|Daniel Charlebois, PhD|
Research Keywords: Biophysics, mathematical and quantitative biology, computational, systems, and synthetic biology, gene regulatory networks, nongenetic variability, population dynamics algorithms, stochastic simulations, microbial evolution experiments, and drug resistance.
Variability & Antimicrobial Resistance
Utilizing mathematical modeling and computer simulation to investigate the role that gene network motifs (how genes are connect to and regulate each other) and nongenetic variability in gene expression play in drug resistance. This research presents a new paradigm for how phenotypic heterogeneity promotes drug resistance and identifies novel therapeutic targets.
Coherent feedforward transcriptional regulatory motifs enhance drug resistance. (2014) D.A. Charlebois, G. Balázsi, M. Kærn. Phys. Rev. E, 89: 052708.
What all the noise is about: The physical basis of cellular individuality. (2012) D.A. Charlebois, M. Kærn. Can. J. Phys., 90: 919-923.
Gene express noise facilitates adaptation and drug resistance independently of mutation. (2011) D.A. Charlebois, N. Abdennur, M. Kærn. Phys. Rev. Lett., 107: 218101.
Image Credit: John Gillespie
Extracellular Environment, Gene Networks & Microbial Evolution
Combining quatitative modeling and synthetic biology with microbial evolution experiments to study how gene networks evolve and how gene network function is affected by the extracellular environment. This work highlights how combining quantitative models and wet-lab experiments can provide new insights into the dynamics of biological systems.
Multiscale effects of heating and cooling on genes and gene networks. (2018) D.A. Charlebois, K. Hauser, S. Marshall, and G. Balázsi. Proc. Natl. Acad. Sci. USA, 115: E10797-E10806.
Efflux pump control alters synthetic gene circuit function. (2016) J. Diao, D.A. Charlebois, D. Nevozhay, Z. Bodi, C. Pal, and G. Balázsi. ACS Synth. Biol., 5: 619-631.
Effect and evolution of gene expression noise on the fitness landscape. (2015) D.A. Charlebois. Phys. Rev. E, 92: 022713.
Development of multiscale algorithms to simulate gene expression, heterogeneous cell population dynamics, and evolution. These algorithms are among the most accurate and efficient ways to computationally investigate cell population dynamics.
Modeling cell population dynamics. (2018) D.A. Charlebois and G. Balázsi. In Silico Biol., in press.
An accelerated method for simulating population dynamics. (2013) D.A. Charlebois and M. Kærn. Commun. Comput. Phys., 14: 461-476.
An algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. (2011) D.A. Charlebois, J. Intosalmi, D. Fraser, and M. Kærn. Commun. Comput. Phys., 9: 89-112.
CellLine, a stochastic cell lineage simulator. (2007) A.S. Ribeiro, D.A. Charlebois and J. Lloyd-Price. Bioinformatics, 23: 3409-3411.
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