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DTSTAMP:20261020T034717Z
LAST-MODIFIED:20180402T135348Z
DTSTART:20180404T160000Z
DTEND:20180404T170000Z
UID:event1935@bu.edu
URL:http://physics.bu.edu/internal/events/show/1935
SUMMARY:Network Topology and Community Function in Spatial Microbial Commun
	ities
DESCRIPTION:Featuring Rajita Menon\n\nPart of the PhD Final Oral Exams.\n\n
	Dissertation Committee:  Kirill Korolev\, Daniel Segre\, Kevin Black\, Rama
	 Bansil. Shyam Erramilli\n\nAbstract:\n\nComplex communities of microbes ac
	t collectively to regulate human health\, provide sources of clean \nenergy
	\, and ripen aromatic cheese. The efficient functioning of these communitie
	s can be directly \nrelated to competitive and cooperative interactions be-
	 tween species. Physical constraints  and \nlocal environment affect the st
	ability of these interactions. Here we explore the role of spatial \nhabita
	t and interaction networks in microbial ecology and human disease.\nIn the 
	first part of the dissertation\, we model mutualism to understand how spa- 
	tial  microbial \ncommunities  survive  number fluctuations in physical hab
	itats.  We explicitly account for the \nproduction\, consumption\, and diff
	usion of public goods in a two-species microbial community. We \nshow that 
	increased sharing of nutrients breaks down coexistence\, and that species m
	ay benefit from \nmaking slower-diffusing nutri- ents. In multi-species com
	munities\, indirect and higher order \ninteractions may affect community fu
	nction. We find that the requirement for spatial proximity \nseverely re- s
	tricts the network of possible microbial interactions. While cooperation be
	tween two \nspecies is stable\, higher-order mutualism requiring three or m
	ore species succumbs easily to number \nfluctuations.  Additional cyclic or
	 reciprocal interactions between pairs can stabilize multi-species communit
	ies.\n\nInter-species interactions also affect human health via the human m
	icrobiome: micro- bial \ncommunities in the gut\, lungs and skin. In the se
	cond part of the dissertation\, we use machine \nlearning  and statistics t
	o establish links between microbiota abundance and composition\, and the \n
	incidence of chronic diseases.   We  study the gut fungal profile to probe 
	the effects of diet and \nfungal dysbiosis in a cohort of Saudi children wi
	th Crohn’s disease.\nWhile statistical microbiome  studies established th
	at each disease phenotype is as- sociated with \na distinct state of intest
	inal dysbiosis\, they often produced conflicting results and identified a \
	nvery large number of microbes associated with disease.  We show that a han
	dful of taxa could drive \nthe dynamics of ecosystem-level abundance change
	s due to strong inter-species interactions. Using \nmaximum entropy methods
	\, we propose a simple statistical approach (Direct Association Analysis or
	 \nDAA)  to account for interspecific interactions. When applied to the lar
	gest dataset on IBD\, DAA \ndetects a small subset of associations directly
	 linked to the disease\, avoids  p- value inflation \nand identifies most p
	redictive features of the microbiome.
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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