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)
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
Next meeting: Friday 3/7 at 3:30pm in 560 Evans.
We’ll be going over the recent Spinney-Ford and Jarzynski review papers.
(Inaugural meeting: Feb 21, 2014, 3.30pm, and every 2 weeks thereafter)
Location: Redwood Center Seminar Room, 560 Evans Hall, UC Berkeley
For email notices signup to the LINEQ mailing list..
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 ]
David A. Sivak, John D. Chodera, Gavin E. Crooks, Phys. Rev. X 3, 011007 (2013)
[ Full text | Journal]
Abstract: Common algorithms for computationally simulating Langevin dynamics must discretize the stochastic differential equations of motion. These resulting finite-time-step integrators necessarily have several practical issues in common: Microscopic reversibility is violated, the sampled stationary distribution differs from the desired equilibrium distribution, and the work accumulated in nonequilibrium simulations is not directly usable in estimators based on nonequilibrium work theorems. Here, we show that, even with a time-independent Hamiltonian, finite-time-step Langevin integrators can be thought of as a driven, nonequilibrium physical process. Once an appropriate worklike quantity is defined – here called the shadow work – Continue reading
Abstract: A deeper understanding of nonequilibrium phenomena is needed to reveal the principles governing natural and synthetic molecular machines. Recent work has shown that when a thermodynamic system is driven from equilibrium then, in the linear response regime, the space of controllable parameters has a Riemannian geometry induced by a generalized friction tensor. Continue reading
Susanne Still, David A. Sivak, Anthony J. Bell and Gavin E. Crooks, Phys. Rev. Lett., 109, 120604 (2012)
[ Full Text | Journal | Press | arXiv ]
Hat Trick! And Editors’ Suggestion. Also a Nature News item by Philip Ball.
This paper is a melding of ideas about machine learning from Susanne Still and Tony Bell, with ideas from David and I about nonequilibrium thermodynamics. For a molecular scale machine with information processing capabilities, there’s a tradeoff between thermodynamic efficiency, memory and prediction. A prodigious memory allows more accurate prediction of the future, which can be exploited to reduce dissipation. But the persistence of memory is a liability, since information erasure leads to increased dissipation. A thermodynamically optimal machine must balance memory versus prediction by minimizing its nostalgia, the useless information about the past.
The University of California, San Francisco, is to be congratulated on their recent hiring of David Sivak (Formally a Crooks Ensemble Postdoctoral Fellow) as a Fellow of Systems Biology.
WebLogo 3.3 web-sever
WebLogo 3.3 code
[Gavin Crooks, David Sivak]
* Improved the algorithm that guesses the sequence type (DNA, RNA or protein) (Kudos: Bug report, Roland Pache )
* Fixed an issue with reading transfac matrices with alternative alphabets(Kudos: Bug report, Nima Emami )
* Fixed Motif.reindex()
* Implemented Motif.reverse() and Motif.complement() (Can now reverse complement transfac matrix input on the command
line.) (Kudos: Feature request, Seth P Boudreaux )
* Command line interface now automagically recognizes transfac files.
* Add command line option “–reverse-stacks NO” which inverts the logo stacks so that the most conserved monomers are at the bottom of the stack, rather than the top. This ordering is consistent with the standard ordering for histograms, and is arguably a better representation. (Kudos: Luke Hutchison )
* Fixed an issue so that the correct color scheme is chosen for the specified sequence type.
* Miscellaneous minor bug fixes and refactoring.
David A. Sivak, Gavin E. Crooks, Phys. Rev. Lett., 108, 190602 (2012)
[ Full Text | Journal | arXiv ]
This is mostly David’s creation, from inception to conclusion, although I like to think I’ve contributed some elegant prose. Essentially, he shows that at the intersection of linear response theory and thermodynamic length analysis, there is a really nice framework for understanding optimal protocols for driving thermodynamic systems. Continue reading