Using synthetic gene circuits as research tools in evolution, development, and cancer.
- Project #1. We study by experiment and computational modeling the effect of biological noise (nongenetic cellular diversity) on survival during drug treatment and the development of drug resistance. We showed that noise can aid cell survival after exposure to stress (drug treatment). We introduced the concept of fitness noise, which arises when noisy protein levels affect cell doubling times. We are currently testing the effect of regulatory network architecture on fitness noise, and its contribution to the emergence of non-genetic and later genetic drug resistance.
- Project #2. We are designing gene constructs to control the distribution of protein levels within a cell population. This goes beyond what other technologies do (which typically control only the cell population mean). For example, we can now independently adjust the mean and noise (measured as the Coefficient of Variation) of a target gene in yeast. We have built "linearizer" gene circuits in yeast and more recently in mammalian cells that can tune the expression of a target gene linearly with inducer concentration, at minimal noise.
- Project #3. We study the responses of large-scale gene regulatory networks of infectious microbes and cancer cells to stress using public gene expression data and regulatory networks. We identify distinct sets of transcriptional subnetworks that are affected following exposure to stress. These results open the door for a systems-level understanding of the response of infectious microbes or cancer cells to stress, providing insights into their drug tolerance or drug resistance.
- Project #4. We study genetic and environmental causes of pattern formation in yeast and cancer cells by applying precisely controlled perturbations in controlled environments. We study by mathematical modeling how physical factors (strain, pressure, friction) interact with biological aspects (growth rate, cell-cell and cell-substrate attachment) to give rise to patterns.
References (since 2006 when GB started his lab):
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. 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. 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.
5. Heath AP, Kavraki L, Balázsi G, Bipolarity of the Saccharomyces Cerevisiae Genome. IEEE 2nd Intl. Conf. Bioinformatics and Biomedical Engineering, 330-333 (2008).
6. 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).
7. 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. 106(13), 5123-5128 (2009).
8. Irimia D, Balázsi G, Agrawal N, Toner M (2009), Adaptive-Control Model for Neutrophil Orientation in the Direction of Chemical Gradients. Biophys. J. 96(10), 3897-3916.
9. Veiga DFT, Dutta B, Balázsi G (2010), Network inference and network response identification: moving genome-scale data to the next level of biological discovery. Mol Biosyst. 6(3), 469-480.
10. Murphy KF, Adams R, Wang, X, Balázsi G, Collins JJ (2010), Tuning and controlling gene expression noise in synthetic gene networks. Nucleic Acids Res. 38(8), 2712-2726.
11. Balázsi G (2010), Network reconstruction reveals new links between aging and calorie restriction in yeast. HFSP Journal 4(3), 94-99.
12. Tiwari A, Balázsi G, Gennaro M, and Igoshin OA (2010), Interplay of multiple feedbacks with post-translational kinetics results in bistability of mycobacterial stress-response. Phys. Biology 7(3), 036005.
13. Nevozhay D, Adams R, Balázsi G (2011), Linearizer Gene Circuits with Negative Feedback Regulation. Methods Mol Biol. 734, 81-100.
14. Datta P, Shi L, Bibi N, Balázsi G, Gennaro ML (2011), Regulation of central metabolism genes of Mycobacterium tuberculosis by parallel feed-forward loops controlled by sigma factor E (σ(E)). J Bacteriol. 193(5), 1154-60.
15. Balázsi G, van Oudenaarden A, Collins JJ (2011), Cellular decision making and biological noise: from microbes to mammals. Cell 144(6), 910-925.
16. Stamatakis M, Adams RM, Balázsi G (2011), A common repressor pool results in indeterminacy of extrinsic noise. Chaos 21(4), 047523 (2011).
17. Quan S, Ray JC, Kwota Z, Duong T, Balázsi G, Cooper TF, Monds RD (2012). Adaptive Evolution of the Lactose Utilization Network in Experimentally Evolved Populations of Escherichia coli. PLoS Genet. 8(1), e1002444 (2012).
18. Dutta B, Pusztai L, Qi Y, André F, Lazar V, Bianchini G, Ueno N, Agarwal R, Wang B, Shiang CY, Hortobagyi GN, Mills GB, Symmans WF, Balázsi G, A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes. Br J Cancer 106(6):1107-16 (2012).
19. Nevozhay D, Adams RM, Van Itallie E, Bennett MR, Balázsi G, Mapping the environmental fitness landscape of a synthetic gene circuit. PLoS Comput. Biol. 8(4):e1002480 (2012).
20. Rohde KH, Veiga DF, Caldwell S, Balázsi G, Russell DG, Linking the Transcriptional Profiles and the Physiological States of Mycobacterium tuberculosis during an Extended Intracellular Infection. PLoS Pathog. 8(6):e1002769 (2012.)
21. Claerhout S, Dutta B, Bossuyt W, Zhang F, Nguyen-Charles C, Dennison JB, Yu Q, Yu S, Balázsi G, Lu Y, Mills GB, Abortive autophagy induces endoplasmic reticulum stress and cell death in cancer cells. PLoS One 7(6):e39400 (2012).
22. Nevozhay D, Zal T, Balázsi G, Transferring a synthetic gene circuit from yeast to mammalian cells. Nat Commun. 4:1451 (2013).
23. Lee J, Lee J, Farquhar KS, Yun J, Frankenberger CA, Bevilacqua E, Yeung K, Kim EJ, Balázsi G, Rosner MR, Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions. Proc. Natl. Acad. Sci., 111(3):E364-73 (2014).
24. Lee J, Tiwari A, Shum V, Mills GB, Mancini MA, Igoshin OA, Balázsi G, Unraveling the regulatory connections between two controllers of breast cancer cell fate. Nucleic Acids Res., 42(11):6839-49 (2014).
25. Charlebois DC, Balázsi G, Kaern M, Coherent feedforward transcriptional regulatory motifs enhance drug resistance. Phys. Rev. E. 89:052708 (2014).
26. Chen L, Noorbakhsh J, Adams RM, Samaniego-Evans J, Agollah G, Nevozhay D, Kuzdzal-Fick J, Mehta P, Balázsi G, Two-Dimensionality of Yeast Colony Expansion Accompanied by Pattern Formation. PLoS Comput. Biol. 10(12):e1003979 (2014).
27. González C, Ray JC, Manhart M, Adams RM, Nevozhay D, Morozov AV, Balázsi G, Stress-response balance drives the evolution of a network module and its host genome. Mol. Syst. Biol. 11(8):827 (2015).
More information may be found on two other websites:
1) The Laufer Center for Physical & Quantitative Biology: 
2) Biomedical Engineering Department: .
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