during which this information is converted into one or more cellular responses. At the scale of individual genes and proteins, these networks have been the focus of extensive experimental work for decades. What is still not well understood, however, is how various and often distant parts of the network work together in the generation of specific outputs including the generation of specific phenotypes. This is in part due to the significant complexity that exists at each level between network input signal and eventual output response. Parallel processing of multiple extracellular signals, excitatory or inhibitory cross-talk between pathways, cooperativity, and the particular state of the network (e.g. disease state, mutation, chemical-induced damage, etc.) are all complicating factors that must be recognized when trying to understand network-derived function and dysfunction.
Of primary interest to Project 1 is resolving how these networks are altered, either permanently or transiently, in response to chemical exposure. Such alterations may occur at small scales where individual genes have been damaged up to larger scales where it is the dynamic balance of multiple elements of gene-regulatory and signaling networks that has been disrupted, in turn leading to altered behavior and disease. The objectives of Project 1 are focused on the development of novel computational methods and tools for the reverse engineering of cell regulatory and signaling networks. A major priority of this work is developing the ability to predict the ramifications of chemical exposure on biological function. Specifically, in this project we will develop and apply computational approaches to the reconstruction and high-level modeling of regulatory networks and their response to chemical perturbation. In addition, we will pursue the development of relevant mechanistic models with particular focus on lipid metabolism and nuclear receptor function. Finally, we will integrate and make accessible the models and associated software tools developed through this work.
This project pursues the following major specific objectives:
- Develop and apply data-driven methods for the inference and high-level modeling of regulatory network response to chemical perturbation
- Develop mechanistic models of nuclear receptor function
- Integrate and deploy high- (Specific Objective 1) and low-level (Specific Objective 2) modeling tools