AI in service of biology.
Most of evolutionary biology is a data problem. Five projects in the lab use AI agents to read the literature, pull structured data out of it, and help with the comparative work that follows. The work includes literature mining, a private lab-management workspace, and experiments in agent-assisted research. Researchers review the results and decide what is useful.
The TraitTrawler and Tealc source repositories are open-source under MIT. They are worked examples that researchers can adapt to their own data, tools, and institution; the lab's private operational records are separate.
AI handles search, extraction, and verification at scale. Researchers supply the questions, the skepticism, and the judgment.
Projects
The projects below span literature mining, lab management, open publishing, and tools for teaching and research.
TraitTrawler A literature-mining agent pointed at any trait, any clade Searches four sources, cascades through a 12-source PDF retriever, and requires double-entry verification before a row is written.
A general-purpose pipeline for building trait datasets from the primary literature. It starts from a keyword search across PubMed, OpenAlex, bioRxiv, and Crossref, retrieves full-text PDFs, and enforces a grounding invariant: every extracted claim must appear verbatim in the cited page of a SHA256-hashed source PDF before the row is written. TraitTrawler generalizes from version one, which was built only to collect karyotype data, to any trait at any phylogenetic scale.
Lab management A status workspace for the PI Projects, people, commitments, and decisions, with detail available when it is needed.
The status monitor is the PI's main working interface. It brings together project updates, next actions, student meetings, and work awaiting a decision. Research, mentoring, teaching, funding, administration, and service remain distinct areas, so the PI can focus on one part of the lab at a time.
Jack, the chief of staff, reports to Heath and coordinates three specialist roles: Sam handles research and funding substance; Daniel supports people, mentoring, and teaching; Tealc handles operations and reliability. All four share one local runtime, project memory, task ownership, and completion records. The monitor brings their useful results together with the PI's own updates.
The four-role organization is installed; important-paper delivery and external schedule consolidation remain in progress. Teaching support is currently on demand. We evaluate useful follow-through and reliable records: a scheduled job or a polished draft does not by itself establish a useful result.
Agent-readable lab Everything we publish is meant to be readable by other researchers' agents llms.txt, llms-full.txt, JSON exports for every database, JSON-LD on every page, an open /data directory.
If AI tools can read our work, they can use it to help other researchers. So we publish everything in formats agents can consume: llms.txt and a longer llms-full.txt as a single-file snapshot of the whole site, JSON exports of every database (CUREs karyotypes, tau, news, publications), structured JSON-LD on every page, and an open data/ directory. Open formats are a form of contribution, and they should be standard practice for any field that wants its work to still be useful in ten years.
Population genetics simulator A Wright-Fisher teaching tool built through AI-assisted coding Drift, selection, mutation, migration, bottlenecks. A test of what a coding agent can produce without manual intervention.
An interactive Wright-Fisher simulator that supports drift, selection, mutation, migration, and bottlenecks. We built it almost entirely through AI-assisted coding, both as a teaching tool for our classes and as a test of what a coding agent can produce when the task is carefully scoped and each output is reviewed.
Teaching
Courses and curricula at Texas A&M that give biology students practical fluency with AI tools, and the skepticism to evaluate the outputs.
Biology & AI CURE A course where every student runs an original evolutionary biology project Course-based undergraduate research built around phylogenetic comparative methods and AI-assisted analysis.
A course-based undergraduate research experience where each student picks a clade, extracts data with agents, runs comparative analyses, and writes up the results. The spring 2026 cohort wrapped up this spring and several of their projects are tracking toward publication.
AI in Biology concentration A formal 10-credit concentration in the TAMU Biology BS and PhD programs Skills that transfer across tools, not just familiarity with whatever is current.
A concentration built with Texas A&M Biology to give students a formal credential alongside their degree. Required courses cover AI fundamentals, computational biology, data literacy, and critical evaluation of model outputs. Designed so the curriculum ages well as the tools change.
AI tools and prompting guides Practical guides for biologists Literature review, data analysis, coding assistance, writing workflows.
Practical guides to AI tools and prompting techniques written for biologists. They cover literature review, data analysis, coding assistance, and writing workflows. Everything we use internally to train students is online.
Principles
A few commitments we hold ourselves to. Some are guardrails, some are the reason we do this work in the first place.
01 Validate before trusting Every AI output is verified before it enters a publication, dataset, or decision.
Every AI output is verified before it enters a publication, dataset, or decision. The form of verification (computational check, statistical test, expert review) depends on the task, but some form of verification is always required.
02 AI amplifies effort; researchers supply judgment Search, extraction, reformatting, summarizing, consistency checking: good uses. Deciding what to conclude stays with the researcher.
Search, extraction, reformatting, summarizing, and consistency checking are good uses of AI. Deciding what question is worth asking, whether a result makes biological sense, and what to conclude: that stays with the researcher.
03 Document how the result was produced Prompts, model versions, and pipeline configurations belong in the methods section.
Reproducible science requires knowing not just what AI generated, but what it was asked, with what model, and under what constraints. Prompts, model versions, and pipeline configurations are part of the methods section.
04 Characterize failure modes, not just capabilities Testing the limits of a tool is as valuable as deploying it successfully.
We care as much about documenting where AI reasoning breaks down as demonstrating what it can do. Systematically testing the limits of a tool is as scientifically valuable as deploying it successfully. The lead-investigator project is designed around this idea.
05 Make domain knowledge explicit A general-purpose model on a specialized problem gives generic results.
A general-purpose AI applied to a specialized scientific problem gives you generic results. Getting something useful means putting the expert knowledge into your prompts, constraints, and validation rules, not assuming the model already has it.
06 Build for the commons Tools, data, and ideas leave this lab in formats other people can pick up and run with.
The point of science is to add to what we collectively understand. Tools, data, and ideas leave this lab in formats other people (and the AI tools they use) can pick up and run with. We are not the last people who will work on these questions, and we want whoever comes next to be better equipped than we were.
We think of this as a release, not a revolution. An incremental but real change in how science gets done, rolled out carefully: agent-readable data, validated pipelines, honest documentation of what broke, and students who know how to use the tools.