BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Machine Learning and Synthetic Biology
DTSTART:20200211T160000
DTSTAMP:20260413T105834Z
UID:ff382e62daf436a45dbb02b19c9171aa9c7df48e7010dd5f2003cca1
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Jean-Loup Faulon\, National Research Institute for Agric
 ulture\, Food and Environment INRAE\, Jouy-en-Josas (F) and University of 
 Manchester (UK)\nBIOENGINEERING SEMINAR\n\nAbstract:\nWe have seen the pas
 t few years a growing interest in using machine learning in biology and sy
 nthetic biology makes no exception to this trend. The three main learning 
 methods found in the synthetic biology literature are supervised learning\
 , reinforcement and active learning\, and in vivo/in vitro learning.\n\nSu
 pervised machine learning is being exploited to predict sequence activitie
 s\, engineer sequences\, associate biological signals with phenotypes\, an
 d optimize culture conditions. An example of promiscuous enzyme activity p
 rediction with Gaussian processes will be shown in the context of undergro
 und metabolism and metabolome completion.\n\nReinforcement and active lear
 ning are using training sets acquired through an iterative process. These 
 methods are particularly emendable to the Design-Build-Test-Learn syntheti
 c biology cycle. Reinforcement learning will be exemplified for retrosynth
 esis and active learning to maximize the productivity of TXTL cell-free sy
 stems.\n\nEngineering information processing devices in living systems is 
 a long-standing venture of synthetic biology. Yet\, the problem of enginee
 ring devices that perform basic operations found in machine learning remai
 ns largely unexplored. As a first step toward engineering biological learn
 ing devices\, a metabolic perceptron will be presented. The performances o
 f the perceptron will be exemplified with biological sample classification
  based on metabolic composition.\n\nReferences:\n\n	Mellor J\, et al. Semi
 -supervised Gaussian Process for automated enzyme search. ACS Synthetic Bi
 ology\, 2016\, 5(6): 518-528.\n	Koch M\, et al. Reinforcement Learning fo
 r Bioretrosynthesis. ACS Synthetic Biology\, 2020\, 9(1): 157-168.\n	Borko
 wski O\, et al. Large scale active-learning-guided exploration to maximize
  cell-free production\, bioRxiv 751669\, doi: https://doi.org/10.1101/7516
 69\n	Pandi A\, et al. Metabolic Perceptrons for Neural Computing in Biolo
 gical Systems. Nature Communications\, 2019\, 10: 3880.\n\n\nBio:\nEducati
 on:\n\n	Ph.D. Computer Science\, Ecole des Mines\, Paris\, 1991\n	Habilita
 tion\, Theoretical Chemistry\, Strasbourg\, 2007\n\nPositions:\n\n\n	2015-
  Director of Research (DR1) at Micalis\, INRA \, France\n	2014- Professor 
 Manchester Institute of Biotechnology \, University of Manchester\, UK\n	2
 010-2015 Director Institute of Systems and Synthetic Biology \, CNRS\, Gen
 opole\, University of Evry\, France\n	2009- Head master program in Systems
  and Synthetic Biology (at the University of Paris-Saclay since 2015)\n	20
 08-2014 – Professor\, Biology Department\, University of Evry\, Evry\, F
 rance\n	1995-2008 – Senior and Distinguished Member (2004)\, Computation
 al Systems Biology Dept. Sandia National Laboratory\, Livermore\, CA\, USA
 \n	1993-1994 – Research Associate\, Computer-Aided Molecular Design Grou
 p. Sandia National Laboratory\, Albuquerque\, NM\, USA\n	1991-1993 – Pos
 t-doc Penn State University\, State College\, PA\, USA\n
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR