The story beyond Artificial Immune Systems Zhou Ji, Ph.D. Center for Computational Biology and Bioinformatics Columbia University Wuhan, China 2009

Evolutionary Algorithms Artificial Life DNA Computing

Evolutionary Algorithms Artificial Life DNA Computing

 Genetic algorithm – a well established algorithm  Artificial Immune Systems – a new area that are diverse and to be defined  Bioinformatics – what is both biology and computer science at the same time

cellular molecular organ population Tissue

1. Chromosomes change between generations crossover Mutation 2. Survival of the fittest How does evolution happen?

 Typical problem handled with GA  optimization  What is search space? – all possible parameters  It is UNKNOWN in general  GA’s basic idea and procedure  Start a population  Evaluate fitness  New population  Selection, crossover, mutation, accepting  Replace  Test (absolute or relative criterion) and loop

 Any computing methods inspired by immune system and computational effort for immunology motivation  Clonal selection  Immune network model  Negative selection algorithms  Danger theory and other new directions

Typical application: clustering Network of “B-cells” to represent the types of antibody Develop based on Interaction between nodes and between node and training data (‘antibody’)

Biologists StatisticiansComputer Scientists

Each of the four letters takes 2 bits to store. One byte thus can store four letters. Human genome include about 3 billion nucleotides: 3 X 10^9 /4 = 8 X 10^8 = 800,000, MB - that takes about one regular CD to store. DNA is strings A, T(U), C, G.

 Natural computing bridges between biology and computer science  Bio-inspired computing  Emulated life  Computing with natural materials  Biology is very interesting from the computer science point of view.