|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, evolution experiments, and drug resistance.
Variability & Antimicrobial Resistance
Mathematical modeling and computer simulation to investigate the role that gene network topology (how genes are connect to and regulate each other) and nongenetic variability in gene expression play in drug resistance. This research presents a new understanding of how nongenetic phenotypic heterogeneity promotes drug resistance and could lead to new therapeutic targets.
Coherent feedforward transcriptional regulatory motifs enhance drug resistance. (2014) Daniel A Charlebois, Gabor Balazsi, Mads Kaern. Physical Review E, 89: 052708.
What all the noise is about: The physical basis of cellular individuality. Daniel A. Charlebois, Mads Kaern. (2012) Canadian Journal of Physics, 90: 919-923.
Gene express noise facilitates adaptation and drug resistance independently of mutation. Daniel A Charlebois, Nezar Abdennur, Mads Kaern. (2011) Physical Review Letters, 107: 218101.
Image Credit: John Gillespie
Environmental Effects & Gene Network Dynamics
Combining systems and synthetic biology with evolution experiments in budding yeast to study how gene networks evolve and how gene network function is affected by the environment. This work highlights how combining quantitative models and wet-lab experiments can provide new insights into the dynamics of biological systems.
Efflux pump control alters synthetic gene circuit function. Junchen Diao, Daniel A. Charlebois, Dmitry Nevozhay, Zoltan Bodi, Csaba Pal, and Gabor Balazsi. (2016) ACS Synthetic Biology, 5: 619-631.
Effect and evolution of gene expression noise on the fitness landscape. Daniel A Charlebois. (2014) Physical Review 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.
An accelerated method for simulating population dynamics. Daniel A. Charlebois, Mads Kaern. Communications in Computational Physics, 14(2): 461-476.
An algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. Daniel A Charlebois, Jukka Intosalmi, Dawn Fraser, Mads Kaern. (2011) Communications in Computational Physics, 9: 89-112.
CellLine, a stochastic cell lineage simulator. Andre S. Ribeiro, Daniel A. Charlebois, Jason Lloyd-Price. (2007) Bioinformatics, 23: 3409-3411.
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