Nov 6, 2018

Parameter Identifiability of the Generalized Lotka-Volterra Model for Microbiome Studies

BioRxiv : the Preprint Server for Biology
Christopher H RemienBenjamin J Ridenhour

Abstract

Background: Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research; predicting how populations change over time, determining how manipulations of microbiomes affect dynamics, and designing synthetic microbiomes to perform tasks are just a few examples. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model. Results: We used structural identifiability analyses to determine the extent to which time series of relative abundances can be used to parameterize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely ide...Continue Reading

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Mentioned in this Paper

Study
Size
Research
Nucleic Acid Sequencing
Human DNA Sequencing
Microbial Interactions
Objective (Goal)
Sequencing
Microbiome
Sequence Determinations, DNA

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