Mirny Lab

The challenge of understanding biological systems from first physical principles is what motivates this research. Biological systems are characterized by remarkable structural complexity at all levels of organization. The Mirny Lab research focuses on two of these levels: the molecular level and the systems level, attempting to bring them together in the analysis of biological phenomena. The laboratory develops multidisciplinary approaches involving computer simulations, evolutionary analysis and biophysical modeling to study how interactions between individual molecules give birth to complex biological systems.

Areas of interest are:
(*) DNA's secret weapon against knots and tangles -- A simple process seems to explain how massive genomes stay organized. But no one can agree on what powers it.
(*) Three dimensional organization of chromosomes -- Beyond encoding information in their linear sequences, chromosomes are organized in three dimensions. We believe that chromosomal organization reflects an interplay between biological processes and statistical properties of polymer ensembles. The recent development of the biochemical chromosome conformation capture (3C) techniques, eg. Hi-C, complements advances in optical views of chromosomal organization.Our goal is to synthesize the high-resolution and high-throughput information from the former with information on cell-to-cell variability and dynamics obtained via the latter. Our approach combines bioinformatic and statistical analyses of experimental data with bottom-up polymer physics models of chromosomes.
(*) Evolutionary Dynamics of Cancer -- The development of cancer can be considered as an evolutionary process within an organism. During cancer progression, cells acquire mutations, compete for resources, and are selected for the ability to grow in a complex and dynamic environment. Our goal is to understand how cancer's evolutionary history shapes its current state. For example, we are interested in how classical population genetics concepts, like genetic load, influence cancer progression and present new opportunities for cancer therapy. We are exploring this possibility using computational and analytical stochastic models of cancer progression, by analyzing cancer genomics data, and by testing therapeutic strategies in cell lines and mouse models via collaborations.