Biomorphic Computing

Biomorphic Computing Professor: Bill Tomlinson Tuesday 2:00-4:50pm Winter 2004 CS 189

Week 1 • Introductions • Syllabus • Biology • Biomorphic Computing • Game of Life • Lab Time

Introductions • Name • Program • How much biology and computer science experience / relevant classes taken. • (note cards)

Syllabus • Hand out • Go over • Questions?

Breaks • I’ll try to remember to take breaks each class (every 1-1.5 hours), but if I forget, please remind me!

Reading/work for the whole week • Spread it out over the whole week. • Leave time to ask questions.

Assignments • I am interested that you understand why code works, rather than simply that it works. Therefore, please comment your code thoroughly on the assignments. • Many code samples similar to the assignments can be found online. You are welcome to use these as reference, but please don’t cut-and-paste them.

Final Projects • Innovative computational implementation based on some aspect of a biological phenomenon that has never before been explored. • Readings - We’ll go over search tools. You find the readings. • Three presentations - proposal, prototype, final. • Keep your eye out (both in our class work and in the rest of your life) for biological phenomena that interest you.

Assignment for next week • Game of Life programming assignment (handed out later in class) • Read: Sims, K. 1991. Artificial Evolution for Computer Graphics. Computer Graphics, 25(4), pp. 319-328. (See syllabus for link.)

Questions • Any questions now? • Throughout the quarter, please come to my office hours (Thurs 3-5) or email wmt@uci.edu if you have any questions or just feel like chatting.

Introduction to biology Merriam Webster: • 1 : a branch of knowledge that deals with living organisms and vital processes

Definitions of life • Break up into pairs • Each group come up with three distinct definitions of life. • Take 10 minutes.

Compare definitions • Come up with ways to break each - counter-examples, false positives.

Merriam-Webster’s • 1 a : the quality that distinguishes a vital and functional being from a dead body b : a principle or force that is considered to underlie the distinctive quality of animate beings -- compare VITALISM 1 c : an organismic state characterized by capacity for metabolism, growth, reaction to stimuli, and reproduction

NASA • There is no broadly accepted definition of 'life.' Suggested definitions face problems, often in the form of robust counter-examples. Here we use insights from philosophical investigations into language to argue that defining 'life' currently poses a dilemma analogous to that faced by those hoping to define 'water' before the existence of molecular theory. In the absence of an analogous theory of the nature of living systems, interminable controversy over the definition of life is inescapable. --Cleland, Carol E.; Chyba, Christopher F., Origins of Life and Evolution of the Biosphere, v. 32, Issue 4, p. 387-393 (2002).

NASA(http://afc.gsfc.nasa.gov/tco/biology101_01.htm) All life carry on a common set of processes: • Reproduction - the production of new individuals of each kind of organism • Growth - life grows in size • Nutrition - activities involved in taking in food from the environment, digesting the food and removal of wastes of digestion. • Transport - the movement of material into the life form (cell) and the distribution of material within the cell. • Respiration - chemical activities that release energy from organic molecules for the use of the organism. • Excretion - the elimination of waste products from the organism. • Synthesis - chemical reactions in which molecules combine. • Regulation - the control and coordination of all functions (no wonder we are such natural bureaucrats it is built into the meaning of life)

Biology topics(from Campbell & Reece, 2001) • Computational / engineering implementations for each of these topics?

The Chemistry of Life • The Chemical Context of Life • Water and the Fitness of the Environment • Carbon and the Molecular Diversity of Life • The Structure and Function of Macromolecules • An Introduction to Metabolism

The Cell • A Tour of the Cell • Membrane Structure and Function • Cellular Respiration: Harvesting Chemical Energy • Photosynthesis • Cell Communication • The Cell Cycle

Genetics • Meiosis and Sexual Life Cycles • Mendel and the Gene Idea • The Chromosomal Basis of Inheritance • The Molecular Basis of Inheritance • From Gene to Protein • The Genetics of Viruses and Bacteria • Organization and Control of Eukaryotic Genomes • DNA Technology and Genomics • Genetic Basis of Development

Mechanisms of Evolution • Descent with Modification: A Darwinian View of Life • The Evolution of Populations • The Origin of Species • Phylogeny and Systematics

The Evolutionary History of Biological Diversity • Early Earth and the Origin of Life • Prokaryotes & the Origins of Metabolic Diversity • The Origins of Eukaryotic Diversity • Plant Diversity I: How Plants Colonized Land • Plant Diversity II: The Evolution of Seed Plants • Fungi • Introduction to Animal Evolution • Invertebrates • Vertebrate Evolution and Diversity

Plant Form and Function • Plant Structure and Growth • Transport in Plants • Plant Nutrition • Plant Reproduction and Biotechnology • Plant Responses to Internal and External Signals

Animal Form and Function • Introduction to Animal Structure and Function • Animal Nutrition • Circulation and Gas Exchange • The Body’s Defenses • Regulating the Internal Environment • Chemical Signals in Animals • Animal Reproduction • Animal Development • Nervous Systems • Sensory and Motor Mechanisms

Ecology • An Introduction to Ecology and the Biosphere • Behavioral Biology • Population Ecology • Community Ecology • Ecosystems • Conservation Biology

Summary of Biology • Living things are successful at exploiting their environments. • They do so in a variety of ways, and on a wide range of scales.

Biomorphic Computing • Using biology to inform computational systems.

Possible Dimensions of Biomorphic Computing • Small (nanotechnology) to large (modeling global ecosystems) • Short (packet-switching based on ant foraging) to long (evolving virtual creatures) • Similar to humans (social HCI) to different from humans (simulating the running motion of the Death’s Head cockroach)

Things that move like living things • Robots (MIT Leg Lab, Stanford PolyPEDAL Lab, etc.) • Simulations (video games, movies)

Things that think like living things • Learning (speech recognition, pattern matching) • Coordinated/cooperative behavior (robot soccer, flocking simulations)

Things that adapt to changing circumstances like living things • evolution • distributed systems

Things that develop like living things • Some research, but underexplored…

Things that help us understand how living things work • Flocking Simulation • Simulated evolution • Computational biology

What’s the use? • Living things are very successful. Harness that success for computational systems. • People are used to interacting with living things. Make computational systems easy to use.

Drawing the right lessons • It’s the shape of the wing, rather than the flapping, that enables controlled flight.

Break

Artificial Life MIT CogNet: • Artificial life (A-Life) uses informational concepts and computer modeling to study life in general, and terrestrial life in particular. It aims to explain particular vital phenomena, ranging from the origin of biochemical metabolisms to the coevolution of behavioral strategies, and also the abstract properties of life as such ("life as it could be"). • Focus on self-organization Ninth International Conference on the Simulation and Synthesis of Living Systems: • Artificial Life is the study of life as an organizational principle, rather than as it exists on Earth as carbon-based.

Strong ALife vs. Weak ALife Is it possible to make machines or computer systems that are really alive? Or does ALife just help us make functional things and understand living things. Take a vote.

References • Chris Langton(1986) • Steven Levy (popular press, 1992) • SAB conference (1990 - present) (From Animals to Animats 1 through 8)

Cellular Automata • Cellular automata are discrete dynamical systems whose behaviour is completely specified in terms of a local relation. A cellular automaton can be thought of as a stylised universe. Space is represented by a uniform grid, with each cell containing a few bits of data; time advances in discrete steps and the laws of the "universe" are expressed in, say, a small look-up table, through which at each step each cell computes its new state from that of its close neighbours. Thus, the system's laws are local and uniform. (http://www.brunel.ac.uk/depts/AI/alife/al-ca.htm)

References • John Von Neumann(1951, 1966) • Stanislaw Ulam (1950) • John Conway (via Gardner, 1970) • Stephen Wolfram (1982, 1983, 2002)

One-Dimensional • One-D - time is the vertical axis. • http://math.hws.edu/xJava/CA/CA.html (Wolfram, 83) Time

One-D Cellular automata in nature? (Wolfram, 83)

Two-D • Entire 2D image is replaced each time step.

John Conway’s Game of Life • 2D cellular automata system. • Each cell has 8 neighbors - 4 adjacent orthogonally, 4 adjacent diagonally. This is called the Moore Neighborhood.

Simple rules, executed at each time step: • A live cell with 2 or 3 live neighbors survives to the next round. • A live cell with 4 or more neighbors dies of overpopulation. • A live cell with 1 or 0 neighbors dies of isolation. • An empty cell with exactly 3 neighbors becomes a live cell in the next round.

Is it alive? • http://www.bitstorm.org/gameoflife/ • Compare it to the definitions…

Game of Life Assignment • Implement the central genetic laws of the Game of Life.

Hand out assignment and source code • http://www.ics.uci.edu/~wmt/courses/BiomoW04/BiomoW04Assignment1.html • http://www.ics.uci.edu/~wmt/courses/BiomoW04/GameOfLife.java

Eclipse • How many people have used it?

Biomorphic Computing