From Target Molecule to Simulation-Ready Digital Microbial Life

Engineering microorganisms to produce target molecules is a central goal of synthetic biology, yet designing a simulation-ready microbial system remains labor-intensive and dependent on expert knowledge. Although large language model (LLM)-based scientific agents can automate parts of this workflow, generating executable biological designs and evaluating their quality remain challenging. We propose digital microbial life design as a new end-to-end problem of transforming a target molecule into a simulation-ready microbial design. To solve this problem, we introduce Origo, a multi-agent system that structures the design workflow using an expert-designed primitive task pool. Each primitive task is bound to specialized MCP tools, while hierarchical multi-agent coordination assigns tasks to specialized agents and coordinates their execution to construct GEMs, and identify enzyme candidates. To address the corresponding evaluation gap, we introduce DLBench, a benchmark for evaluating digital microbial life design through network-level executability and reaction-level biological plausibility, with 40 target products and 22 rate-limiting enzymes. Experiments on DLBench show that Origo outperforms general-purpose LLMs and biomedical agents across both tasks. This work provides a foundation for AI-driven microbial design, downstream simulation, and hypothesis generation for wet-lab validation.