Systems Biology in Ecology & Agriculture
In recent years, life sciences are being revolutionized by the generation of large data sets, specifically by overloads of genomics.
The explosion of available information and the challenges in its interpretation are referred to as “big data” science.
In medical sciences for example, personal genomics is considered a key enabler for predictive medicine.
Similarly, metagenomics data provides a unique "personalized" signature for ecological samples and the specific exchange fluxes between microbial members.
Metabolic networks approaches allow the processing of 'big data' genomic information into predictive models and are considered central for microbial community engineering.
Our research aims at harnessing microbial function for the service of ecology & agriculture through the educated design of communities.
To this end, we apply and develop computational models for predicting and understanding the networks of interactions formed within microbial communities by analyzing meta/genomics data. Using our tools we can delineate trophic dependencies, exchanges, competitive and cooperative interactions within natural microbial communities and use simulations for predicting potential routes for the optimization of predefined functions.
The research in the group focuses on the activity of microbial communities in agricultural soil and is targeted for harnessing genomic approaches towards promoting sustainable solutions in agriculture practice.
Research projects include
Promoting enhanced degradation of herbicides in soil
Deciphering microbial functions in amendment-based solutions for soil-borne disease suppression
Delineating tri trophic networks between crop plants, sap-feeding pests and their microbial symbionts
Listen to the podcast (Hebrew):
See our publications:
Wrote on us:
Microbial Consortium Design Benefits from Metabolic Modeling.
Projects we take part in:
Model Farm for Sustainable Agriculture.