Networks have been widely used to describe many biological processes. Understanding the structure of biological network, especially regulatory network, will provide a key to discovering the mechanisms underlying important biological processes and pathogenesis of diseases. One of the most challenging tasks in systems biology is how to correctly reconstruct the networks from the high-dimensional data generated by modern genomic technology. Most network inference methods assume the network structure is time-invariant. Some recent studies revealed the structures of some biological networks are non-stationary or time-varying. For example, the neural information flow networks of brains are changing during learning process. Importantly, cancer studies found the native T cells would be converted into senescent T cells due to the structure changes of genetic network during tumorigenesis. The stationary network inference methods can't be used to reconstruct the time-varying network. Non-stationary network inference methods are urgently needed to investigate the time-varying networks at different stages. Some researchers have attempted to develop some time-varying network inference methods. However, the inferred networks using existing methods are only correlation or causality graphs, not regulatory networks which require activation & inhibition information. This project aims to develop novel non-stationary network inference methods to reconstruct time-varying regulatory networks from time series data. Since the networks are highly complex, it is not realistic to manually verify large networks as being used by the traditional methods. We will develop a powerful Model Checker, which is a Turing Award winning technique for hardware system verification, to intelligently verify the inferred time-varying networks. Our long-term goal is to integrate the statistical inference and model checking techniques in a unified platform to automatically reconstruct and verify time-varying networks. This integrative systems biology approach will make the large-network inference and verification automatic, intelligent and efficient. Recent cancer studies show that, restoring senescent T cells represents a promising strategy for cancer treatment. In collaboration with cancer immunologist, we will apply computational-experimental approaches to investigate what structure changes of the genetic network and how they induce T-cell's functional changes and influence its fate decision making from naive T-cells to senescent T-cells. Answering these questions will significantly improve our understanding of the mechanisms underlying the T cell differentiation during tumorigenesis.

Public Health Relevance

This project aims to develop a novel systems biology approach to reconstruct the time-varying biological networks from high-dimensional data in collaboration with the cancer immunologist. The proposed research has relevance to public health, because it seeks to investigate what and how the structure changes of genetic network induce the T-cell's functional changes during tumorigenesis, which will ultimately improve our understanding of the mechanisms underlying the T cell differentiation and cancer.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Academic Research Enhancement Awards (AREA) (R15)
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Special Emphasis Panel (ZRG1)
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Resat, Haluk
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Saint Louis University
Biostatistics & Other Math Sci
Schools of Arts and Sciences
Saint Louis
United States
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