Elsevier

Information Sciences

Volume 178, Issue 23, 1 December 2008, Pages 4469-4481
Information Sciences

Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones

https://doi.org/10.1016/j.ins.2008.07.027Get rights and content

Abstract

We present the results of our work in simulating the recently discovered findings in molecular biology regarding the significant role which histones play in regulating the gene expression in eukaryotes. Extending the notion of inheritable genotype in evolutionary computation from the commonly considered model of DNA to chromatin (DNA and histones), we present epigenetic programming as an approach, incorporating an explicitly controlled gene expression through modification of histones in strongly-typed genetic programming (STGP). We propose a double cell representation of the simulated individuals, comprising somatic cell and germ cell, both represented by their respective chromatin structures. Following biologically plausible concepts, we regard the plastic phenotype of the somatic cell, achieved via controlled gene expression owing to modifications to histones (epigenetic learning, EL) as relevant for fitness evaluation, while the genotype of the germ cell corresponds to the phylogenesis of the individuals. The beneficial effect of EL on the performance characteristics of STGP is verified on evolution of social behavior of a team of predator agents in the predator–prey pursuit problem. Empirically obtained performance evaluation results indicate that EL contributes to about 2-fold improvement of computational effort of STGP. We trace the cause for that to the cumulative effect of polyphenism and epigenetic stability, both contributed by EL. The former allows for phenotypic diversity of genotypically similar individuals, while the latter robustly preserves the individuals from the destructive effects of crossover by silencing certain genotypic combinations and explicitly activating them only when they are most likely to be expressed in corresponding beneficial phenotypic traits.

Introduction

“The major problem, I think, is chromatin [the dynamic complex of DNA and histone proteins that makes up chromosomes]. What determines whether a given piece of DNA along the chromosome is functioning, since it’s covered with the histones? What is happening at the level of methylation and epigenetics [chemical modification of the DNA that affects gene expression]? You can inherit something beyond the DNA sequence. That’s where the real excitement of genetics is now.”

J.D.Watson [23]

Until a few years ago, the role of histones (the family of proteins which DNA is wrapped around forming a super-coiled chromatin fiber) in molecular biology community was viewed as solely to help pack the long DNA into the tiny nucleus of eukaryotes’ cells. However, as the results of recent research suggest, the histones play a significant role in regulating the synthesis, repair, recombination and transcription of DNA [9], [19], [22]. For example, it is believed that regulation of the transcription mechanism of the same DNA via “histone code” during cell division controls the specialization of the cells yielding the well-known variety of cell types. Also, the gene expression regulated by “histone code” might control the variances in phenotypes (i.e. biochemistry, morphology, physiology and behavior) seen on different stages of life cycle of living organisms as developing, maturing and aging. Moreover, the onset of some genetically associated diseases (and even cancer) is viewed as a process triggered by both a sudden activation of the genes that “contribute” to the disease and/or deactivation of the genes that “fight” the disease. The histone code is regarded as an integrating link in the information pathway of epigenesis of living organisms. As illustrated in Fig. 1, the interaction between the phenotype and various environmental factors (such as food, viral infections, exposure to toxins, radiation, light and UV) leads to corresponding variations in the histone code, which in turn result in modified (beneficial or detrimental) gene expression.

Without touching the details of either the chromatin structure or the underlying chemical processed in histones, we would like to generalize the recently emerged findings that transcription of the genes in DNA is controlled by the surrounding chemical structure of histones. The acetylation of histones correlates with transcriptional activity of the corresponding DNA genes, while the methylation, with silencing (transcriptional inactivation) of genes.

In the proposed approach we extend the notion of inheritable genotype in genetic programming (GP) from the commonly considered model of DNA of the living organisms (the genetic program, typically represented as parse tree or corresponding linear representation as S-expression, prefix or postfix polish notation) into chromatin (DNA and histones). We attempt to mimic the naturally observed phenomenon of regulating gene expression via histone code into a software system featuring epigenesis, embedded into the simulated evolution (phylogenesis). Because we are mainly interested in beneficial modifications of histone code, which take place within the life cycle of evolved simulated organisms, we view the proposed approach of epigenetic programming as a form of epigenetic learning (EL) incorporated in GP. The objective of our work is to explore the effects of EL on the performance characteristics, and especially on the computational effort of evolution of autonomous agents in multi-agent systems (MAS).

Our work can be viewed as related to the methods of employing heuristics, phenotypic plasticity, Baldwin effect, inactive code [17], neutral code [5] and redundant representations [20] in GP. In contrast to the approaches of using heuristics [16], phenotypic plasticity [3] and Baldwin effect [6], the proposed learning mechanism does not imply direct manipulation of either the simulated DNA or the phenotype. Instead, the ontogenetic adaptation of phenotypes in our approach is achieved by controllable and inheritable gene expression mechanisms. The silenced genes can still comprise the genotype of the individual without affecting its phenotype. In addition to being biologically more plausible, such an approach offers (i) better phenotypic diversity of genotypically similar individuals and (ii) an efficient way to preserve the individuals from the destructive effects of crossover by explicit activation of the growing genetic combinations only when they are most likely to be expressed as beneficial phenotypic traits.

The previous work on the implications of emergent inactive code [5], [17], [20] on the performance of genetic programming has not been intended to investigate the effects of explicit manipulations of the inactive code, while the approach proposed in this paper centers on the computational efficiency of maintaining and explicitly manipulating the inactive code in a biologically plausible way.

The remainder of this document is organized as follows. Section 2 introduces the task, which we use to test our hypotheses – an instance of the general, well-defined yet difficult to solve predator–prey pursuit problem. The same section addresses the issue of developing the software architecture of the agents. Section 3 elaborates the main features of developed genetic programming, used to evolve the functionality of agents. The proposed mechanism of EL is introduced in Section 4. Section 5 presents empirically obtained results of the implications of EL on the performance of evolution. Conclusions are drawn in Section 6 where the anticipated directions for future research are mentioned.

Section snippets

Instance of predator prey pursuit problem

Currently, the main application areas of MAS are problem solving, simulation, collective robotics, software engineering, and construction of synthetic worlds [4]. Considering the latter application area and focusing on the autonomy of agents and the interactions that link them together [18], the following important issues can be raised: How can agents cooperate? What is the architecture they should feature so that they can achieve their goals? What approaches can be applied to automatically

Limiting the search space of genetic programming

We consider a set of stimulus–response rules as a natural way to model the reactive behavior of predator agents [8], which in general can be evolved using artificial neural networks, genetic algorithms, and genetic programming (GP). GP is a domain-independent problem solving approach in which a population of computer programs (individuals) is evolved to solve problems [10]. The simulated evolution in GP is based on the Darwinian principle of reproduction and survival of the fittest. In GP

Chromatin representation

In the developed approach of EL, incorporated in STGP, we consider the predator agents in STGP as simulated individuals passing through the phases of birth, ontogenetic adaptation and survival (reproduction) or death (Fig. 6).

At the phase of birth, the individual in STGP is represented as a single embryonic cell expressed by its respective chromatin. The ontogenetic adaptation is initiated by the simulated division of the embryonic cell into single germ cell and single somatic cell. Both cells

Values of parameters

The values of parameters of STGP used in our experiments are as follows: the population size is 400 genetic programs, the selection ratio is 0.1, including 0.01 elitism, and the mutation ratio is 0.02, equally divided between sub-tree mutation, transposition and histone modification. The termination criterion is defined as a disjunction of the following conditions: (i) fitness of the best genetic program in less than 300 and the amount of initial situations in which the prey is captured

Conclusion

We presented the results of our work inspired by recently discovered findings in molecular biology which suggest that histones play a significant role in regulating the gene expression in eukaryotes. Extending the notion of inheritable genotype in GP from commonly considered model of DNA to chromatin, we propose an approach of epigenetic programming as a way to incorporate the naturally observed phenomenon of regulated gene expression via modification of histones. Considering the individual as

Acknowledgements

The authors thank Katsunori Shimohara for his immense support of this research. The research was supported in part by the National Institute of Information and Communications Technology of Japan.

References (23)

  • M. Benda, B. Jagannathan, R. Dodhiawala, On optimal cooperation of knowledge sources, Technical Report BCS-G2010-28,...
  • R.A. Brooks

    A robust layered control system for a mobile robot

    IEEE Journal of Robotics and Automation

    (1986)
  • A.I. Esparcia-Alcázar, K.C. Sharman, Phenotype plasticity in genetic programming: a comparison of darwinian and...
  • J. Ferber

    Multi-Agent Systems

    (1999)
  • C. Ferreira

    Gene expression programming: a new adaptive algorithm for solving problems

    Complex Systems

    (2001)
  • D. Floreano et al.

    Co-evolution and ontogenetic change in competing robots

  • T. Haynes et al.

    Evolving behavioral strategies in predators and prey

  • J.H. Holand

    Emergence: From Chaos To Order

    (1999)
  • T. Jenuwein et al.

    Translating the Histone Code

    Science

    (2001)
  • J.R. Koza

    Genetic Programming: On the Programming of Computers by Means of Natural Selection

    (1992)
  • S. Luke et al.

    Evolving Teamwork and Coordination with Genetic Programming

  • Cited by (0)

    View full text