Seminar @ Memorial Sloan Kettering Cancer Center

maxMolecular Machines: Optimal thermodynamic control and The thermodynamic cost of nostalgia

10:30-11:30am, May 7th 2015

ZRC 105, 417 East 68th Street New York, NY 10065

paper Thermodynamic metrics and optimal paths,
David A. Sivak, Gavin E. Crooks, Phys. Rev. Lett., 108, 190602 (2012)
[ Full text | Journal | More ]
title The geometry of thermodynamic control,
Patrick R. Zulkowski, David A. Sivak, Gavin E. Crooks and Michael R. DeWeese, Phys. Rev. E, 86, 041148 (2012)
[ Full text | Journal | More ]

Princeton Lectures on The Dynamics of Disorder

Over the next 2 weeks I will be lecturing at the Princeton Center for Theoretical Science on non-equilibrium statistical dynamics.

Lecture notes and other resources

== The Fluctuations of Dissipation ==
1:00-2:00 Tuesday, April 7th, A06 Jadwin Hall
Foundations of non-equilibrium statistical dynamics. I’ll explain the basics of fluctuations theorems and the Jarzynski’s identity.

== The Ambiguity of Time’s Arrow (Public Lecture) ==
8:00pm, A10 Jadwin Hall

== The Geometry of Thermodynamics ==
1:00-2:00 Tuesday, April 14th, A06 Jadwin Hall

== Entropy, information and Maxwell’s Demon ==
1:00-2:00 Thursday, April 16th 17th, A06 Jadwin Hall
Maxwell’s demon, Szilard’s engine, Landauer’s principle, and the feedback fluctuation theorems.


“And then there’s quantum, of course.” The monk sighed. “There’s always bloody quantum.” — Night Watch, Terry Pratchett

Berkeley Lectures on the Dynamics of Disorder

Starting this Thursday, I will be giving 3 guest introductory lectures on the non-equilibrium statistical dynamics of small systems, as part of Phill Geissler’s Advanced Statistical Mechanics course.

Lecture notes and other resources

Tentative syllabi:

== The fluctuations of dissipation ==
2:00-3:30 Thursday, March 12th, 425 Latimer
Equilibrium and disequilibrium; fluctuations theorems and the Jarzynski’s identity; experiments and observations.

== Entropy and information ==
2:00-3:30 Tuesday, March 17th, 425 Latimer
Maxwell’s demon, Szilard’s engine, Landauer’s principle, and the feedback fluctuation theorems.

== Perturbation and response ==
2:00-3:30 Thursday, March 19th, 425 Latimer
Perturbations and response; fluctuations and dissipation; from fluctuation theorems to linear response; the geometry of thermodynamics.

Announcement: Enrollment open for Kavli Scientist-Writer workshops

Enrollment is now open for the second year of the Kavli Scientist-Writer workshops @ NYU. I had the honor of attending the inaugural class, and it was great. As my fellow alumni puts it:

“The best course I’ve had since college… Scientists sorely need instruction of this kind, whether they try to write for the public or only for their peers. Every department at every institution should have this kind of course, and the organizers of this first pass did a fantastic job.”

– Jacqueline Gotteib, Kavli Institute of Brain Science, Columbia University

Our job, as scientists, is to explore strange new ideas, to seek out new knowledge and new understanding, to boldly experiment where no scientist has tinkered before. But none of that counts, if we don’t come back and tell people what we have discovered. And it dosn’t count as telling people if we write turgid incomprehensible prose that nobody will read or understand.

Article: Scaling laws governing stochastic growth and division of single bacterial cells

Srividya Iyer-Biswas, Charles S. Wright, Jonathan T. Henry, Klevin Lo, Stanislav Burov, Yihan Lin, Gavin E. Crooks, Sean Crosson, Aaron R. Dinner, Norbert F. Scherer, Proc. Natl. Acad. Sci. U.S.A. (2014)

[ Full text | Journal | arXiv ]cell

This is the experimental paper that logically (but, alas, not chronologically) precedes the companion theory paper.

Abstract: Uncovering the quantitative laws that govern the growth and division of single cells remains a major challenge. Using a unique combination of technologies that yields unprecedented statistical precision, we find that the sizes of individual Caulobacter crescentus cells increase exponentially in time. We also establish that they divide upon reaching a critical multiple (≈1.8) of their initial sizes, rather than an absolute size. We show that when the temperature is varied, the growth and division timescales scale proportionally with each other over the physiological temperature range. Strikingly, the cell-size and division-time distributions can both be rescaled by their mean values such that the condition-specific distributions collapse to universal curves. We account for these observations with a minimal stochastic model that is based on an autocatalytic cycle. It predicts the scalings, as well as specific functional forms for the universal curves. Our experimental and theoretical analysis reveals a simple physical principle governing these complex biological processes: a single temperature-dependent scale of cellular time governs the stochastic dynamics of growth and division in balanced growth conditions.

Article: Universality in Stochastic Exponential Growth

Srividya Iyer-Biswas, Gavin E. Crooks, Norbert F. Scherer, and Aaron R. Dinner, Phys. Rev. Lett., 113, 028101 (2014)

[ Full text | Journal ]cell

Abstract: Recent imaging data for single bacterial cells reveal that their mean sizes grow exponentially in time and that their size distributions collapse to a single curve when rescaled by their means. An analogous result holds for the division-time distributions. A model is needed to delineate the minimal requirements for these scaling behaviors. We formulate a microscopic theory of stochastic exponential growth as a Master Equation that accounts for these observations, in contrast to existing quantitative models of stochastic exponential growth (e.g., the Black-Scholes equation or geometric Brownian motion). Our model, the stochastic Hinshelwood cycle (SHC), is an autocatalytic reaction cycle in which each molecular species catalyzes the production of the next. Continue reading

WebLogo 3.4 Released

WebLogo 3.4 web-server

WebLogo 3.4 code

3.4 (2014-06-03) [Gavin Crooks, Eric Talevich]

* Python 3
Weblogo now runs under python 2.6, 2.7, 3.2, 3.3 & 3.4 (Python2.5 is no longer
supported.) Note that the api for creating a logo has changed. See docs.
(Kudos: Eric Talevic)
* Fix bug with using Ghostscipt 9.10 (Issue 36)
(Kudos: Michael Imbeault, Estienne Swart, FiReaNG3L)
* Fix various bugs in transfac parsing.
(Kudos: Promita Bose, Christopher Lamantia)
* Fix –complement of gapped sequences, added –revcomp option
(Kudos: Jacob Engelbrecht))
* Miscellaneous minor bug fixes and refactoring.
(Kudos: Kamil Slowikowski, Jacob Engelbrecht)

Article: Time step rescaling recovers continuous-time dynamical properties for discrete-time Langevin integration of nonequilibrium systems

David A. Sivak, John D. Chodera, Gavin E. Crooks, arXiv:1301.3800

[ Full text | Journal | ArXiv]

Abstract: While the numerical integration of deterministic equations of motion for molecular systems now has a well-developed set of algorithms with commonly agreed-upon desirable properties, the simulation of stochastic equations of motion lacks algorithms with a similar degree of universal acceptance. Part of the difficulty is in determining which of many properties should be satisfied by such a discrete time integration scheme, with additional difficulties in satisfying many properties simultaneously with a single scheme. The desire to use these integration schemes for nonequilibrium simulations and in conjunction with recent nonequilibrium fluctuation theorems adds additional complications. Here, we compare a number of discrete time integration schemes for Langevin dynamics, Continue reading

Tech. Note: Survey of Simple, Continuous, Univariate Probability Distributions

Unimodal distributions

Version: 0.5 BETA

In a desperate attempt to preserve my own sanity, a survey of probability distributions used to describe a single, continuous, unimodal, univariate random variable.

Whats New: Added uniform product, half generalized Pearson VII, half exponential power distributions, GUD and q-Type distributions; Moved Pearson IV to own section; Fixed errors in Inverse Gaussian; Added random variate generation appendix. Fixed typos. Thanks to David Sivak, Dieter Grientschnig, Srividya Iyer-Biswas and Shervin Fatehi.

[ Full Text ]

Continue reading