Simulation of Biology

CS 7492
Instructor: Greg Turk
Semester: Spring 2021

Time: TBD
Location: Remote Lecture Delivery, Regular Q&A Sessions

Optional Book:
The Origins of Life: From the Birth of Life to the Origin of Language
by John Maynard Smith and Eors Szathmary

Course Description

This course covers a broad array of techniques for computer simulation of biological systems. The course material will draw from biology, artificial life, robotics, computer graphics and other research areas. Some of the course topics include self-replication, artificial chemistry, the spreading of disease, multi-cellular development, simulation of evolution, cellular automata, mass-spring simulators, L-systems for plant development, animal locomotion (walking, swimming, jumping), flocking and herding behavior in groups, predator/prey systems, parasites, and foraging behavior.

Students will carry out a number of programming projects during the course. These projects are designed to give students first-hand experience with a range of simulation methods that are used in biology. Grading for the class will primarily be based on student performance on these projects. Basic programming skills are recommended for students entering the course. Projects will be done using Processing, which is a Java-based programming environment. Prior programming experience in a Java or another C-like language is the best preparation for the course.

No previous background in biology is necessary.

Homework

Homework 1 (Life Cellular Automata) - This first homework is due at the end of the second week of class.

Homework 2 (SIR Model of Infection)

Homework 3 (Reaction-Diffusion)

Homework 4 (Flocking)

Homework 5a (Mass/Spring Locomotion) 5b (Rigid Body Simulation). Pick just ONE of these for your last project!

Tentative Schedule

Week 1
Cellular automata and Game of Life.
Christopher Langton's Self-Reproducing Loops.
Stephen Wolfram's Cellular Automata Classes. Optional: Christopher Langton's Edge of Chaos.

Week 2
Coupled ordinary differential equations and numerical integration.
Agent-based models of interaction.
SIR epidemiological model of disease spread (susceptible, infectious, recovered).

Week 3
Craig Reynolds' flocking of virtual creatures.

Week 4
Metabolic pathways chart1 and chart2.
Tim Hutton's self-reproducing simulated molecules.
Dill's 2D version of protein folding. Optional: Cyrus Levinthal on protein folding complexity.
Classic DNA paper by Watson and Crick. Optional: Stanley Miller's bubbling flask to produce amino acids.
Seeing if genetic code is optimal.

Week 5
Pattern formation by reaction-diffusion.

Week 6
Simulation of lipid micelles .

Week 7
The Genetic Algorithm.

Week 8
Simulation of Sympatric Speciation.
Plant growth simulation with voxel space automata.

Week 9
Plant growth using L-systems.
Creating branching patterns using Laplacian growth.
Simulation of leaf venation.

Week 10
Development papers: Fleischer/Barr, Eggenberger, Furusawa/Kaneko.

Week 11
Evolving virtual creatures from Karl Sims.
Evolving autonomous agents from Frank Dellaert and Randall Beer.

Week 12
Evolution and manufacturing of crawling robots.
Swimming creatures from Tu and Terzopoulos.
Evolved flying creatures.

Week 13
Thomas Ray's Tierra system of evolving programs.
Robert Axelrod and the Iterated Prisoner's Dilemma.

Week 14
Craig Reynolds on Co-Evolution for game of tag.
Ant foraging behavior.

Course Topics

Self-Organization

self-replication (von Neumann, Christopher Langdon, others)

complexity at edge of chaos

Molecules

artificial chemistry

metabolism

molecular hypercycles

RNA folding

DNA codon optimality

Membranes and Cells

membrane formation

cell models

cell cytoskeletons

immune systems

Cell aggregation (courtesy of Kurt Fleischer)

Development

multicellular development

slime mold aggregation

pattern formation

gene cascades/networks

cell simulation of development (Fleischer and Barr)

L-systems for plant development

Plant growth (courtesy of Przemyslaw Prusinkiewicz)

Evolution

evolution

speciation

Dawkins on major events in evolution

genetic algorithms

blind watchmaker

co-evolution (Karl Sims, Craig Reynolds, Danny Hillis)

sexual selection

Locomotion

modes of locomotion

Braitenberg vehicles

evolution of walking and hopping motion (Karl Sims)

swimming (Terzopoulos)

Walking simulation (courtesy of Karl Sims)

Physics Simulation Techniques

partial differential equations (PDE's)

reaction-diffusion

cellular automata (life, spiral waves, etc.)

mass-spring systems

Tentacle motion (courtesy of Andrew Cantino)

Multi-Organism Interaction

communication

Prisoner's dilemma, tit-for-tat

predator/prey

flocks, schools, swarms

ant foraging

parasites

digital creatures (Thomas Ray)

Flocking with collision avoidance (courtesy of Craig Reynolds)

Go to Greg Turk's Home Page.