Evolving Gene Regulatory Networks with Mobile DNA Mechanisms

This paper uses a recently presented abstract, tuneable Boolean regulatory network model extended to consider aspects of mobile DNA, such as transposons. The significant role of mobile DNA in the evolution of natural systems is becoming increasingly clear. This paper shows how dynamically controlling network node connectivity and function via transposon-inspired mechanisms can be selected for in computational intelligence tasks to give improved performance. The designs of dynamical networks intended for implementation within the slime mould Physarum polycephalum and for the distributed control of a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:1303.722

On the evolution of Boolean networks for computation: A guide RNA mechanism

© 2015 Taylor & Francis. There is a growing body of work within computational intelligence which explores the use of representations inspired by the genetic regulatory networks of biological cells. This paper uses a recently presented abstract, tunable model of such networks to investigate how their design through simulated evolution is affected through the ability to dynamically rewire connectivity. The contextual editing of transcribed RNA by other molecules such that the form of the final product differs from that specified in the corresponding DNA sequence is ubiquitous. It is here shown that a guide RNA-inspired editing mechanism can be selected for under various scenarios

On Natural Genetic Engineering: Structural Dynamism in Random Boolean Networks

This short paper presents an abstract, tunable model of genomic structural change within the cell lifecycle and explores its use with simulated evolution. A well-known Boolean model of genetic regulatory networks is extended to include changes in node connectivity based upon the current cell state, e.g., via transposable elements. The underlying behaviour of the resulting dynamical networks is investigated before their evolvability is explored using a version of the NK model of fitness landscapes. Structural dynamism is found to be selected for in non-stationary environments and subsequently shown capable of providing a mechanism for evolutionary innovation when such reorganizations are inherited

Evolving functional and structural dynamism in coupled boolean networks

© 2014 Massachusetts Institute of Technology. This article uses a recently presented abstract, tunable Boolean regulatory network model to further explore aspects of mobile DNA, such as transposons. The significant role of mobile DNA in the evolution of natural systems is becoming increasingly clear. This article shows how dynamically controlling network node connectivity and function via transposon-inspired mechanisms can be selected for to significant degrees under coupled regulatory network scenarios, including when such changes are heritable. Simple multicellular and coevolutionary versions of the model are considered

Evolving Boolean regulatory networks with epigenetic control

The significant role of epigenetic mechanisms within natural systems has become increasingly clear. This paper uses a recently presented abstract, tunable Boolean genetic regulatory network model to explore aspects of epigenetics. It is shown how dynamically controlling transcription via a DNA methylation-inspired mechanism can be selected for by simulated evolution under various single and multicellular scenarios. Further, it is shown that the effects of such control can be inherited without detriment to fitness

Evolving boolean networks on tunable fitness landscapes

This paper presents an abstract, tunable model by which to explore aspects of artificial genetic regulatory networks and their design by simulated evolution. The random Boolean network formalism is combined with the NK and $NKCS$ models of fitness landscapes. This enables the systematic study of the interactions between the underlying genetic machinery and elements of the phenotype produced. Previously reported results from the models individually are explored within this context, using both synchronous and asynchronous updating. The evolution of network size is then explored in particular under varying conditions. © 2012 IEEE