An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data.
Microbiome
; 7(1): 118, 2019 08 22.
Article
en En
| MEDLINE
| ID: mdl-31439018
ABSTRACT
BACKGROUND:
The dynamics of microbial communities is driven by a range of interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood. With the increasing availability of high-throughput microbiome taxonomic profiling data, it is now conceivable to directly learn the ecological models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is severely limited by the lack of accurate absolute cell density measurements (biomass).METHODS:
We present a new computational approach that resolves this key limitation in the inference of generalized Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference with an expectation-maximization algorithm (BEEM).RESULTS:
BEEM outperforms the state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM's application to previously inaccessible public datasets (due to the lack of biomass data) allowed us to construct ecological models of microbial communities in the human gut on a per-individual basis, revealing personalized dynamics and keystone species.CONCLUSIONS:
BEEM addresses a key bottleneck in "systems analysis" of microbiomes by enabling accurate inference of ecological models from high throughput sequencing data without the need for experimental biomass measurements.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Interacciones Microbianas
/
Microbioma Gastrointestinal
/
Modelos Biológicos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Microbiome
Año:
2019
Tipo del documento:
Article
País de afiliación:
Singapur