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CAAIL

Contributing

Thanks for your interest in adding to the Cellular Agriculture AI Library. This repository is a curated collection of papers, software, datasets, and other resources at the intersection of cellular agriculture and AI. We welcome suggestions from researchers, engineers, students, and anyone working in or learning about the field.

Two ways to contribute

You don’t need to know Git to suggest a resource.

Option 1 — Open an issue (no Git required)

Pick the template that fits and fill it out:

A maintainer will incorporate accepted suggestions. This is the right path if you’re not comfortable with pull requests or just want to flag something quickly.

Option 2 — Open a pull request

Faster to merge, and you get authorship credit in the Git history. Branch from main (e.g. feat/papers-cosenza-2024, docs/add-bioprocess-section), apply the change directly, and open the PR back against main. The format guides below tell you exactly what to edit.

Adding a paper to Papers.md

First decide which kind of paper you’re adding — the file has two homes for entries:

  • Primary research (a paper applying a specific AI method to a specific cell-ag problem) → goes in the matrix + ## References section. Most papers go here.
  • Review, perspective, position paper, or commentary (a paper surveying the field or opining on it rather than applying one method) → goes in the ## Reviews & Perspectives section only, with no matrix cell. See “Adding a review or perspective paper” below.

Primary research

The matrix and the ## References list must be kept in sync:

  1. Pick the next ID. Scan both the ## References list and the ## Reviews & Perspectives section for the highest existing <a id="N"> number and use N+1 (the two sections share one numeric counter). Reference IDs are permanent — please don’t renumber existing entries, since the matrix cells (and any external links) point at them by ID.

  2. Append the reference at the end of the ## References section in APA style:

    <a id="42">42</a> Author, A. B., & Coauthor, C. D. (2024). Title of the paper. *Journal Name, 12*(3), 456–478. https://doi.org/10.xxxx/yyyy
    • Use APA style, with the journal name italicized (*…*) and the DOI as a full https://doi.org/... link.
    • List all authors — don’t abbreviate to “et al.” in the reference list.

  3. If the paper has associated code, add a blockquote on the next line:

    > **Code**: https://github.com/<owner>/<repo>

  4. Add the paper to every applicable matrix cell. For each AI method (row) and research area (column) the paper covers, add [42](#42) to the corresponding cell — comma-separated with any existing entries:

    | [Bayesian Optimization](...) | [2](#2),[3](#3),[42](#42) | ... |

    Current matrix rows: Bayesian Optimization, Deep Learning, GNN, CNN, GAN/VAE, Genetic Algorithms, SVM, Ensemble Learning, K-Nearest Neighbors, Active Learning, LLMs / AI Agents.

    Current matrix columns: Media Optimization, Cellular Engineering, Bioprocess Control, Scaffolding, Sensory Prediction, AI Tooling / Methodology, AI Evaluation & Benchmarking.

  5. If the paper uses an AI method that isn’t yet a row, add a new row. The row label should link to the Wikipedia article for that method (so the link stays stable):

    | [Reinforcement Learning](https://en.wikipedia.org/wiki/Reinforcement_learning) | | | [42](#42) | | |

  6. If the paper covers a research area that isn’t yet a column, see “Adding a new research area” below.

  7. The AI Tooling / Methodology column is for papers about general-purpose AI methods or agent frameworks that don’t yet have a specific cell-ag application (e.g. a paper introducing a biomedical AI agent that could be applied to media optimization). When a follow-up paper applies one of these tools to a specific area, it goes in that area’s column instead.

Before opening the PR, double-check that every matrix [N](#N) you added resolves to a reference entry, and that your new reference is reachable from at least one matrix cell.

Adding a review or perspective paper

Reviews, position papers, and commentaries don’t participate in the matrix. Add them to the ## Reviews & Perspectives section instead:

  1. Pick the next ID (same counter as primary references — see step 1 above).

  2. Append the reference at the end of the ## Reviews & Perspectives section, in the same APA format as primary references:

    <a id="43">43</a> Author, A. B. (2024). Title of the review. *Journal, vol*(issue), pp. https://doi.org/...

  3. Do not add a matrix cell — reviews stay out of the matrix.

If you’re unsure whether a paper is “primary research” or “review/perspective,” err on the side of the matrix when the paper has a clear single contribution (new method, new application) and on the side of Reviews & Perspectives when it surveys multiple methods or zooms out to the field as a whole.

Adding software, datasets, or other resources

Software.md and Databases.md

Use the existing hierarchical structure. Each entry is an H3 link, followed by a one-paragraph summary:

## <Application area>
Short framing paragraph (optional if the section already exists).


### [<Tool or database name>](<canonical URL>)


Summary: One to three sentences describing what it is and, importantly, *how it applies to cellular agriculture* — not just what it does in general.
  • For software, link to the project’s GitHub repo (or canonical home if not on GitHub).
  • For databases, link to the database’s primary canonical home (UniProt, KEGG, ChEMBL, etc.).
  • Group new entries under an existing section if one fits. If none does, add a new ## section.

The Datasets/ directory

Datasets/ is a directory of per-species pages, not a flat file — see Datasets/CLAUDE.md for the full per-page schema. To add a data resource:

  • Pick the page. A species-specific data deposit (e.g. a bovine RNA-seq study) goes on its species page (Datasets/Cow.md, Datasets/Pig.md, …). Cross-species human pretraining corpora and reference GEMs go on Datasets/HumanReference.md; the CHO GEM family on Datasets/CHOReference.md; AI/ML benchmark datasets on Datasets/Benchmarks.md. If no species page exists for your species, propose a new one (mirror an existing sparse stub like Datasets/Goat.md) and add it to the index table in Datasets/README.md.
  • Add a row to the page’s “Complete data inventory” table — Study (linked title), data type, tissue, a short description, dataset size, and area of research. If the deposit fits an existing thematic cluster on that page, mention it in the cluster prose too. A sparse stub page (e.g. Datasets/Goat.md) has no table yet — its “Complete data inventory” section is a placeholder note; when you add the first deposit, replace that note with a table by copying the column headers from a populated page such as Datasets/Cow.md.
  • Link the canonical accession — the NCBI SRA/GEO/PRIDE, Mendeley Data, or Hugging Face landing page. If the source has no usable accession (data on request, supplementary-table-only), keep the entry with an explicit note rather than dropping it.

Picking between Datasets/, Databases.md, and OtherResources.md. CAAIL distinguishes between fixed train-on artifacts and living query/lookup resources:

  1. Train-on artifacts → the Datasets/ directory. ML pretraining corpora, perturbation atlases, downloadable benchmark datasets, individual GEM model artifacts, per-species sequencing deposits.
  2. Query / lookup resources → Databases.md. Repositories, ontologies, spectral libraries, structure / compound / pathway databases.
  3. “Database” in the name, or any directory / registry / tracker → Databases.md — even when the content is people, companies, or regulation rather than scientific data.
  4. Initiatives and programs → OtherResources.md. Research programs, funding mechanisms, and similar non-database, non-tool resources.
  5. Borderline cases → dual-listed. Resources that are genuinely both software and database (e.g. GNPS) get a full entry in their primary-home file and a short cross-referenced entry in the other.

Benchmark placement (Paper + Dataset + Database triangle). AI/ML benchmarks have a distinct artifact shape — a paper, a downloadable eval dataset, and (sometimes) a live leaderboard. Place each aspect in its appropriate file:

  • Paper describing the benchmark → Papers.md with a > **Code**: blockquote anchoring the project’s canonical home.
  • The data (questions / scenarios / spectra / sequences) + any bundled scoring code shipped with the data → Datasets/Benchmarks.md. The bundled scoring code is not a separate Software.md entry — it’s part of the dataset distribution.
  • Live leaderboard or continuously-updated results tracker → Databases.md under “Benchmark Leaderboards & Results Trackers” (per rule 3, trackers belong in Databases.md).
  • Separately-installable evaluation framework that brings its own data (e.g. a framework that scores models against externally-hosted data the framework does not itself distribute) → Software.md. CausalBench is the existing example — it’s a benchmarking framework, not a bundled-data benchmark, so it lives in Software.md and has no Datasets/ entry.

All entries cross-link via the established Companion to [Papers.md ref #N] convention.

Talks.md, Primers/, and OtherResources.md

All three are bulleted lists under category headings:

* [<Title>](<URL>)

Pick the file by medium and intent:

  • Lectures, talks, and webinars → Talks.md (e.g. conference talks, the Broad Institute MIA series). The site renders Talks.md as inline players on /talks/, so prefer canonical YouTube watch URLs.
  • Newcomer onboarding material → the Primers/ directory — field-overview videos, intro primers, and learning playlists aimed at someone new to the field. Cell-ag orientation goes in Primers/CellAg.md; AI/ML learning material goes in Primers/AI.md. These render at /primers/cell-ag/ and /primers/ai/ with videos and playlists embedded inline, so prefer canonical YouTube watch / playlist URLs; internal cross-links to the rest of the library use repo-relative paths (e.g. ../Papers.md, ../OtherResources.md#courses).
  • Everything else educational or contextual → OtherResources.md — courses, books, journal editorials and opinion, ecosystem initiatives and programs, and curated bibliographies / “awesome lists”.

If you’re adding a kind of resource that doesn’t fit an existing ## section in the chosen file, create a new ## section rather than mixing it into an existing list.

Adding a new research area

A research area is a column in the Papers.md matrix, backed by a deep-dive page under ResearchAreas/. To add one:

  1. Create ResearchAreas/<AreaName>.md (PascalCase, no spaces — e.g. ProteinDesign.md). The file should give a short overview of why the area matters to cellular agriculture and what computational/AI techniques are being applied to it. Existing files like MediaOptimization.md are good models.

  2. Add a column to the Papers.md matrix, with the column header linked to your new file:

    | | ... | [Protein Design](./ResearchAreas/ProteinDesign.md) | ... |

  3. Link your new area from the README’s “What’s Inside” section if it stands as a primary entry point. (Not every research area needs to — small/exploratory areas can live only inside Papers.md.)

  4. Backfill matrix cells for any existing references that also apply to your new area.

Citation style

We use APA throughout Papers.md. In short: author last name, initials, year, title, italicized journal/venue, volume(issue), pages, DOI.

Author, A. B., Coauthor, C. D., & Third, E. F. (2024). Title of the paper. *Journal Name, 12*(3), 456–478. https://doi.org/10.xxxx/yyyy
  • Italicize journal/publication names with *…*.
  • DOI as a full https://doi.org/... link (not dx.doi.org, not a bare DOI string).
  • List all authors in the reference; the [N] matrix anchor serves as the in-text abbreviation.

Pull request checklist

Before opening a PR, please confirm:

  • Your branch is off the latest main and uses a <type>/<slug> name (e.g. feat/papers-..., docs/...).
  • All new links resolve (no 404s).
  • For Papers.md changes: every new reference is reachable from at least one matrix cell, and every new matrix [N](#N) resolves to a reference.
  • No existing reference IDs were renumbered.
  • Commit messages follow Conventional Commits (Angular flavor), e.g. feat(papers): add Cosenza 2024 multi-fidelity BO paper.
  • The PR description mentions which research area(s) and AI method(s) the change covers, so reviewers can spot-check the matrix updates.

Licensing of contributions

This repository is licensed under the MIT License. By submitting a contribution (pull request, issue, or other form), you agree that your contribution is offered under the same license, and you confirm that you have the right to do so. We don’t host copyrighted abstracts or full text — please link to the canonical source instead, since third-party content stays under its original license regardless of CAAIL’s.

Questions

If you’re not sure whether a resource fits, how to categorize it, or how to format an entry, open an issue using one of the templates above and a maintainer will help. We’d rather guide you than have you not contribute.

Cite · Content licensed MIT · canonical source on GitHub