CIS - "Get to know your neighbors" Seminar series - Prof. John Maddocks
Event details
| Date | 05.07.2021 |
| Hour | 15:15 › 16:15 |
| Speaker | Prof. John Maddocks |
| Location | Online |
| Category | Conferences - Seminars |
Title: DNA and Big Data
Abstract: It is well understood that the sequence of DNA codes for what genes are expressed, and in which variant. This is the realm of bioinformatics: studying patterns, and in particular local variations, in strings of letters of length some billions, and annotating sequence variants with known changes in biological and medical function. In other words WHAT each part of a genome is responsible for. But there is now a widespread consensus that to understand HOW a genome functions, the sequence-dependence of the physical properties of DNA, such as intrinsic shape and stiffness as expressed in its statistical mechanics, are also crucial. In this talk I will describe two ways in which simple machine learning approaches can be applied to big data sets to address the sequence-dependent statistical mechanics of DNA. First, times series data, generated during long duration, fully atomistic, Molecular Dynamics simulations of short DNA fragments can be used to train a local, sequence-dependent, coarse-grain, Gaussian, equilibrium distribution model that we call cgDNA+ https://cgdnaweb.epfl.ch/. This first part includes the description of some special properties of any Gaussian with a banded stiffness (or inverse covariance) matrix, which are apparently not widely known. Second I will discuss properties of the large ensembles (millions or more elements) of banded Gaussians that are generated 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 epigenetic base modifications, such as methylation, strongly affect the statistical mechanics properties of DNA.
Bio: John Maddocks obtained his D.Phil in applied mathematics from the University of Oxford in 1981. After various postdoctoral positions (Stanford, Oxford, Minnesota) he joined the faculty of the University of Maryland in 1985. He assumed the Chair of 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 EPFL the bulk of his research efforts have been directed toward understanding the physics of DNA.
The Center for Intelligent Systems at EPFL (CIS) is a collaboration among IC, ENAC, SB; SV and STI that brings together researchers working on different aspects of Intelligent Systems.
In order to promote exchanges among researchers and encourage the creation of new, collaborative projects, CIS is organizing a "Get to know your neighbors" series. Each seminar will consist of one short overview presentation geared to the general public at EPFL.
The CIS seminar will take place live on Zoom: https://epfl.zoom.us/j/62368327539
Please connect to your zoom account using your "@epfl.ch" address, as this live event is only open to the EPFL community
Monday, July 5th, 2021 from 3:15 to 4:15 pm
NB: Video recordings of the seminars will be made available on our website and published on our social media pages
Abstract: It is well understood that the sequence of DNA codes for what genes are expressed, and in which variant. This is the realm of bioinformatics: studying patterns, and in particular local variations, in strings of letters of length some billions, and annotating sequence variants with known changes in biological and medical function. In other words WHAT each part of a genome is responsible for. But there is now a widespread consensus that to understand HOW a genome functions, the sequence-dependence of the physical properties of DNA, such as intrinsic shape and stiffness as expressed in its statistical mechanics, are also crucial. In this talk I will describe two ways in which simple machine learning approaches can be applied to big data sets to address the sequence-dependent statistical mechanics of DNA. First, times series data, generated during long duration, fully atomistic, Molecular Dynamics simulations of short DNA fragments can be used to train a local, sequence-dependent, coarse-grain, Gaussian, equilibrium distribution model that we call cgDNA+ https://cgdnaweb.epfl.ch/. This first part includes the description of some special properties of any Gaussian with a banded stiffness (or inverse covariance) matrix, which are apparently not widely known. Second I will discuss properties of the large ensembles (millions or more elements) of banded Gaussians that are generated 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 epigenetic base modifications, such as methylation, strongly affect the statistical mechanics properties of DNA.
Bio: John Maddocks obtained his D.Phil in applied mathematics from the University of Oxford in 1981. After various postdoctoral positions (Stanford, Oxford, Minnesota) he joined the faculty of the University of Maryland in 1985. He assumed the Chair of 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 EPFL the bulk of his research efforts have been directed toward understanding the physics of DNA.
The Center for Intelligent Systems at EPFL (CIS) is a collaboration among IC, ENAC, SB; SV and STI that brings together researchers working on different aspects of Intelligent Systems.
In order to promote exchanges among researchers and encourage the creation of new, collaborative projects, CIS is organizing a "Get to know your neighbors" series. Each seminar will consist of one short overview presentation geared to the general public at EPFL.
The CIS seminar will take place live on Zoom: https://epfl.zoom.us/j/62368327539
Please connect to your zoom account using your "@epfl.ch" address, as this live event is only open to the EPFL community
Monday, July 5th, 2021 from 3:15 to 4:15 pm
NB: Video recordings of the seminars will be made available on our website and published on our social media pages
Practical information
- General public
- Free
Organizer
- CIS