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SUMMARY:CIS - "Get to know your neighbors" Seminar series - Prof. John Mad
 docks
DTSTART:20210705T151500
DTEND:20210705T161500
DTSTAMP:20260916T032114Z
UID:e5dc4a2d6b3739e58f0f94e823389371a7a53f037bb819e27985eb8e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. John Maddocks\nTitle:  DNA and Big Data\n\nAbstract: It
  is well understood that the sequence of DNA codes for what genes are expr
 essed\, and in which variant. This is the realm of bioinformatics: studyin
 g patterns\, and in particular local variations\, in strings of letters of
  length some billions\, and annotating sequence variants with known change
 s in biological and medical function. In other words WHAT each part of a g
 enome is responsible for. But there is now a widespread consensus that to 
 understand HOW a genome functions\, the sequence-dependence of the physica
 l properties of DNA\, such as intrinsic shape and stiffness as expressed i
 n its statistical mechanics\, are also crucial. In this talk I will descri
 be two ways in which simple machine learning approaches can be applied to 
 big data sets to address the sequence-dependent statistical mechanics of D
 NA. First\, times series data\, generated during long duration\, fully ato
 mistic\, Molecular  Dynamics simulations of short DNA fragments can be us
 ed to train a local\, sequence-dependent\, coarse-grain\, Gaussian\, equil
 ibrium distribution model that we call cgDNA+ https://cgdnaweb.epfl.ch/. T
 his first part includes the description of some special properties of any 
 Gaussian with a banded stiffness (or inverse covariance) matrix\, which ar
 e apparently not widely known. Second I will discuss properties of the lar
 ge ensembles (millions or more elements) of banded Gaussians that are gene
 rated by using the cgDNA+ model to scan genomes\, thus closing the circle 
 back to bioinformatics. As time permits I will also give examples of how e
 pigenetic base modifications\, such as methylation\, strongly affect the s
 tatistical mechanics properties of DNA.\n\nBio: John Maddocks obtained his
  D.Phil in applied mathematics from the University of Oxford in 1981. Afte
 r various postdoctoral positions (Stanford\, Oxford\, Minnesota) he joined
  the faculty of the University of Maryland in 1985. He assumed the Chair o
 f Applied Analysis at the EPFL in 1997. Currently he also holds a Visiting
  Fellowship funded from the Einstein Research Foundation of Berlin. He has
  published in a wide range of areas of applied mathematics and mechanics\,
  such as robotics and the mechanics of knots\, but since moving to the EPF
 L the bulk of  his research efforts have been directed toward understandi
 ng the physics of DNA.\nThe Center for Intelligent Systems at EPFL (CIS) i
 s a collaboration among IC\, ENAC\, SB\; SV and STI that brings together r
 esearchers working on different aspects of Intelligent Systems.\n \nIn or
 der to promote exchanges among researchers and encourage the creation of n
 ew\, collaborative projects\, CIS is organizing a "Get to know your neighb
 ors" series. Each seminar will consist of one short overview presentation 
 geared to the general public at EPFL.   \n \nThe CIS seminar will take
  place live on Zoom: https://epfl.zoom.us/j/62368327539\n\n\nPlease connec
 t to your zoom account using your "@epfl.ch" address\, as this live event 
 is only open to the EPFL community\nMonday\, July 5th\, 2021 from 3:15 to 
 4:15 pm\nNB: Video recordings of the seminars will be made available on ou
 r website and published on our social media pages
LOCATION:Zoom https://epfl.zoom.us/j/62368327539 https://epfl.zoom.us/j/62
 368327539
STATUS:CONFIRMED
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