Molecular Computing and Evolution

• In Vitro Evolution

• In Vitro Clustering

• Genetic Algorithms

• Artificial Immune Systems

• Biological Evolution and Molecular Computing

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

Error-Prone PCR

• Finite Error Rate for all Polymerases

• Conditions to increase error rate

• Restrict access to one base

• Mn Introduces errors

• Point Mutataions

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

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

))(,( *ijjij ii

Memory

Hybridization

Associative Memory Mechanism

Memory

Hybridization

Data Molecules

Memory Molecules

SH

Difference Molecules

In Vitro Evolution

Memory Molecules

DNAMemoryArray

Seedwith DesignedMolecules

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)

Artificial Immune Systems

• Recognition of Self and Non-Self

• T-cells and B-cells

• Site Specific Recognition

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

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.

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)

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

Formal Model

• Formal Language Model

• Intramolecular recombination. The guide is x. Deletex wx from original.

• Intermolecuar recombination. Strand Exchange.

wxuxvuxwxv

xvuuxvxvuuxv ''''

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.