Difference between revisions of "CHIP:Research"

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<big>'''Research Interests'''</big>
 
<big>'''Research Interests'''</big>
  
 +
<font color="green">
 +
<big>'''"Using synthetic gene circuits as novel research tools to control cells and study cellular evolution, development, and cancer."'''</big>
 +
<font color="black">
  
'''Mathematical/computational modeling and experimental characterization of biomolecular interaction networks, --- to unravel molecular mechanisms underlying cellular survival and evolution in stress.'''
 
  
 +
*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 #1. We study by experiment and computational modeling the effect of noise mediated by regulatory network architecture on survival during drug treatment and the development of drug resistance. Our earlier studies proved that noise can aid survival after a single exposure to stress. We introduced the concept of fitness noise, which is unavoidable when noisy protein levels affect cell doubling times. We are currently testing the effect of regulatory network architecture on fitness noise, which contributes 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 #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 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 #3. We study the response of the large-scale gene regulatory networks of infectious microbes and cancer cells to stress using published microarray data. 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.
  
*Project #4. 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.
+
<br>
  
 +
[[Image:Balazsi_Lab.jpg‎|500px|Photo with Daniel and Tamás]]
  
'''References:'''<br>
+
<br>
  
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).<br><br>
+
'''References since 2006 when GB established his lab -<br>
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).<br><br>
+
<font color="red">(* indicates a "core" paper recommended for anyone interested in joining the lab): <br><br><br>
 +
<font color="black">
 +
 
 +
'''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). [http://www.sciencedirect.com/science/article/pii/S1097276506007441] <br><br>
 +
2. Murphy, KF, Balázsi G, Collins JJ. ''Combinatorial promoter design for engineering noisy gene expression.'' Proc. Nat. Acad. Sci. 104(31):12726-12731 (2007).<br><br>
 
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).<br><br>
 
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).<br><br>
 
4. Strickler JR, Balázsi G. ''Planktonic copepods reacting selectively to disturbances.'' Phil. Trans. R. Soc. B. (2007)<br><br>
 
4. Strickler JR, Balázsi G. ''Planktonic copepods reacting selectively to disturbances.'' Phil. Trans. R. Soc. B. (2007)<br><br>
Line 23: Line 31:
 
5. Heath AP, Kavraki L, Balázsi G, ''Bipolarity of the Saccharomyces Cerevisiae Genome.'' IEEE 2nd Intl. Conf. Bioinformatics and Biomedical Engineering, 330-333 (2008).<br><br>
 
5. Heath AP, Kavraki L, Balázsi G, ''Bipolarity of the Saccharomyces Cerevisiae Genome.'' IEEE 2nd Intl. Conf. Bioinformatics and Biomedical Engineering, 330-333 (2008).<br><br>
 
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). <br><br>
 
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). <br><br>
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., USA. 106(13), 5123-5128 (2009). <br><br>
+
'''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). [http://www.pnas.org/content/106/13/5123.long] <br><br>
 
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. <br><br>
 
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. <br><br>
 
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. <br><br>
 
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. <br><br>
Line 31: Line 39:
 
13. Nevozhay D, Adams R, Balázsi G (2011), Linearizer Gene Circuits with Negative Feedback Regulation. Methods Mol Biol. 734, 81-100. <br><br>
 
13. Nevozhay D, Adams R, Balázsi G (2011), Linearizer Gene Circuits with Negative Feedback Regulation. Methods Mol Biol. 734, 81-100. <br><br>
 
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. <br><br>
 
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. <br><br>
15. Balázsi G, van Oudenaarden A, Collins JJ (2011), Cellular decision making and biological noise: from microbes to mammals. Cell 144(6), 910-925. <br><br>
+
'''15*. Balázsi G, van Oudenaarden A, Collins JJ (2011), Cellular decision making and biological noise: from microbes to mammals. Cell 144(6), 910-925. [http://www.sciencedirect.com/science/article/pii/S0092867411000699] <br><br>
 
16. Stamatakis M, Adams RM, Balázsi G (2011), A common repressor pool results in indeterminacy of extrinsic noise. Chaos 21(4), 047523 (2011). <br><br>
 
16. Stamatakis M, Adams RM, Balázsi G (2011), A common repressor pool results in indeterminacy of extrinsic noise. Chaos 21(4), 047523 (2011). <br><br>
 
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). <br><br>
 
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). <br><br>
 
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). <br><br>
 
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). <br><br>
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). <br><br>
+
'''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). [http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002480] <br><br>
 
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.)<br><br>
 
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.)<br><br>
 
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).<br><br>
 
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).<br><br>
22. Nevozhay D, Zal T, Balázsi G, Transferring a synthetic gene circuit from yeast to mammalian cells. Nat Commun. 4:1451 (2013).<br><br>
+
22*. Nevozhay D, Zal T, Balázsi G, Transferring a synthetic gene circuit from yeast to mammalian cells. Nat Commun. 4:1451 (2013). [https://www.nature.com/articles/ncomms2471] <br><br>
 +
'''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). [http://www.pnas.org/content/111/3/E364.long] <br><br>
 +
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).<br><br>
 +
25. Charlebois DC, Balázsi G, Kaern M, Coherent feedforward transcriptional regulatory motifs enhance drug resistance. Phys. Rev. E. 89:052708 (2014). [http://journals.aps.org/pre/pdf/10.1103/PhysRevE.89.052708]<br><br>
 +
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). [http://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1003979.PDF]<br><br>
 +
'''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). [http://msb.embopress.org/content/msb/11/8/827.full.pdf]<br><br>
 +
28. Belete MK, Balázsi G, Optimality and adaptation of phenotypically switching cells in fluctuating environments. Phys. Rev. E 92:062716 (2015). [http://journals.aps.org/pre/pdf/10.1103/PhysRevE.92.062716]<br><br>
 +
29. Ray JC, Wickersheim ML, Jalihal AP, Adeshina YO, Cooper TF, Balázsi G, Cellular Growth Arrest and Persistence from Enzyme Saturation. PLoS Comput. Biol. 12(3):e1004825 (2016). [http://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1004825.PDF]<br><br>
 +
30. Chauhan R, Ravi J, Datta P, Chen T, Schnappinger D, Bassler KE, Balázsi G, Gennaro ML, Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis. Nat. Commun. 7:11062 (2016). [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821874/pdf/ncomms11062.pdf]<br><br>
 +
31. Diao J, Charlebois DA, Nevozhay D, Bódi Z, Pál C, Balázsi G, Efflux Pump Control Alters Synthetic Gene Circuit Function. ACS Synth. Biol. 5(7):619-631 (2016) [http://pubs.acs.org/doi/ipdf/10.1021/acssynbio.5b00154].<br><br>
 +
32. Bien H, Balázsi G, Book review on "Biomolecular Feedback Systems" by Del Vecchio and Murray, Quart. Rev. Biol. 91(2), 220-221 (2016).<br><br>
 +
33. Charlebois DA, Balázsi G, Frequency-dependent selection: a diversifying force in microbial populations, Mol. Syst. Biol. 12(8):880 (2016) [http://msb.embopress.org/content/msb/12/8/880.full.pdf].<br><br>
 +
34. Shao Q, Trinh JT, McIntosh CS, Christenson B, Balázsi G, Zeng L, Lysis-lysogeny coexistence: prophage integration during lytic development, MicrobiologyOpen, (2016) [http://onlinelibrary.wiley.com/doi/10.1002/mbo3.395/epdf].<br><br>
 +
35. Trinh J, Székely T, Shao Q, Balázsi G, Zeng L, Cell Fate Decisions Emerge as Phages Cooperate or Compete Inside their Host. Nat. Commun. 8:14341 (2017). [https://www.nature.com/articles/ncomms14341.pdf] <br><br>
 +
36. Bódi Z, Farkas Z, Nevozhay D, Kalapis D, Lázár V, Csörgő B, Nyerges Á, Szamecz B, Fekete G, Papp B, Oliveira JL, Moura G, Santos MAS, Székely T Jr., Balázsi G, Pál Cs. Phenotypic heterogeneity promotes adaptive evolution PloS Biol. 15(5):e2000644 (2017). [http://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.2000644&type=printable]<br><br>
 +
37. Bouklas T, Alonso-Crisóstomo L, Székely T Jr., Diago-Navarro E, Orner EP, Smith K, Munshi MA, Del Poeta M, Balázsi G, Fries BC. Generational distribution of a Candida glabrata population: Resilient old cells prevail, while younger cells dominate in the vulnerable host. PloS Path. 13(5):e1006355 (2017). [http://journals.plos.org/plospathogens/article/file?id=10.1371/journal.ppat.1006355&type=printable]<br><br>
 +
38. Cortes M, Trinh J, Zeng L, Balázsi G. Late-arriving signals contribute less to cell fate decisions. Biophys. J. In Press (2017).<br><br>
 +
<br>
 +
 
 +
[[Image:Balazsi_WikiImage1.jpg‎|500px|Research cartoon]]
 +
 
 +
<br>
  
 
'''More information may be found on two other websites:'''
 
'''More information may be found on two other websites:'''
  
1) The [http://gsbs.uth.tmc.edu/ GSBS] website: [http://www.uthouston.edu/gsbs/faculty/faculty-directory/faculty-profiles.htm?id=1346902]
+
1) The Louis & Beatrice Laufer Center for Physical & Quantitative Biology: [http://laufercenter.stonybrook.edu/people/faculty]
  
2) The Systems Biology website: [http://faculty.mdanderson.org/G%C3%A1bor_Bal%C3%A1zsi/Default.asp]. <br><br>
+
2) Biomedical Engineering Department: [http://www.bme.stonybrook.edu/people/faculty/fac_core.html]. <br><br>
  
 
'''Back to the main page:''' [[CHIP]]
 
'''Back to the main page:''' [[CHIP]]

Latest revision as of 11:15, 29 September 2017

Research Interests

"Using synthetic gene circuits as novel research tools to control cells and study cellular 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.


Photo with Daniel and Tamás


References since 2006 when GB established his lab -
(* indicates a "core" paper recommended for anyone interested in joining the 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). [1]

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). [2]

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. [3]

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). [4]

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). [5]

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). [6]

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). [7]

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). [8]

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). [9]

28. Belete MK, Balázsi G, Optimality and adaptation of phenotypically switching cells in fluctuating environments. Phys. Rev. E 92:062716 (2015). [10]

29. Ray JC, Wickersheim ML, Jalihal AP, Adeshina YO, Cooper TF, Balázsi G, Cellular Growth Arrest and Persistence from Enzyme Saturation. PLoS Comput. Biol. 12(3):e1004825 (2016). [11]

30. Chauhan R, Ravi J, Datta P, Chen T, Schnappinger D, Bassler KE, Balázsi G, Gennaro ML, Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis. Nat. Commun. 7:11062 (2016). [12]

31. Diao J, Charlebois DA, Nevozhay D, Bódi Z, Pál C, Balázsi G, Efflux Pump Control Alters Synthetic Gene Circuit Function. ACS Synth. Biol. 5(7):619-631 (2016) [13].

32. Bien H, Balázsi G, Book review on "Biomolecular Feedback Systems" by Del Vecchio and Murray, Quart. Rev. Biol. 91(2), 220-221 (2016).

33. Charlebois DA, Balázsi G, Frequency-dependent selection: a diversifying force in microbial populations, Mol. Syst. Biol. 12(8):880 (2016) [14].

34. Shao Q, Trinh JT, McIntosh CS, Christenson B, Balázsi G, Zeng L, Lysis-lysogeny coexistence: prophage integration during lytic development, MicrobiologyOpen, (2016) [15].

35. Trinh J, Székely T, Shao Q, Balázsi G, Zeng L, Cell Fate Decisions Emerge as Phages Cooperate or Compete Inside their Host. Nat. Commun. 8:14341 (2017). [16]

36. Bódi Z, Farkas Z, Nevozhay D, Kalapis D, Lázár V, Csörgő B, Nyerges Á, Szamecz B, Fekete G, Papp B, Oliveira JL, Moura G, Santos MAS, Székely T Jr., Balázsi G, Pál Cs. Phenotypic heterogeneity promotes adaptive evolution PloS Biol. 15(5):e2000644 (2017). [17]

37. Bouklas T, Alonso-Crisóstomo L, Székely T Jr., Diago-Navarro E, Orner EP, Smith K, Munshi MA, Del Poeta M, Balázsi G, Fries BC. Generational distribution of a Candida glabrata population: Resilient old cells prevail, while younger cells dominate in the vulnerable host. PloS Path. 13(5):e1006355 (2017). [18]

38. Cortes M, Trinh J, Zeng L, Balázsi G. Late-arriving signals contribute less to cell fate decisions. Biophys. J. In Press (2017).


Research cartoon


More information may be found on two other websites:

1) The Louis & Beatrice Laufer Center for Physical & Quantitative Biology: [19]

2) Biomedical Engineering Department: [20].

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