Molecular Microprograms

Simon J. Hickinbotham; Edward Clark; Susan Stepney; Tim Clarke; Adam Nellis; Mungo Pay; Peter Young; George Kampis; István Karsai; Eörs Szathmáry

Molecular Microprograms

Simon J. Hickinbotham, Edward Clark, Susan Stepney, Tim Clarke, Adam Nellis, Mungo Pay, Peter Young, George Kampis, István Karsai, Eörs Szathmáry

Research output: Contribution to conference › Paper

Abstract

Bacteria offer an evolutionary model in which rich interactions between phenotype and genotype lead to compact genomes with efficient metabolic pathways. Central to this is the expression and folding of sequences of amino acids to form proteins. We seek an analogous process that supports a rich artificial heredity. These systems can be simulated by stochastic chemistry models, but there is currently no scope for open-ended evolution of the molecular species that make up the models. Instruction-set based Artifical Life has appropriate evolutionary properties, but the individual is represented as a single executing sequence with little additional physiology. We describe a novel combination of stochastic chemistries and evolvable molecule microprograms that gives a rich evolutionary framework. Key to this approach is the use of inexact sequence matching for binding between individual molecules and for branching of molecular microprograms. We illustrate the approach by implementation of two steady-state replicase RNA analogues that demonstrate "invasion when rare".

Original language	Undefined/Unknown
Pages	297-304
Publication status	Published - 2009

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http://dx.doi.org/10.1007/978-3-642-21283-3_37

PLAZZMID: Evolutionary algorithms from bacterial
Stepney, S.
EPSRC
2/06/08 → 30/11/11
Project: Research project (funded) › Research

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title = "Molecular Microprograms",

abstract = "Bacteria offer an evolutionary model in which rich interactions between phenotype and genotype lead to compact genomes with efficient metabolic pathways. Central to this is the expression and folding of sequences of amino acids to form proteins. We seek an analogous process that supports a rich artificial heredity. These systems can be simulated by stochastic chemistry models, but there is currently no scope for open-ended evolution of the molecular species that make up the models. Instruction-set based Artifical Life has appropriate evolutionary properties, but the individual is represented as a single executing sequence with little additional physiology. We describe a novel combination of stochastic chemistries and evolvable molecule microprograms that gives a rich evolutionary framework. Key to this approach is the use of inexact sequence matching for binding between individual molecules and for branching of molecular microprograms. We illustrate the approach by implementation of two steady-state replicase RNA analogues that demonstrate {"}invasion when rare{"}.",

author = "Hickinbotham, {Simon J.} and Edward Clark and Susan Stepney and Tim Clarke and Adam Nellis and Mungo Pay and Peter Young and George Kampis and Istv{\'a}n Karsai and E{\"o}rs Szathm{\'a}ry",

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TY - CONF

T1 - Molecular Microprograms

AU - Hickinbotham, Simon J.

AU - Clark, Edward

AU - Stepney, Susan

AU - Clarke, Tim

AU - Nellis, Adam

AU - Pay, Mungo

AU - Young, Peter

AU - Kampis, George

AU - Karsai, István

AU - Szathmáry, Eörs

PY - 2009

Y1 - 2009

N2 - Bacteria offer an evolutionary model in which rich interactions between phenotype and genotype lead to compact genomes with efficient metabolic pathways. Central to this is the expression and folding of sequences of amino acids to form proteins. We seek an analogous process that supports a rich artificial heredity. These systems can be simulated by stochastic chemistry models, but there is currently no scope for open-ended evolution of the molecular species that make up the models. Instruction-set based Artifical Life has appropriate evolutionary properties, but the individual is represented as a single executing sequence with little additional physiology. We describe a novel combination of stochastic chemistries and evolvable molecule microprograms that gives a rich evolutionary framework. Key to this approach is the use of inexact sequence matching for binding between individual molecules and for branching of molecular microprograms. We illustrate the approach by implementation of two steady-state replicase RNA analogues that demonstrate "invasion when rare".

AB - Bacteria offer an evolutionary model in which rich interactions between phenotype and genotype lead to compact genomes with efficient metabolic pathways. Central to this is the expression and folding of sequences of amino acids to form proteins. We seek an analogous process that supports a rich artificial heredity. These systems can be simulated by stochastic chemistry models, but there is currently no scope for open-ended evolution of the molecular species that make up the models. Instruction-set based Artifical Life has appropriate evolutionary properties, but the individual is represented as a single executing sequence with little additional physiology. We describe a novel combination of stochastic chemistries and evolvable molecule microprograms that gives a rich evolutionary framework. Key to this approach is the use of inexact sequence matching for binding between individual molecules and for branching of molecular microprograms. We illustrate the approach by implementation of two steady-state replicase RNA analogues that demonstrate "invasion when rare".

M3 - Paper

SP - 297

EP - 304

ER -

Molecular Microprograms

Abstract

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Projects

PLAZZMID: Evolutionary algorithms from bacterial

Cite this