A collection of resources that apply formal systems to model biological processes. Some easily accessible books to understand the general approaches in this field are also included.
McCullouch and Pitts study of neurons in biology birthed the pivotal paper that kickstarted A.I., regular languages, and digital computers. It was the only paper included in the EDVAC report by John Von Neumann.
An interactive demo of their model is available here: https://justinmeiners.github.io/neural-nets-sim/
Reaction-Diffusion Systems
For animations, checkout: https://chemlambda.github.io/collection.html
Correction to the article: https://www.nature.com/articles/s41598-019-51082-3
Tweet thread on it here: https://twitter.com/kaznatcheev/status/1102973539634892800
Neurocomics (Beginner)
Introducing Evolution (Beginner)
Introducing Genetics (Beginner)
Introducing Epigenetics (Beginner)
Complexity: A Very Short Introduction (Intermediate)
Games of Life (Intermediate)
A really good survey of books in complexity science here.
- Rashevsky and Rosen
Rashevsky devised the primary model of neural networks which was interpreted using the boolean language by McCullough/Pitts. His student Robert Rosen went on to work on using Category Theory to model biology. These works need to be catalogued here. Herbert Simon was also his student.
There could also be more possible works from the conference where McCullough presented his work and met Pitts.
- Ilya Pregorine’s Work
- Gregory Chaitin’s work
- Stochastic CFG for RNA
- Zuse-Fredkin thesis / Digital Philosophy
Think I need to mention the work of Zuse-Fredkin thesis along with Ulam-Neumann model.
- Knot Theory for Protein Molecules
- Wang Tiles and DNA
- Molecular Networks and Monomial Ideals
- Computational Modeling, Formal Analysis, and Tools for Systems Biology: Survey
- Luca Cardelli
- Morphism of reaction networks that couple structure to function
Ready is a program for exploring continuous and discrete cellular automata, including reaction-diffusion systems, on grids and arbitrary meshes.
A beautiful piece of writing from James Somers on the rich tapestry of the field of biology and what user interface design might have to offer the field.
A really nice series of articles on the biological aspects of neural networks by Jack Terwilliger