Summary
Animal and plant microbiome functions can be modulated, and thereby optimized, for sustainable food production. However, the outcome, i.e., the microbial response, can vary greatly depending on (e.g.)Animal and plant microbiome functions can be modulated, and thereby optimized, for sustainable food production. However, the outcome, i.e., the microbial response, can vary greatly depending on (e.g.) the genetic background and developmental stage of the host, and the farming environment. The interactions between the biological process of the host and their microbiome are still only superficially understood, even though microbial interventions have been used for years. This incomplete understanding means that new attempts to improve microbiome functions are both inefficient and costly, and unlikely to hit upon the optimal solutions. An approach that recognizes the intimate biological interactions between host genome and microbiome functions holds the potential to greatly reduce cost and improve the outcome.
To that end, FindingPheno will develop a holistic statistical framework to decipher biomolecular interactions between host and microbiome by combining biological knowledge and state-of-the-art statistical methods: structural causal modelling, variable selection, dimensionality reduction and feature detection. We will then apply the framework to case studies from actual food production systems, using a unique multi-omics data set from three biological systems – chicken, salmon and maize – derived from ongoing research projects. In addition, we demonstrate the utility of the framework to obtain biological insights from publicly available data sets from tomato and bees.
We expect to show how to improve the effectiveness of microbiome interventions in sustainable food production, and simultaneously, we will offer avenues for quick and easy application of this new approach to other relevant biotechnology-based industries, e.g. enzyme production and fermentation.
To that end, FindingPheno will develop a holistic statistical framework to decipher biomolecular interactions between host and microbiome by combining biological knowledge and state-of-the-art statistical methods: structural causal modelling, variable selection, dimensionality reduction and feature detection. We will then apply the framework to case studies from actual food production systems, using a unique multi-omics data set from three biological systems – chicken, salmon and maize – derived from ongoing research projects. In addition, we demonstrate the utility of the framework to obtain biological insights from publicly available data sets from tomato and bees.
We expect to show how to improve the effectiveness of microbiome interventions in sustainable food production, and simultaneously, we will offer avenues for quick and easy application of this new approach to other relevant biotechnology-based industries, e.g. enzyme production and fermentation.
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| Web resources: | https://cordis.europa.eu/project/id/952914 |
| Start date: | 01-03-2021 |
| End date: | 28-02-2025 |
| Total budget - Public funding: | 5 793 085,00 Euro - 5 793 085,00 Euro |
Cordis data
Original description
Animal and plant microbiome functions can be modulated, and thereby optimized, for sustainable food production. However, the outcome, i.e., the microbial response, can vary greatly depending on (e.g.)Animal and plant microbiome functions can be modulated, and thereby optimized, for sustainable food production. However, the outcome, i.e., the microbial response, can vary greatly depending on (e.g.) the genetic background and developmental stage of the host, and the farming environment. The interactions between the biological process of the host and their microbiome are still only superficially understood, even though microbial interventions have been used for years. This incomplete understanding means that new attempts to improve microbiome functions are both inefficient and costly, and unlikely to hit upon the optimal solutions. An approach that recognizes the intimate biological interactions between host genome and microbiome functions holds the potential to greatly reduce cost and improve the outcome.To that end, FindingPheno will develop a holistic statistical framework to decipher biomolecular interactions between host and microbiome by combining biological knowledge and state-of-the-art statistical methods: structural causal modelling, variable selection, dimensionality reduction and feature detection. We will then apply the framework to case studies from actual food production systems, using a unique multi-omics data set from three biological systems – chicken, salmon and maize – derived from ongoing research projects. In addition, we demonstrate the utility of the framework to obtain biological insights from publicly available data sets from tomato and bees.
We expect to show how to improve the effectiveness of microbiome interventions in sustainable food production, and simultaneously, we will offer avenues for quick and easy application of this new approach to other relevant biotechnology-based industries, e.g. enzyme production and fermentation.
Status
SIGNEDCall topic
BIOTEC-07-2020Update Date
27-10-2022
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