Using Artificial Epigenetic Regulatory Networks to Control Complex Tasks within Chaotic Systems

Turner, Alexander P.; Lones, Michael A.; Fuente, Luis A.; Stepney, Susan; Caves, Leo S. D.; Tyrrell, Andy M.

doi:10.1007/978-3-642-28792-3_1

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…We have speculated that such a coupled architecture could have benefits when a controller needs to switch rapidly between different tasks, and an analysis of coupled controllers solving the Lorenz and robot tasks tends to support this. In other work, we are looking at an alternative approach to handling this kind of task switching which uses an analogue of epigenetic chromatin remodelling [105].…”

Section: Discussionmentioning

confidence: 99%

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

2014

IEEE Trans. Evol. Computat.

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Biological organisms exist within environments in which complex, non-linear dynamics are ubiquitous. They are coupled to these environments via their own complex, dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behaviour of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organisation of biochemical networks. We refer to these as artificial biochemical networks, and show how they can be evolved to control trajectories within three behaviourally diverse complex dynamical systems: the Lorenz system, Chirikov's standard map, and legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks. We find both approaches to be advantageous in certain situations, though note that the relative trade-offs between different models of artificial biochemical network strongly depend on the type of dynamical systems being controlled.

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Section: Discussionmentioning

confidence: 99%

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

2014

IEEE Trans. Evol. Computat.

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“…Epigenetic mechanisms partially regulate extra-villous trophoblast differentiation and invasiveness into the endometrium (Figure 1 ) (Rahnama et al, 2006 ; Rugg-Gunn, 2012 ; Chen et al, 2013a ). Over the last decade the study of epigenetics has been applied to many complex biological systems, such as trophoblast differentiation, in order to reach an understanding of the mechanisms and the pathways (Cox et al, 2009 , 2011 ; Choi, 2010 ; Hemberger, 2010 ; Senner and Hemberger, 2010 ; Turner et al, 2012 ; Arnold et al, 2013 ).…”

Section: Epigenetic Mechanisms Are Involved In Developing a Healthy Pmentioning

confidence: 99%

DNA methyltransferases and TETs in the regulation of differentiation and invasiveness of extra-villous trophoblasts

2013

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Specialized cell types of trophoblast cells form the placenta in which each cell type has particular properties of proliferation and invasion. The placenta sustains the growth of the fetus throughout pregnancy and any aberrant trophoblast differentiation or invasion potentially affects the future health of the child and adult. Recently, the field of epigenetics has been applied to understand differentiation of trophoblast lineages and embryonic stem cells (ESC), from fertilization of the oocyte onward. Each trophoblast cell-type has a distinctive epigenetic profile and we will concentrate on the epigenetic mechanism of DNA methyltransferases and TETs that regulate DNA methylation. Environmental factors affecting the mother potentially regulate the DNA methyltransferases in trophoblasts, and so do steroid hormones, cell cycle regulators, such as p53, and cytokines, especially interlukin-1β. There are interesting questions of why trophoblast genomes are globally hypomethylated yet specific genes can be suppressed by hypermethylation (in general, tumor suppressor genes, such as E-cadherin) and how invasive cell-types are liable to have condensed chromatin, as in metastatic cancer cells. Future work will attempt to understand the interactive nature of all epigenetic mechanisms together and their effect on the complex biological system of trophoblast differentiation and invasion in normal as well as pathological conditions.

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“…This has been shown to be effective (Lones et al, 2010(Lones et al, , 2011Fuente et al, 2012;Turner et al, 2012Turner et al, , 2013a.…”

Section: Similarities To Other Modelsmentioning

confidence: 99%

“…For a network containing ten genes and three epigenetic molecules, there are 72 variables which must be optimised. This is done using a genetic algorithm (section 4.1) as research has previously shown that networks evolved using genetic algorithms have been able to express complex dynamics (Banzhaf, 2003;Nordin et al, 1995;Banzhaf et al, 2006;Turner et al, 2012Turner et al, , 2013bLones et al, 2010). To perform the crossover operation of the genetic algorithm, the genes and epigenetic molecules will be treated as the fundamental units of the network, and that they can only be crossed over as individual units to limit disruption.…”

Section: Optimisation Of the Network For Computationmentioning

confidence: 99%

The artificial epigenetic network

Turner

Lones

Fuente

et al. 2013

2013 IEEE International Conference on Evolvable Systems (ICES)

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The term epigenetics refers to typically heritable biological mechanisms which facilitate stable yet reversible modifications of gene expression or phenotype state, without alteration of the underlying genetic code. More specifically, epigenetic mechanisms allow organisms to control which genes are active at a given time. In eukaryotes, epigenetic mechanisms have essential roles in gene regulation, cellular differentiation and genetic packaging. These epigenetic mechanisms give rise to functionality which DNA alone is generally incapable of providing.This thesis takes inspiration from the fields of genetics and epigenetics, and builds a computational model which captures the beneficial properties of epigenetics in silico. This computational model is referred to as the artificial epigenetic network. The artificial epigenetic network can dynamically control which genes within the network are active at a given time, allowing certain groups of genes to become specialised towards specific aspects of a task.Hence, the artificial epigenetic network can contain many different regulatory circuits, each with specific properties. This gives the networks the ability to more readily express a wider range of dynamical behaviours, which were found to produce a number computational benefits. The artificial epigenetic network is applied to a diverse range of control tasks, each with varying dynamics, to ascertain how the functionality of the artificial epigenetic structures effects the functionality of the network. An emergent property is that the epigenetic structures can partition the network into functional units corresponding to the logical decomposition of the tasks, and control these units with a switch like behaviour. This provides an interface, where a user can gain control over the complex dynamics of the target domain via the activation or deactivation of these switches.3

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Using Artificial Epigenetic Regulatory Networks to Control Complex Tasks within Chaotic Systems

Cited by 7 publications

References 16 publications

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

DNA methyltransferases and TETs in the regulation of differentiation and invasiveness of extra-villous trophoblasts

The artificial epigenetic network

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