TY - JOUR
T1 - Probabilistic modeling of microbial metabolic networks for integrating partial quantitative knowledge within the nitrogen cycle
AU - Eveillard, Damien
AU - Bouskill, Nicholas J.
AU - Vintache, Damien
AU - Gras, Julien
AU - Ward, Bettie
AU - Bourdon, Jérémie
N1 - Publisher Copyright:
© 2019 Eveillard, Bouskill, Vintache, Gras, Ward and Bourdon.
PY - 2019
Y1 - 2019
N2 - Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems.
AB - Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems.
KW - Ammonia oxidizing bacteria
KW - Microbial ecology
KW - Modeling
KW - Nitrogen
KW - Probabilistic simulation
UR - http://www.scopus.com/inward/record.url?scp=85064388208&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064388208&partnerID=8YFLogxK
U2 - 10.3389/fmicb.2018.03298
DO - 10.3389/fmicb.2018.03298
M3 - Article
C2 - 30745899
AN - SCOPUS:85064388208
SN - 1664-302X
VL - 10
JO - Frontiers in Microbiology
JF - Frontiers in Microbiology
IS - JAN
M1 - 3298
ER -