Bioinformatics

Building Biological Tool-Use Agents: Architecture and Patterns The vision of agentic omics — autonomous AI systems that orchestrate biological discovery — depends on a deceptively simple capability: tool use. An agent that can reason about biology but cannot access BLAST, AlphaFold, or single-cell analysis pipelines is like a biologist who understands theory but has never touched a pipette. This post provides a practical architecture for building biological tool-use agents. We cover the essential tool inventory, the unique error-handling challenges of biological data, prompt engineering patterns for biological reasoning, and a reference architecture based on the ReAct (Reason + Act) loop. This is the “how-to” companion to Post 13’s conceptual overview and Post 14’s vision of agentic omics. ...

When a new foundation model in computational biology is released, the accompanying paper inevitably features tables of bolded numbers demonstrating state-of-the-art performance. Whether it is predicting protein structures or annotating single-cell data, the claims are often spectacular. But how do we truly know if these AI systems work in ways that matter to biology, rather than just optimizing arbitrary computational metrics? For the vision of Agentic Omics to become reality—where autonomous agents orchestrate models like AlphaFold and DNABERT-2 to drive drug discovery—we need a rigorous understanding of when these models succeed, when they hallucinate, and when their benchmarks deceive us. Claims of AI breakthroughs are only as strong as their evaluation methodologies. ...

The bottleneck for AI in computational biology is rarely a shortage of sophisticated models; it is the sheer difficulty of making biological data AI-ready. The “Agentic Omics” vision—where autonomous AI agents orchestrate domain-specific models to accelerate drug discovery—fundamentally rests on the assumption that these agents have access to standardized, clean, and computable data. In this post, we explore the unglamorous but critical foundation of omics AI: the data infrastructure. We trace the journey from raw sequencing reads to the structured tensor formats required by modern foundation models, exploring the evolving standards, the scale of the challenge, and how cloud infrastructure is adapting. ...

In the first post of this series, we mapped the omics landscape: genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics. The next question is obvious: why did AI suddenly get so good at several of these fields at once? The short answer is that biology turned out to be unusually compatible with the same family of models that transformed natural language processing. DNA, RNA, proteins, and even single-cell expression matrices are not “language” in any literal sense, but they are structured symbol systems with long-range dependencies, rich context, and vast quantities of unlabeled data. That is exactly the setting where self-supervised foundation models thrive. ...