Genesis: Towards the Automation of Systems Biology Research

• Published in: arXiv.org 2024-09
• Main Authors: Tiukova, Ievgeniia A, Brunnsåker, Daniel, Bjurström, Erik Y, Gower, Alexander H, Kronström, Filip, Reder, Gabriel K, Reiserer, Ronald S, Korovin, Konstantin, Soldatova, Larisa B, Wikswo, John P, King, Ross D
• Format: Article
• Language: English
• Subjects: Automation; Biology; Bioreactors; Closed loops; Learning; Mass spectrometry; Robots; Scientists; Software agents
• ISSN: 2331-8422

The cutting edge of applying AI to science is the closed-loop automation of scientific research: robot scientists. We have previously developed two robot scientists: `Adam' (for yeast functional biology), and `Eve' (for early-stage drug design)). We are now developing a next generation robot scientist Genesis. With Genesis we aim to demonstrate that an area of science can be investigated using robot scientists unambiguously faster, and at lower cost, than with human scientists. Here we report progress on the Genesis project. Genesis is designed to automatically improve system biology models with thousands of interacting causal components. When complete Genesis will be able to initiate and execute in parallel one thousand hypothesis-led closed-loop cycles of experiment per-day. Here we describe the core Genesis hardware: the one thousand computer-controlled \(\mu\)-bioreactors. For the integrated Mass Spectrometry platform we have developed AutonoMS, a system to automatically run, process, and analyse high-throughput experiments. We have also developed Genesis-DB, a database system designed to enable software agents access to large quantities of structured domain information. We have developed RIMBO (Revisions for Improvements of Models in Biology Ontology) to describe the planned hundreds of thousands of changes to the models. We have demonstrated the utility of this infrastructure by developed two relational learning bioinformatic projects. Finally, we describe LGEM+ a relational learning system for the automated abductive improvement of genome-scale metabolic models.