Steve Pressé Ph.D.

Assistant Professor, Physics, Chemistry
Adjunct Faculty, IU School of Medicine, Cell and Integrative Physiology
Associate Faculty, Chemistry and Chemical Biology
Primary Appointment: Physics


Postdoctoral Fellow, Biophysics, University of California, San Francisco, CA

Ph.D. Chemical Physics, Massachusetts Institute of Technology, Cambridge, MA

B.Sc.Hon. Chemistry, McGill University, Montreal QC

Awards & Honors

2015             Scialog Fellow

2008-2010     FQRNT Postdoctoral Fellowship 

2007-2008     FQRNT Doctoral Fellowship

2005-2007     NSERC Doctoral Fellowship

2003-2005     NSERC Master’s Fellowship

2004             Outstanding Teaching Award, MIT Chemistry Dept.

For full list of awards, see CV.


2015             IUCRG / Purdue Research Foundation Summer Grant

2014             NSF MCB / Purdue Research Foundation Research Grant

2013             Burroughs-Wellcome Travel Grant / Purdue Research Foundation Summer Grant

Professional Affiliations

 American Physical Society (APS)

 Sigma Xi, Golden Key

Teaching Assignments

Physics 251 --  Heat, Electricity and Optics (Fall 2015)

Physics 585 -- Graduate Biophysics (Fall 2014)

Physics 617 -- Graduate Statistical Physics (Fall 2013)

Recitation Physics 251 --  Heat, Electricity and Optics (Fall 2013, Spring 2013, 2015)

We are looking for highly motivated undergraduate and graduate students.  If you are 1) interested in building models relevant to biological problems or doing biophysical experiments, 2) like math/programming, then please don't hesitate to send your CV regardless of your major. See our Lab webpage ( for more details.

Current Research

Our work involves theoretical studies (and now experimental studies as well) from the level of single molecule to the level of systems biology in collaboration with experiment. At the length scale and time scale relevant to biology, most events are random. That is, events are described using probabilistic models. Often models are necessary to make sense of the data. Yet data is limited and noisy while extracting probabilistic models from data is a challenging (inverse) problem. We develop the theoretical and numerical tools to tackle these problems using techniques motivated from statistical mechanics and stochastic processes. 

For instance, our experimental collaborators and our group work on a variety of problems which include protein diffusion as it undergoes catalytic reactions and the single molecule mechanics of protein translocation and degradation by molecular motors. We are also interested in understanding chemotaxis from the motor protein to the organismal level in the presence of little chemoattractant.  

On the pure theory side, we are interested in many problems. Here are some examples: 1) Developing information criteria for model discrimination specifically targeted at single molecule fluorescence and force spectroscopy experiments. 2) Developing an information theoretic basis for ubiquitous models in biophysics (say the Markov process as a simple example). The purpose would be to derive principled generalizations of these models broadly applicable to biophysical problems. 

See our group website for a more detailed description and publications.

Select Publications

All Available as pdfs on my website (

C. Riedel, R. Gabizon, C.A.M. Wilson, K. Hamadani, K. Tsekouras, S. Marqusee, S. Pressé*, C. Bustamante*, “The heat released during catalytic turnover enhances the enzymes diffusion”, Nature517, 227 (2015) 

             -- Featured in C&EN News,, Nature News and Views, School of Science Website.

G. Rollins, J.Y. Shin, C. Bustamante, S. Pressé*, “A stochastic approach to the molecular counting problem in super-resolution microscopy”, Proc. Natl. Acad. Sc. (Direct Submission), 112, E110 (2015) 

              -- Featured on: EurakeAlert!, PNAS Research Highlight, IUPUI SoS Website 

S. Pressé*, “A data-driven alternative to the fractional Fokker-Planck equation”, J. Stat. Phys.accepted (2015).

K. Tsekouras, A. Siegel, R. Day, S. Pressé*, “Inferring Diffusional Dynamics from FCS in Heterogeneous Nuclear Environments”, Biophys. J., accepted (2015). 

S. Pressé*, “Nonadditive entropy maximization is incompatible with Bayesian updating”, Phys. Rev. E., 90, 052149 (2014). 

S. Pressé*, J. Peterson, J. Lee, P. Elms, J.L. MacCallum, S. Marqusee, C. Bustamante, K. Dill, “Single molecule conformational memory extraction: P5ab RNA hairpin”, J. Phys. Chem. B, ASAP (2014) 

S. Pressé*, K. Ghosh, J. Lee, K. Dill, “ Nonadditive entropies yield probability distributions with biases not warranted by the data”, Physical Review Letters, 111, 180604 (2013)

               -- Featured in School of Science Website, Science Daily

M. Sen, R.A. Maillard, K. Nyquist, P. Rodriguez-Aliaga, S. Pressé, A. Martin*, C. Bustamante*, “The ClpXP protease functions as a motor with constant ‘rpm’ but different ‘gears’ ”, Cell, 115, 636 (2013) 

               -- Featured in School of Science Website

S. Pressé*, J. Lee, K. Dill, “Extracting conformational memory from single-molecule kinetic data”, J. Phys. Chem. B, 117, 495 (2013)

S. Pressé*, K. Ghosh, J. Lee, K. Dill, “Principles of maximum entropy and maximum caliber in statistical physics”, Rev. Mod. Phys., 85, 1115, (2013)

J. Lee*, S. Pressé*, “Microcanonical origin of the maximum entropy principle for open systems”, Phys. Rev. E, 86, 041126 (2012)

J. Lee*, S. Pressé*, “A derivation of the master equation from path entropy maximization”, J. Chem. Phys., 137, 074103 (2012)

G.J. Peterson*, S. Pressé, K. Peterson, K.A. Dill, “Simulated evolution of protein-protein interaction networks with realistic topology”, PLoS ONE, 7, e39052 (2012)

Hao Ge*, S. Pressé, K. Ghosh, K.A. Dill, “Markov processes follow from the principle of maximum caliber”, J. Chem. Phys., 136, 064108 (2012) – Selected by the Editors as a Research Highlight.

S. Pressé*, K. Ghosh, K.A. Dill, “Modeling stochastic dynamics in biochemical systems with feedback using maximum caliber”, J. Phys. Chem. B, 115, 6202 (2011)

G. J. Peterson, S. Pressé, K.A. Dill*, “Nonuniversal power law scaling in the probability distribution of scientific citations”, Proc. Natl. Acad. Sc., 107, 16023 (2010).

For full list of publications, see CV.