CHIP:Research

Research Interests (See also the GSBS website for G. Balázsi: [1]):

Mathematical/computational modeling and experimental characterization of biomolecular interaction networks to unravel molecular mechanisms underlying cellular survival in 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 are designing gene constructs to shape the distribution of protein levels within a cell population. For example, we can now independently adjust the mean and noise (Coefficient of Variation) of a target gene in yeast. We have also built a "linearizer" gene circuit that converts a nonlinear (sigmoidal) dose response to linear.

• 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 microbes 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 systems-level understanding of the response of infectious microbes to stress, as well as their drug tolerance or drug resistance.

• Project #5. We analyze and interpret the large-scale proteomics/drug screening/siRNA data collected at our department in the Gordon Mills laboratory. We are inferring signaling networks based on experimental data, and study their overlap with known interaction networks.

References:

1. Blake WJ, Balázsi G, Kohanski MA, Isaacs FJ, Murphy KF, Kuang Y, Cantor CR, Walt DR, Collins JJ. Phenotypic consequences of promoter-mediated transcriptional noise. Mol. Cell 24(6):853-865 (2006).

2. Murphy, KF, Balázsi G, Collins JJ. Combinatorial promoter design for engineering noisy gene expression. Proc. Nat. Acad. Sci., USA. 104(31):12726-12731 (2007).

3. Balázsi G, Collins JJ. Sensing Your Surroundings: Taking the inventory inside single cells. News and Views. Nature Chemical Biology 3(3):141-142 (2007).

4. Strickler JR, Balázsi G. Planktonic copepods reacting selectively to disturbances. Phil. Trans. R. Soc. B. (2007)

5. Balázsi G, Oltvai ZN. A Pitfall in Series of Microarrays: The Position of Probes Affects the Cross Correlation of Gene Expression Profiles. In: Korenberg MJ, editor, Microarray Data Analysis: Methods and Applications (Humana Press, 2007). Also: Methods Mol Biol. 2007; 377:153-62. Review.

6. Balázsi G. Statistical evaluation of genetic footprinting data. In: Osterman A, Gerdes SY, editors, Gene Essentiality at Genome Scale: Protocols and Bioinformatics (Humana Press, 2007). Also: Methods Mol Biol. 2008; 416:355-9.

7. Ernst J, Beg QK, Kay KA, Balázsi G, Oltvai ZN, Bar-Joseph Z. A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli. PLoS Comput Biol. 2008 Mar 28; 4(3):e1000044.

8. Heath AP, Kavraki L, Balázsi G, Bipolarity of the Saccharomyces Cerevisiae Genome. IEEE 2nd Intl. Conf. Bioinformatics and Biomedical Engineering, 330-333 (2008).

9. Balázsi G, Heath A, Shi L, Gennaro ML (2008). The temporal response of the Mycobacterium tuberculosis gene regulatory network during growth arrest. Mol. Systems Biol. 4:225 (2008).

10. Nevozhay D, Adams R, Murphy K, Josic K, Balázsi G (2009). Negative autoregulation linearizes the dose response and suppresses the heterogeneity of gene expression. Proc. Nat. Acad. Sci., USA. (2009). Accepted for publication.