BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20190918T155541
DTSTART;TZID=America/Detroit:20190924T113000
DTEND;TZID=America/Detroit:20190924T130000
SUMMARY:Workshop / Seminar:Complex Systems Seminar | Statistical Mechanics of Microbiomes
DESCRIPTION:Abstract: Next-generation sequencing\, high-throughput metabolomics and other measurement technologies have opened vast new horizons for collecting data on the structure and function of microbial communities. But it remains unclear how to leverage this data for effective intervention in medical and agricultural applications. We do not know which quantities can be reliably predicted\, which are hopelessly contingent\, and what the predictors are for the former. In this talk\, I will draw on conceptual tools from Statistical Physics\, which were designed to answer precisely these sorts of questions. In particular\, I will argue that the key features of community structure are encoded in a susceptibility matrix\, which contain the response of species population sizes to small changes in growth rates. I will show how to estimate this matrix in different scenarios from existing data sets\, and then explain how it can be used to cluster species into functionally redundant groups for enhanced predictability of community composition.
UID:63981-16051364@events.umich.edu
URL:https://events.umich.edu/event/63981
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Biosciences,Ecology,Lsaresearch,Natural Sciences,Physics,Research,seminar
LOCATION:Weiser Hall - 747
CONTACT:
END:VEVENT
END:VCALENDAR