From csbi-events at mit.edu Wed Nov 15 07:58:09 2006 From: csbi-events at mit.edu (CSBi events) Date: Wed, 15 Nov 2006 07:58:09 -0500 Subject: [CSBi-events] Fwd: CSBi Seminar - November 17, 2006! References: Message-ID: <5BE46AA6-E808-4E25-A1F4-05EC1BAAD0E5@mit.edu> ?Hilda Harris-Ransom, Senior Administrative Assistant > Department of Biology > Massachusetts Institute of Technology > 77 Massachusetts Ave, 68-204E > Cambridge, MA 02139 > > Phone: 617-253-9395 > Fax: 617-253-8699 > From csbi-events at mit.edu Wed Nov 15 08:52:02 2006 From: csbi-events at mit.edu (CSBi events) Date: Wed, 15 Nov 2006 08:52:02 -0500 Subject: [CSBi-events] CSBi Seminar Series Message-ID: <6C321483-402B-4613-9175-642F52E1C84E@mit.edu> PLEASE JOIN US! FRIDAY, NOVEMBER 17TH CSBi SEMINAR SERIES Dr Xiaowei Zhuang Professor of Chemistry and Chemical Biology and Physics Harvard University Single-Molecule Imaging of Biomolecular and Cellular Processes Friday, November 17, Hosted by Professor Sebastian Seung MIT, Brain and Cognitive Sciences Eastman Laboratories, (6-120) 3:00-4:00 Light refreshments served at 2:45 Contact: Hilda Harris-Ransom 253-9395 http://csbi.mit.edu Sponsored by CSBi From csbi-events at mit.edu Fri Nov 17 11:12:58 2006 From: csbi-events at mit.edu (CSBi events) Date: Fri, 17 Nov 2006 11:12:58 -0500 Subject: [CSBi-events] PLEASE JOIN US ---CSBI SEMINAR TODAY - NOVEMBER 17TH Message-ID: <968C298C-77B1-403C-9763-F2C961906212@mit.edu> CSBi and MIT Community Welcome! Dr. Xiaowei Zhuang Professor of Chemistry and Chemical Biology and Physics Harvard University 'Single-Molecule Imaging of Biomolecular and Cellular Processes" Friday, November 17, 2006 Time: 3:00-4:00 Eastman Laboratories (6-120) Light Refreshments at 2:45 Hosted by Professor Sebastian Seung MIT, Brain and Cognitive Sciences Contact: Hilda Harris-Ransom, Biology 253-9395 From csbi-events at mit.edu Mon Nov 20 08:20:09 2006 From: csbi-events at mit.edu (CSBi events) Date: Mon, 20 Nov 2006 08:20:09 -0500 Subject: [CSBi-events] CSBi Symposium Registration and Abstract Submission Open Now! Message-ID: Please Mark your Calendars! The registration is open for the 2007 CSBi symposium! Please follow our link to registration and to poster submission! CSBi Team http://csbi.mit.edu/events/annualsymposium/2007 2007 CSBi Symposium "Discovery, Design, and Development of Human Drugs and Therapeutics" Launched in 2003, the annual CSBi Symposium covers recent developments in and perspectives on the field of systems biology. This symposium usually attracts a large number of academic and industrial scientists who are interested in concepts emerging from this new field. More information for this year's symposium can be found in the Navigation Bar on the left. Wednesday, January 31, 2007 MIT, Kresge Auditorium (W16) Symposium Poster Session: Date: Tuesday, January 30, 2007 Time: 5:00-7:00 p.m. Location: 68-180, 181 Abstract submission deadline is January, 10, 2007 Register now until January 19, 2007 From csbi-events at mit.edu Mon Nov 20 10:22:41 2006 From: csbi-events at mit.edu (CSBi events) Date: Mon, 20 Nov 2006 10:22:41 -0500 Subject: [CSBi-events] PLEASE JOIN US TUESDAY MORNING 10:30 CSBI SEMINAR Message-ID: <77E4CDE0-9E45-42FF-B85C-B2E8E1B9F8BC@mit.edu> Please Join US tomorrow! Challenges for Intelligent Image Processing in Cryo-Electron Microscopy Christoph Best Dept. of Molecular Structural Biology Max Planck Institut fuer Biochemie 10.30am Tuesday November 21st First Floor Conference Room 500 Technology Square (NE47) Abstract Cryo-electron microscopy enable the imaging of macromolecular complexes and cellular structures in a near-natural state at molecular resolution. Recent developments in preparation, instrumentation, and automation carry the promise of imaging molecular structures at sub-nanometer resolutions in their native environment, as well as creating molecular maps of the macromolecular complexes in the living cell. These advancements pose unique new informatics problems in image processing. In particular, methods from machine learning and probabilistic modeling will play a large role in classifying images, combining them into three-dimensional structures, and extracting information from them. I will discuss several examples where modern informatics methods may improve electron microscopy such as model-free maximum-likelihood classification of projection images, particle picking through support vector machines, and 3D reconstruction from random projections using the Baum-Welch algorithm and Level Set methods. From csbi-events at mit.edu Tue Nov 28 08:06:47 2006 From: csbi-events at mit.edu (CSBi events) Date: Tue, 28 Nov 2006 08:06:47 -0500 Subject: [CSBi-events] WELCOME DR. LAUREN LINTON TO CSBI References: <5CB9E223-1FA8-452C-B2B9-72C324F5D67B@wi.mit.edu> Message-ID: Bruce and I would like to announce that Dr. Lauren Linton is the new Executive Director for CSBi. Lauren is no stranger to MIT or to systems biology. She was an MIT undergraduate in Chemistry, received her PhD from Stanford and then held a number of positions in the Cambridge area biotech scene including VP for Functional Proteomics and Cell Biology at Applied Biosystems. From 1997-2001, Lauren was the co-Director of the WI/MIT Center for Genome Research where she was responsible for ramping up the Center activities as it sequenced the genome. She has consistently demonstrated her management and leadership skills in the academic and industrial sectors whether with small start-ups or large companies. As CSBi Executive Director, Lauren will be charged with increasing the range and scope of CSBi research, education, and outreach activities at MIT as well as developing multiple lines of funding for support of CSBi with government, biotech and pharma. Please join us in welcoming Lauren to the CSBi community. Paul and Bruce From csbi-events at mit.edu Wed Nov 29 15:53:22 2006 From: csbi-events at mit.edu (CSBi events) Date: Wed, 29 Nov 2006 15:53:22 -0500 Subject: [CSBi-events] Talk on December 1st at 1:30 Message-ID: <927FBCB9-84A5-42B5-9A21-E6E30157C8B5@MIT.EDU> Talk: Friday, December 1 Time 1:30-3:30 Place 46-3015 Dr. Liam Paninski Statistical methods for understanding neural codes The neural coding problem --- deciding which stimuli will cause a given neuron to spike, and with what probability --- is a fundamental question in systems neuroscience. The high dimensionality of both stimuli and spike trains has spurred the development of a number of sophisticated statistical techniques for learning the neural code from finite experimental data. In particular, modeling approaches based on maximum likelihood have proven to be flexible and powerful. We present three such applications here. One common thread is that the models we have chosen for these data each have concave loglikelihood surfaces, permitting tractable fitting (by maximizing the loglikelihood) even in high dimensional parameter spaces, since no local maxima can exist for the optimizer to get ``stuck'' in. First we describe neural encoding models in which a linear stimulus filtering stage is followed by a noisy integrate-and-fire spike generation mechanism incorporating after-spike currents and spike- dependent conductance modulations. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can effectively reproduce a variety of spiking behaviors. We use this model to analyze extracellular data from populations of retinal ganglion cells, simultaneously recorded during stimulation with dynamic light stimuli. Here the model provides insight into the biophysical factors underlying the reliability of these neurons' spiking responses, and provides a framework for analyzing the cross-correlations observed between these cells. (Joint work with E.J. Chichilnisky, J. Pillow, J. Shlens, E. Simoncelli, and V. Uzzell, at NYU and Salk.) Next we describe how to use this model to ``decode'' the underlying subthreshold somatic voltage dynamics, given only the superthreshold spike train. We also point out some connections to spike-triggered averaging techniques. We close by discussing recent extensions to highly biophysically- detailed, conductance-based models, which have the potential to allow us to estimate the density of active channels in a cell's membrane and also to decode the synaptic input to the cell as a function of time. (With M. Ahrens and Q. Huys, Gatsby CNU.) Host Emery N. Brown, M.D., Ph.D. Professor of Computational Neuroscience and Health Sciences and Technology Department of Brain and Cognitive Sciences MIT-Harvard Division of Health Science and Technology Massachusetts Institute of Technology 77 Massachusetts Avenue, 46-6079 Cambridge, MA 02139 tel: 617 324 1880 email: enbrown1 at mit.edu Associate Professor of Anaesthesia Harvard Medical School Department of Anesthesia and Critical Care Massachusetts General Hospital 55 Fruit Street Clinics 3 Boston, MA 02114 tel: 617 726 8786 fax: 617 726 8410 email: brown at neurostat.mgh.harvard.edu