GENOMI: A Breakthrough System for Robust Genetic Interaction Discovery, Revolutionizing Biological Insight For the Discovery of Novel Gene Regulatory Networks and Application of Gene Regulatory Networks to Industrial Level Genetic Engineering and Vaccine Development

In order to resolve crucial global issues, the widespread application of genetic engineering at an industrial level is key. However, the majority of synthetically engineered strains fail at the industrial level due to disruptions in gene regulation. This stems from a lack of understanding and usage of gene regulatory networks (GRNs), which control cellular processes and metabolism. Effective manipulation of GRNs can improve product yield and functionality significantly. However, current GRN inference tools are extremely slow, inaccurate, and incompatible with industrial scale processes, because of which there are no complete expression based GRNs for any organism. This research proposes a novel computational system, GENOMI, to enable fast GRN inference for integration into industrial scale pipelines. GENOMI consists of two main parts. First, we create a novel mutual information algorithm that replaces traditional sequential inference methods, ensuring compatibility with parallel processing. Second, we integrate a novel GNN architecture based on spectral convolution to efficiently learn global and local regulatory structures. On in silico benchmarks, GENOMI outperforms all industry leaders, achieving a nearly 300% increase in AUPRC compared to the industry leading method, GENIE3. GENOMI also reduced computing time by a factor of 9.5 and was able to perform on a classroom GPU. When applied on a real E. coli dataset, GENOMI not only recovered 98% of existing interactions, but discovered 468 novel candidate interactions, constructing the most complete expression based GRN of E. coli to date, providing a novel biological blueprint for genetic engineers to use at the industrial level.