Biographica uses multi-modal machine learning to decode crop genetics and accelerate the development of resilient food sources. The platform identifies and prioritizes high-value genetic targets for gene-editing by analyzing targets within their full biological context. This process streamlines the agbiotech discovery pipeline, increasing success rates while reducing the time and cost associated with trait development.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Identifying optimal genetic targets for crop gene-editing is challenging due to the complexity of plant genomes and the limitations of traditional in silico models, leading to inefficiencies, high costs, and potential off-target effects. Existing methods often focus on well-studied genes, overlooking novel targets with potentially greater efficacy and minimal pleiotropic effects.
Solution
Biographica leverages biology-aware machine learning and high-throughput sequencing to identify and prioritize high-value genetic targets for crop gene-editing. The platform analyzes all genes in the genome, going beyond readily apparent targets to discover novel leads with maximal efficacy. By integrating in silico modeling with in vivo validation, Biographica enhances the precision of genomic modifications, enabling the development of more productive, sustainable, and climate-resilient crops. The system prioritizes targets based on their predicted impact on desired traits and potential for unintended pleiotropic effects, streamlining the gene-editing pipeline and increasing the success rate of crop improvement efforts.
Target Audience
Biographica's primary customers are crop trait developers, agbiotech companies, and agricultural research institutions seeking to improve the efficiency and success rate of their gene-editing programs.
Features
- Genome-wide in silico screening to identify novel genetic targets beyond human-biased hypotheses
- Physiology-informed modeling of gene-trait mappings for accurate target prediction
- Prioritization of targets based on efficacy and potential pleiotropic effects
- High-throughput sequencing data integration for comprehensive genomic analysis
- Biology-aware machine learning algorithms trained on extensive crop genetics datasets