1. Molecular Computing and Evolution In Vitro Evolution In Vitro Clustering Genetic Algorithms Artificial Immune Systems Biological Evolution and Molecular Computing

2. In Vitro Evolution Mutagenesis Mutation is a change in the genes Chemical Modification of Bases PCR-based –Site Directed Mutagenesis –Error-Prone PCR –DNA Shuffling

5. Error-Prone PCR Finite Error Rate for all Polymerases Conditions to increase error rate Restrict access to one base Mn Introduces errors Point Mutataions

6. DNA Shuffling Digest Initial Population Multiple Cycles of PCR without Primers Fragments from Initial Population Server as Primers Error-Prone PCR Swapping Sequences Between Molecules

7. Clustering Algorithm Feature map that preserves neighborhood relations in input data Cluster or Categorize the Data Similar Data Placed in Same Category Competitive Learning Prototypical Representations of Clusters of Data

8. Memory Hybridization Associative Memory Mechanism Memory Hybridization

9. Data Molecules Memory Molecules SH Difference Molecules In Vitro Evolution Memory Molecules DNA Memory Array Seed with Designed Molecules

10. Genetic Algorithms John Holland Mutation and Crossover (sexual reproduction) Selection for Fit Individuals in a Population Children Reproduced from Fit Parents Effectively Searches Space (Wide Search Pattern)

12. Artificial Immune Systems Recognition of Self and Non-Self T-cells and B-cells Site Specific Recognition

15. The Evolution of DNA Computing How do cells and nature compute? Thesis: Ciliates compute a difficult HP problem in Gene Unscrambling. Similarities to Adleman’s Path Finding Problem in the Cell, RNA Editing Turing Machine Power

17. The Problem in the Cell Genomic Copies of some Protein-coding genes are obscured by intervening non- protein-coding DNA sequence elements (internally eliminated sequences, IES) Protein-coding sequences (macronuclear destined sequences, MDS) are present in a permuted order, and must be rearranged.

18. How the cell computes Relies on short repeat sequences to act as guides in homologous recombination events Splints analogous to edges in Adleman One example represents solution of 50 city HP (50 pieces reordered)

19. Hybridization Error Short repeat sequences, necessary but not sufficient for reassembly Incorrect hybridization produces newly scrambled patterns in evolution Would dominate in hybridizations Eliminated in macronucleus (telomere addition sequences selectively retained) Telomere: Unusual sequences at ends of genes

20. Formal Model Formal Language Model Intramolecular recombination. The guide is x. Deletex wx from original. Intermolecuar recombination. Strand Exchange.

21. Assumption By clever structural alignment…, the cell decides which sequences are IES and MDS, as well as which are guides. After this decision, the process is simply sorting, O(n). Decision process unknown, but amounts to finding the correct path. Most Costly.