Research Interests

The challenge of understanding biological systems from first physical principles is what motivates my research. Biological systems are characterized by remarkable structural complexity at all levels of organization. However, we believe that simple physical models are valuable for describing these systems. My laboratory develops multidisciplinary approaches involving:

·  Population genetics and evolutionary theory
·  Polymer physics theory and simulation
·  Statistical interpretation of genome-wide data

A key feature of our approach is the direct collaborations we have with other scientists in the area. A number of my graduate students were co-advised by an experimentalist.  Nearly every student in my lab works directly with our experimental collaborators, contributing both to experimental design, data analysis, and modeling.

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. My goal is to understand how cancer's evolutionary history shapes its current state. For example, I am 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 massive cancer genomics data, and by testing therapeutic strategies in cell lines and mouse models via collaborations.

Biophysics of Protein-DNA Interactions
To respond to stimuli cells need to regulate gene expression quickly and accurately. Protein-DNA interactions are a key determinant of this process. Our lab develops kinetic and thermodynamic models to account for the speed and accuracy of protein-DNA binding. Our models investigate the importance of conformational changes of proteins, sliding along DNA, and specific/non-specific binding to DNA. We then compare these models with both single-molecule experiments, and microarray-based data.

Selected Publications:

Wunderlich, Z., Mirny, L.A. (2009). An optimized energy potential can predict SH2 domain-peptide interactions. Nucleic Acids Res. 1-13.

Mirny, L.A. (2008). Cell commuters avoid delays. Nature Physics, 4, 93-95.

Mirny, L.A. (2010) Nucleosome-mediated cooperativity between transcription factors. PNAS 107(52):22534-9.

Tafvizi, A., Huang, F., Fersht, A.R., Mirny, L.A., van Oijen, A.M. (2011). A single-molecule characterization of p53 search on DNA. PNAS 108(2), pp563-568.

Mirny, L.A., Needleman, D.J. (2010). Quantitative characterization of filament dynamics by single-molecule lifetime measurements. Methods Cell Biol. 95:583-600.