Undergraduate foundations
DNA Computing Foundations
From Molecular Information to Learning SystemsAn undergraduate introduction to biological information, molecular algorithms, gene regulation, and genomic foundation models.
DNA2AGI Open Library
Two books. One evidence ladder.
Learn DNA computing as a field. Study DOGMA as a testable architecture.
The foundational textbook and research monograph share terminology, mathematics, citations, and reproducible publishing, while keeping established science separate from MapleAI hypotheses.
2independent books
19long-form chapters
12 hguided reading
159ksource words
Advanced undergraduate and research
DOGMA
DNA-Organized Genomic Model ArchitectureA research monograph on regulated non-transformer sequence models, recursive learning, and evidence-gated evolution.
Book I / Established foundations
From molecular alphabet to measurable computation
Each step changes the scientific question: chemistry stores information, interactions execute rules, and models learn statistical structure.
Biological fact
Four bases store a sequence
A, T, C, and G form an addressable polymer whose direction and complement carry structure.
5'-ATCG-3'Research Method
Biology first. Claims last.
- 01Observe the substrate
Separate molecular mechanisms from metaphors about computation.
- 02Formalize the operation
Translate pairing, regulation, and recurrence into precise algorithms.
- 03Implement the smallest test
Compare each mechanism with strong sequence-model baselines.
- 04Freeze evaluation
Measure transfer, retention, calibration, safety, and uncertainty.
- 05Publish failures too
Promote only reproducible gains and preserve rejected candidates.
10 reading units in DNA Computing
Start Here
2 unitsPrefacePrefaceDNA is simultaneously a molecule, a durable information medium, and the substrate of a distributed chemical control system. Those roles make it a remarkable subject for computer scientists, but they also create a common source of confusion: biological information processing, wet-lab DNA computation, and machine lear...1 minRoadmapReader’s RoadmapNucleotides, base pairing, information content, replication, transcription, translation, and biological error correction1 min
Biological Information
3 unitsChapter 1The Code of Life — DNA as an Information SystemIn April 1953, James Watson and Francis Crick published a nine-hundred-word paper that changed every science it touched [ Watson and Crick , 1953 ]. The double-helical structure of deoxyribonucleic acid (DNA) revealed that hereditary information is encoded in a molecular sequence—a string over a four-letter alphabet...28 minChapter 2A History of DNA Computing — From Adleman to Molecular ProgrammingOn a November day in 1994, Leonard Adleman walked into his laboratory at the University of Southern California and solved an instance of the Hamiltonian path problem—not on a computer, but in a test tube. Using carefully designed DNA oligonucleotides, enzymatic reactions, and gel electrophoresis, he demonstrated tha...37 minChapter 3Gene Regulation as ComputationEvery cell in a human body contains the same genome—approximately 3.2 billion base pairs, encoding roughly 20,000 protein-coding genes. Yet a neuron looks and functions nothing like a liver cell, a muscle fiber, or an immune cell. The difference is not in what genes are present , but in which genes are expressed —an...27 min
Learning Systems
3 unitsChapter 4Machine Learning Foundations for Biological AIParts I established that biology encodes, regulates, and evolves information through sophisticated computational mechanisms. This chapter begins Part II by building the machine learning foundations needed to translate those biological principles into working AI systems. We cover neural networks, optimization, repres...43 minChapter 5Transformers and Large Language ModelsChapter 4 established the foundational building blocks of machine learning: neural networks, optimization, embeddings, and evolutionary algorithms. This chapter focuses on the single most consequential architecture of the modern deep learning era: the transformer . We trace its development from the attention mechani...37 minChapter 6Biological Foundation ModelsIn the previous chapters we learned how neural networks work (Chapter 4 ) and how transformers power large language models (Chapter 5 ). Those tools were built for human language—English sentences, code, and web text. But in the last five years, researchers have taken the same ideas and pointed them at biology: at p...47 min
Reference
2 unitsAppendix AMathematical FoundationsThis appendix collects the minimum mathematics needed to read the DOGMA chapters and evaluate the experiments. It is a reference, not a substitute for a course in linear algebra, probability, or statistics.4 minAppendix BGlossaryArtificial general intelligence. There is no universally accepted test. This book treats AGI as a research target requiring broad, transferable capability, not as a label for the current DOGMA model.4 min