Decoding crop genetics with AI
We’re on a mission to accelerate the development of more productive, sustainable, nutritious & climate-resilient food sources.
We use cutting-edge deep learning and knowledge graph technology to identify and prioritise high value targets for crop gene-editing.
Unlocking the future of food
The world is facing a food security crisis. We must produce more food in the next 50 years than in the previous 10,000 combined - and at a time when climate change is reducing crop productivity.
Gene-editing provides a solution to this challenge. But, identifying which genes to edit and how remains a major bottleneck for gene-edited trait developers.
We find the right genes to edit, prioritising novel leads and working with you to identify effective edits.
Next-generation crops demand next-generation targets
Our ability to introduce edits has outstripped our understanding of what edits to introduce. In silico models for crop trait discovery have been developed for traditional breeding and do not provide the detail on genes and pathways required for editing.
Building on a decade of machine learning innovation in drug discovery, we’re harnessing high-throughput sequencing and biology-aware machine learning to rapidly identify novel, high quality genetic targets for any trait, in any crop.
We analyse all genes in the genome to go beyond ‘low-hanging fruit’, identifying targets with maximal efficacy and minimal undesired pleiotropic effects.
This is what we’re building at Biographica!
“What agbiotech needs to help guide CRISPR use is an efficient discovery platform that tests things in silico with models that recognize genes, metabolic processes, or signaling pathways, and that get strengthened by in vivo testing so they can predict the two or three genomic changes that will provide the trait outcome. Such models could help companies have a massive impact on developing and breeding the produce of tomorrow.”
— Head of Crop Trait & Technology Discovery at Syngenta
Reshaping the crop development funnel
Increased success in gene discovery through:
- Broad in silico screening of potential targets beyond human-biased hypotheses
- Physiology-informed modelling of gene-trait mappings
- Prioritisation of targets according to efficacy & pleiotropy