Microbiology

Microbiology – 61,000 plant-associated microbes and still growing

Through AMS, BioConsortia has established a large resource base of fully characterized plant-associated bacteria and fungi.

Classical microbiology and modern genomic tools are used to build a profile of each microbe’s potential value for use in a BioConsortia product. Root colonization is also assessed using a unique microbial tagging and in-planta imaging system. This deep understanding feeds into an in-house database and predictive modelling pipeline aiding the selection of leads for plant based screening.

Recognizing that finding the perfect microbe isn’t always achievable, a suite of molecular tools is used for strain optimization through scar-less gene-editing strategies.

Microbial Genomics – uncovering hidden potential


Genomic analysis can provide insight into microbial potential. Genes typically grouped in the following categories:
• Nutrient acquisition
• Root colonization
• Bioactive metabolites
• Abiotic stress resistance
• Plant growth promotion
• Pathogenicity and safety
Combining data from genomics, microbiome, microbial phenotyping, and in planta assays for predicting the best leads and consortia

Tagged for success – a window into root colonization

BioConsortia’s microbe tagging and root colonization pipeline brings together innovative, high-throughput fluorescent marker tagging with rapid root system visualization to shine light on how isolates interact with plants. This sophisticated tool identifies robust plant colonizers under multiple environmental conditions – factors essential for in-field performance of microbial products.

Consortia by design – using science over guesswork

State-of-the-art methods in data science are applied to untangle the convoluted relationship between microbial communities and plants.

Microbiome data collected during the AMS process is leveraged alongside trait information from individual isolates to build predictive models of beneficial and antagonistic microbial associations.

This machine-learning approach takes the guess-work out of consortia re-construction, increasing the probability of success while reducing the resources required for downstream screening.