Evolution, studied broadly, built for AI.
We are a biology lab at Texas A&M. We study how evolution works across the tree of life: genome structure, sex chromosomes, karyotypes, morphology, behavior, adaptation. Running underneath all of that is a layer of AI agents we've built so a single lab can do comparative work nobody could reach alone, and a deliberate effort to put undergraduates on real research problems from their first semester. Darwin gave us the framework. We build what comes on top.
What we work on
Five overlapping programs. The through-line is asking quantitative questions across the tree of life that the traditional, single-lab, single-trait approach can't handle.
Genome structure & karyotype evolution Why chromosome number and arrangement evolve the way they do 63,682 karyotypes, six databases, and a 2026 preprint showing dysploidy rates vary 844-fold across eukaryotes.
This is the part of the lab with the longest continuous track record. We build machine-readable karyotype databases, run phylogenetic comparative methods on them, and ship the analytical tools as R packages. The current headline: birds, long treated as the textbook case of chromosomal stasis, sit above the global median once microchromosome dynamics are resolved. Stasis tracks life history, not taxonomic group.
Sex chromosomes & sexual antagonism Birth, degeneration, and turnover of sex chromosomes across animals From ordinary autosomes to the X, Y, Z, W, and the mating-type systems in between.
Beetles alone display more sex chromosome systems than any other animal order. We use that diversity to study what drives sex chromosome birth, how fast Y chromosomes degenerate once they stop recombining, and what sexually antagonistic alleles do to genome architecture in the process. Methods work draws on dosage-compensation data, comparative cytogenetics, and population genetic theory.
Selection, drift & adaptation How much of what we see is selected, and how much is just drift? Two 2024 papers suggest drift is doing more of the work than we usually give it credit for.
Comparative work across Coleoptera and Carnivora found that effective population size (via range size) predicts karyotype evolution rates better than any measure of selective pressure. That's uncomfortable for classic adaptive accounts of chromosome evolution and increasingly useful for thinking about how evolution actually proceeds in shrinking populations of conservation concern.
Epistasis & quantitative genetics Wright was right: epistasis is the rule, not the exception 1,600+ line-cross datasets analyzed with the SAGA framework.
We re-analyzed over 1,600 line-cross datasets spanning plants and animals using an information-theoretic framework (SAGA) we built for this kind of work. Epistasis was detected in the majority of crosses. That settles a decades-old Fisher-vs-Wright debate empirically, and it reshapes how we think about the response to selection.
Organisms beyond genomes Morphology, behavior, and the genetics of domestication Iridescent scarab beetles, Betta aggression, Solanum crosses, and freshwater crab invasions.
Not all of the work is about chromosomes. Students in the lab are working on the evolution of male combat morphology in scarabs, the genetic architecture of aggression in Betta splendens, morphological evolution in Solanum hybrids, and the repeated evolution of freshwater living in Brachyuran crabs. Each of these feeds back into the comparative and genomic work.
How we work with AI
Three projects running right now, plus a deliberate choice about how we publish everything else.
TraitTrawler A literature-mining agent pointed at any trait, any clade Search four sources, 12-source PDF cascade, double-entry verification before a row gets written.
A general-purpose pipeline for building trait datasets from the primary literature. Starts from a keyword search across PubMed, OpenAlex, bioRxiv, and Crossref. Retrieves full-text PDFs through a 12-source cascade. Extracts structured trait data with mandatory double-entry verification (two independent extractions must agree). Writes to CSV only when validated. Generalizes the cytogenetics agent that built our CUREs dataset to any trait on any phylogenetic scale.
AI as lead investigator An agent running a project end-to-end. We expect it to fail. Claude-powered, carrying a real research problem in claw evolution from hypothesis to manuscript.
We handed a complete research problem to a Claude-powered agent and asked it to run the project: form hypotheses, select comparative methods, run analyses, draft a manuscript. We expect it to fail, and the failure is the point. Each breakdown teaches us how to design the next version. The goal is not a robot PI. It's an agent that works at our side as scientific collaborator, programmer, and an army of students and postdocs, doing meaningful work when the task is correctly scoped, tested, and examined.
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; structured JSON-LD on every page; and an open data/ directory. This is how we think science should be published in the 2020s, full stop.
The best version of AI in biology isn't a black box that answers your question. It's an agent that reads the same papers you do, does the grunt work you shouldn't have to, and leaves a trail you can audit.
How we teach
Undergraduates run real projects here. They are named authors on the papers. A large share of our students publish before they graduate.
Biology & AI CURE A course where every student runs an original evolutionary biology project 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 their results. The spring 2026 cohort is in progress and several of their projects are tracking toward publication.
AI in Biology concentration A 10-credit formal concentration in the TAMU Biology BS and PhD programs Skills that transfer across tools, not just familiarity with whatever is current.
A concentration we 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 it ages well as the tools change.
Open teaching materials Guides for grad students, prompting, experimental design, phylogenetics Everything we use internally is online.
Grad 101, BIOL 682 readings, the R seminar archive, the AI-tools guide, the prompting guide, phylogenetics 101, experimental design notes, and a growing reading group archive. If we use it to train someone, it's on the site.
Latest from the lab
April 2026 · bioRxiv preprint Dismantling chromosomal stasis across the eukaryotic tree of life Copeland, McConnell, Barboza et al.
63,682 karyotypes, 55 eukaryotic clades. Dysploidy rates vary by a factor of 844. Birds, long treated as the textbook example of chromosomal stasis, exceed the global median once microchromosome dynamics are accounted for. Chromosomal stasis tracks life history, not taxonomic group.
All publications → · All lab news →
Who we are
Principal investigator Heath Blackmon, Associate Professor of Biology Associate Department Head for Graduate Studies. PhD with Jeff Demuth (UTA, 2015). Postdoc with Emma Goldberg and Yaniv Brandvain (Minnesota).
I opened the lab in 2017. My background is in theoretical population genetics and cytogenetics, and most of my field time is spent chasing scarab beetles. I also chair the TAMU EEB interdisciplinary doctoral program.
The team 20 current members, about half undergraduates 6 PhD students, 1 post-bacc, 2 research staff, 11 undergraduate researchers.
Nobody in this lab is working on a warmed-over PI-assigned project. Students (including first-year undergraduates) are put on questions where their contribution is real and authorable. The alumni list is long now, and it's the thing I'm most proud of.
Working with us
Prospective graduate students How we decide on the next cohort and what we look for Applications go through the TAMU Biology or EEB programs.
If the research program here lines up with what you want to do, read the expectations page and then email me. Strong computational grounding helps but is not required. Willingness to read primary literature, break things, and recover is required. TAMU EEB runs on its own application calendar; TAMU Biology on the department calendar.
Undergraduates at TAMU There are at least two ways in The Biology & AI CURE, and direct project-based research.
Enroll in the CURE for a course-based way to join, or email directly with a short pitch for a project you'd like to work on. We accept undergraduates every semester. Lab member expectations (10 hrs/week, lab-meeting participation, taking ownership of projects) apply from day one.
Collaborators and data users The data are yours. The code is yours. Cite us and go. If you want TraitTrawler pointed at your clade, let's talk.
Every dataset the lab produces is machine-readable and licensed for reuse. If you want to collaborate on a project that uses one of these agents (especially TraitTrawler on a new clade or trait) we are actively looking for partners. Email is the fastest path.