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The variational principles called maximum entropy (MaxEnt) and maximum caliber (MaxCal) are reviewed. MaxEnt originated in the statistical physics of Boltzmann and Gibbs, as a theoretical tool for predicting the equilibrium states of thermal systems. Later, entropy maximization was also applied to matters of information, signal transmission, and image… (More)

- G. J. Peterson, Steve Pressé, Ken A. Dill
- Proceedings of the National Academy of Sciences…
- 2010

We develop a model for the distribution of scientific citations. The model involves a dual mechanism: in the direct mechanism, the author of a new paper finds an old paper A and cites it. In the indirect mechanism, the author of a new paper finds an old paper A only via the reference list of a newer intermediary paper B, which has previously cited A. By… (More)

- Steve Pressé, Kingshuk Ghosh, Julian Lee, Ken A Dill
- Physical review letters
- 2013

Different quantities that go by the name of entropy are used in variational principles to infer probability distributions from limited data. Shore and Johnson showed that maximizing the Boltzmann-Gibbs form of the entropy ensures that probability distributions inferred satisfy the multiplication rule of probability for independent events in the absence of… (More)

- Maya Sen, Rodrigo A. Maillard, +4 authors Carlos Bustamante
- Cell
- 2013

ATP-dependent proteases are vital to maintain cellular protein homeostasis. Here, we study the mechanisms of force generation and intersubunit coordination in the ClpXP protease from E. coli to understand how these machines couple ATP hydrolysis to mechanical protein unfolding. Single-molecule analyses reveal that phosphate release is the force-generating… (More)

- Steve Pressé
- 2014

The maximum entropy method—used to infer probabilistic models from data—is a special case of Bayes’s model inference prescription which, in turn, is grounded in basic propositional logic. By contrast to the maximum entropy method, the compatibility of nonadditive entropy maximization with Bayes’s model inference prescription has never been established. Here… (More)

- Steve Pressé, Jack Peterson, +5 authors Ken Dill
- The journal of physical chemistry. B
- 2014

Extracting kinetic models from single molecule data is an important route to mechanistic insight in biophysics, chemistry, and biology. Data collected from force spectroscopy can probe discrete hops of a single molecule between different conformational states. Model extraction from such data is a challenging inverse problem because single molecule data are… (More)

- Konstantinos Tsekouras, Amanda P Siegel, Richard N Day, Steve Pressé
- Biophysical journal
- 2015

Fluorescence correlation spectroscopy (FCS) is a noninvasive technique that probes the diffusion dynamics of proteins down to single-molecule sensitivity in living cells. Critical mechanistic insight is often drawn from FCS experiments by fitting the resulting time-intensity correlation function, G(t), to known diffusion models. When simple models fail, the… (More)

- G. Jack Peterson, Steve Pressé, Kristin S. Peterson, Ken A. Dill
- PloS one
- 2012

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many… (More)

Complex feedback systems are ubiquitous in biology. Modeling such systems with mass action laws or master equations requires information rarely measured directly. Thus rates and reaction topologies are often treated as adjustable parameters. Here we present a general stochastic modeling method for small chemical and biochemical systems with emphasis on… (More)

- Steve Pressé, Julian Lee, Ken A Dill
- The journal of physical chemistry. B
- 2013

Single-molecule data often come in the form of stochastic time trajectories. A key question is how to extract an underlying kinetic model from the data. A traditional approach is to assume some discrete state model, that is, a model topology, and to assume that transitions between states are Markovian. The transition rates are then selected according to… (More)