Gábor Balázsi, Ph.D. 2001, University of Missouri UT M. D. Anderson Cancer Center Systems Biology Contact Information

Research Interests: Mathematical/computational modeling and experimental characterization of biomolecular interaction networks

The focus in our laboratory is on the system-level understanding of the molecular mechanisms underlying cellular response to stress.

Project #1. We study by experiment and computational modeling the combined effect of noise and feedback regulation on the development of drug resistance. Our earlier studies proved that noise can aid survival after a single exposure to stress. The current project will test the effect of feedback regulation on the development and maintenance of non-genetic drug resistance. We will apply multiple exposures to stress, testing how a cell population benefits from the "memory" of earlier stress events due to positive autoregulation.

Project #2. We study the pre-apoptotic signaling events responsible for chemotherapeutic drug resistance in cancer cell lines. In particular, we are focusing on the c-Jun N-terminal Kinase (JNK) MAP kinase and a positive feedback loop involving one of its upstream regulators. We are mapping the signaling network around this feedback loop, and developing mathematical models to determine its dynamics in response to drug. In parallel, we monitor the activity of key kinases by Western blotting. We hope to identify novel pre-apoptotic mechanisms and signaling interactions with the potential to discover new biomarkers and drug targets.

Project #3. We aim to identify the network topology around stress-related genes within large-scale gene regulatory networks of three organisms: E. coli, S. cerevisiae and H. sapiens. We have discovered a distinct pattern of positioning and regulation of stress-related genes that is similar across the kingdoms of life, suggesting that it emerged due to similar evolutionary driving forces acting on all forms of life.

Project #4. We study the response of the large-scale gene regulatory networks of infectious bacteria to stress using published microarray data. We identify distinct sets of transcriptional subnetworks (origons) that are affected at various times following exposure to stress. These results open the door for a system-level understanding of the response of infectious microbes to stress, as well as their drug tolerance or drug resistance.

Project #5. We are involved in the analysis and interpretation of the large-scale proteomics/drug screening/siRNA data collected at our department in the Gordon Mills laboratory.

__________________________
Balázsi G, Collins JJ (2007) Taking the inventory inside single cells. Nat Chem Biol. 3:141-142.

Blake WJ, Balázsi G, Kohanski MA, Isaacs FJ, Murphy KF, Kuang Y, Cantor CR, Walt DR, Collins JJ (2006) Phenotypic consequences of promoter-mediated transcriptional noise. Mol Cell 24:853-865.

Balázsi G, Barabási AL, Oltvai ZN (2005) Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. Proc Natl. Acad. Sci. U. S. A. 102:7841-7846.