EPIAL

An Epigenetic Approach for an Artiﬁcial Life Model

Jorge Sousa and Ernesto Costa

Evolutionary and Complex Systems Group

Centre of Informatics and Systems of the University of Coimbra, Portugal

Keywords:

Artiﬁcial life, Epigenetics, Regulation, Dynamic environments, Environmental inﬂuence.

Abstract:

Neo-Darwinist concepts have always been questioned and, nowadays, one of the sources of debate is epi-

genetic theory. Epigenetics study the relation between phenotypes and their environment, and the way this

relation can regulate the genetic expression, while producing traits that can be inherited by offspring. This

work presents an Artiﬁcial Life model designed with epigenetic concepts of regulation and inheritance. A

platform was developed, in order to study the evolutionary signiﬁcance of the epigenetic phenomena, both

at individual and population levels. Differences were observed in the evolutionary behavior of populations,

regarding the epigenetic variants. Agents without epigenetic structures display difﬁculties thriving in dynamic

environments, while epigenetic based agents are able to achieve regulation. It is also possible to observe the

persistence of acquired traits during evolution, despite the absence of the signal that induces those same traits.

1 INTRODUCTION

150 years after the publication of “On the Origin of

Species” (Darwin, 1859), Darwin’s theory of evolu-

tion by means of natural selection is still a source

of inspiration, but also of debate. Neo-Darwinism

(Dawkins, 1976), the idea that evolution is gene cen-

tric and that the organisms’ structures are untouchable

by the environment, is due for a revision, as defended

by some authors (Jablonka and Lamb, 2005). Differ-

ent theories claim that the modern synthesis provides

for an incomplete view of evolution, regarding the

separation between organism and nature (Wadding-

ton, 1942). Genetic structures are far more com-

plex than previously thought, due to the non linear

nature of the mapping between genotype and phe-

notype. Epigenetics posit the existence of environ-

mentally based regulatory operations, and the pos-

sibility for inheritance of structural marks (Jablonka

and Lamb, 2005). Several approaches in Artiﬁcial

Life (ALife) attempt to model biological phenomena,

mostly focusing on the Neo-Darwinist point of view

in evolution. Most models perceive the environment

solely as a factor of selection, discarding interrela-

tions between agents and their world, such as the in-

ﬂuence of the environment in the developmental pro-

cesses (Rocha, 2007), which could provide for an al-

ternative evolutionary approach (Jablonka and Lamb,

2005). In this work, we present an approach for an

ALife model that considers epigenetic concepts, fo-

cusing on the regulation of organisms and the possible

inheritance of epigenetic acquired marks. By includ-

ing regulatory elements in the agents’ structures, our

model enables the study of the evolutionary signiﬁ-

cance of epigenetic concepts, while contributing to an

enriched ALife model. The remainder of this article

is organized in the following way: section 2 brieﬂy

tackles epigenetic theory; section 3 presents the cur-

rent state of the art in epigenetic or related ALife mod-

els; section 4 describes the developed model, EpiAL;

in section 5, some experimental results are shown; ﬁ-

nally, section 6 discusses some ﬁnal remarks pointing

to some future work directions.

2 EPIGENETICS

Epigenetics is conceived as a set of genetic mech-

anisms and operations involved in the regulation of

gene activity, allowing the creation of phenotypic

variation without a modiﬁcation in the genes’ nu-

cleotides. Some of these variations are also pos-

sibly inheritable between generations of individuals

(Gilbert and Epel, 2009). The focus is on the ma-

jor importance of development in organisms, but also

on the relationship between the mechanisms of devel-

Sousa J. and Costa E. (2010).

EPIAL - An Epigenetic Approach for an Artiﬁcial Life Model.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Artiﬁcial Intelligence, pages 90-97

DOI: 10.5220/0002732500900097

 SciTePress

opment and the genetic system and, ultimately, with

evolution itself (Jablonka and Lamb, 2005). In terms

of phylogeny, epigenetics refers to the traits that are,

or can be, inherited by means other than DNA nu-

cleotides. Epigenetics in relation to ontogeny refers

to the inﬂuence, through epigenetic effects, of struc-

tural genetic parts of an individual during its lifetime

(Gorelick, 2004). Although epigenetic marks can be

inheritable, their frequency of inheritance is lower

than nucleotide sequences (Holliday, 1994). This is

due to the fact that epigenetic signals are much eas-

ier to alter through environmental disturbances and,

therefore, it could result in a high and undesirable

variability (Gorelick, 2004). Four types of (cellular)

epigenetic inheritance systems (EIS) have been theo-

rized (Jablonka and Lamb, 2005): (i) self-sustaining

regulatory loops; (ii) structural templating; (iii) chro-

matin marking systems; (iv) RNA-mediated inheri-

tance . The one that is used for our work is the chro-

matin system, in which methylation is one of the pos-

sible marks (Jablonka and Lamb, 2005). Methylation

of DNA refers to the addition of a methyl group to

the base sequence, that although does not change the

coding properties of the base, can inﬂuence its gene

expression (Bender, 2004; Boyko and Kovalchuk,

2008). It is known (Gorelick, 2004) that stress re-

sponses (tolerance, resistance, avoidance or escape)

from the organisms, which are induced from environ-

mental conditions, can lead to responses of methy-

lation or demethylation of a binding site of chromo-

somes. The effects of these responses can both be her-

itable and remain present during more than one gener-

ation (Gorelick, 2004). Most individuals, during de-

velopment, possess mechanisms that erase the methy-

lation marks from the parents, sometimes resetting the

genome to the original state (Youngson and Whitelaw,

2008). Nevertheless, there are cases in which these

erasure operations are not fully accomplished, with

epigenetic variation persisting through meiosis, and

being retained by the offspring (Jablonka and Lamb,

2005).

3 STATE OF THE ART

Artiﬁcial Life (ALife) is a scientiﬁc area whose main

goal is the study of life and life-like phenomena by

means of computational models, aiming at a better

understanding of those phenomena. As a side ef-

fect, ALife has also produced nature-inspired solu-

tions to different engineering problems. At the core

of ALife activity, we ﬁnd the development of mod-

els that can be simulated with computers. Although

some works use epigenetic theory (or related con-

cepts) for problem solving techniques, to the best of

our knowledge, none tackles the question of exper-

imenting with epigenetic ideas regarding the evolu-

tionary questions posed by the concept itself. This

is a ﬂaw that is pointed out by other authors as well

(Rocha, 2007). There are some approaches for prob-

lems solving model that take inspiration in epige-

netic, or epigenetic related ideas. In (Rocha and Kaur,

2007), the authors model an agent structure that is

able to edit the genotype, allowing the same genotype

to produce different phenotypic expressions. This

edition, however, is not inﬂuenced by environmen-

tal conditions. A dynamic approach for the envi-

ronment is presented in (Clune et al., 2007), where

an Avida (Ofria and Wilke, 2004) based model pro-

motes a time based symmetric environment in order

to induce the agents to produce phenotypic plastic-

ity (Pigliucci, 2001). In (Tanev and Yuta, 2008), epi-

genetic theory is used in order to model a different

sort of genetic programming, with the modelling of

different life phases (development, adult life) being

used to adapt the agents to the environment. During

the simulations, the agents adapt to the environment

using epigenetic based processes, that are separated

between somatic and germ line structures. Finally,

in (Periyasamy et al., 2008), epigenetic concepts are

used for the formulation of an Epigenetic Evolution-

ary Algorithm (EGA). The algorithm is used in order

to attempt an optimization for the internal structures

of organizations, with a focus on the autopoietic be-

havior of the systems.

4 EPIAL MODEL

EpiAL aims at studying the plausibility for the exis-

tence of epigenetic phenomena and its relevance to an

evolutionary system, from an ALife point of view. In

this section, we ﬁrst describe the conceptual design

and notions used in EpiAL, focusing in the agent, the

regulatory mechanisms and the environment. Then

we present the dynamics of the system, explaining

how to evaluate the agents and the mechanisms of in-

heritance of EpiAL.

4.1 Conceptual Design

In our model, epigenetics is considered as the ability

for an agent to modify its phenotypic expression due

to environmental conditions. This means that an agent

has regulatory structures that, given an input from the

environment, can act upon the genotype, regulating

its expression. We also consider the possibility for the

epigenetic marks to be inherited between generations,

EPIAL - An Epigenetic Approach for an Artificial Life Model

through the transmission of partial or full epigenetic

marks (methylation values), allowing the existence

of acquired traits (methyl marks) to be transmitted

through generations of agents. The proposed model

is based on two fundamental entities, the agent(s) and

the environment. Their relation and constitution are

conceptually depicted on ﬁgure 1. The environment

Figure 1: EpiAL conceptual model.

provides inputs that are perceived by the sensors of

the agent (S). These sensors are connected to the el-

ements that compose the epigenotype (EG), i.e., the

epigenetic code of the agent. The epigenotype is com-

posed of structures that can act over the agents’ geno-

type (G), performing regulatory functions. This regu-

lation occurs in methylation sites that are assigned to

each of the genes, controlling their expression. The

methylation value for a gene is a real value compre-

hended between 0 (not methylated at all) and 1 (fully

methylated). It is this value that determines, stochasti-

cally, the type of expression for each of the genes. Fi-

nally, genes are expressed, originating the phenotype

of the agents, which is composed of a set of traits (T).

This mapping from expressed genotype to phenotype

is performed according to a function (f) that relates

sets of genes with the traits. In ﬁgure 2, we show an

example where each trait is dependent of 3 genes.

Figure 2: Genotype to phenotype mapping.

4.2 Regulation

The regulatory actions are taken under control of the

epigenes. An agent can contain one or several epi-

genes, that have the structure shown in ﬁgure 3. Epi-

genes are composed of two main parts, sensory and

(a) Generic epigene structure.

(b) Concrete example of an epigene.

Figure 3: Epigene structure.

regulatory (ﬁgure 3(a)). The sensory section is a tu-

ple that states (i) the sensor to which the epigene is

connected and (ii) the reference value that is going

to be used to compare with an environmental effect.

The possible reference values (for instance, the mean

values for each range of environmental effects) are

previously setup by the experimenter. The other con-

stituent of the epigenes, the regulatory section, con-

tains a list of tuples, with each tuple representing a

gene and a regulatory operation. During a regulatory

phase, the reference value encoded in the epigene is

compared against the environmental value perceived

by the sensor corresponding to the epigene. If the epi-

gene detects an activation, it acts on the genotype by

ﬁring the rules that it encodes. Formulas 1 and 2 are

used to deﬁne the update of the methylation sites, with

m(g) representing the methylation value of gene g,

BaseMethyl a methylation constant and AcValue the

activation value perceived from the sensory operation,

proportional to the level of activation perceived. The

rules can be to methylate (formula 1) or demethylate

(formula 2) a certain gene, which means that a methy-

lation value is either increased or decreased for that

gene.

m(g)

new

= max(1,m(g)

old

+(BaseMethyl ∗AcValue))

(1)

m(g)

new

= min(0, m(g)

old

−(BaseMethyl ∗AcValue))

(2)

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4.3 Environment

The environment is modelled as a 2D grid with each

location being transposable or not (a wall). Each

of the locations also has different attributes - tem-

perature, light and food - that can vary along time.

These attributes are used to deﬁne the favoured traits

for the locations. A certain location in the world

favours agents which have the traits more adequate

for the current environmental conditions. Therefore,

a change in an environmental condition might also

imply a change in the trait that is favoured. This is

shown in ﬁgure 4, where a modiﬁcation in the tem-

perature state also implies a modiﬁcation in the set

of favoured traits. However, environmental dynamics

are also used to promote the regulatory expression of

agents. This allows for an inﬂuence of the environ-

ment over the agents not only by performing selec-

tion, but also by inducing possible structural changes

(either adaptive or disruptive) within the agent. Be-

cause the agents sense environmental conditions, if

there is a change in the environment then it is possible

that regulatory actions are undertaken in the agents

structures.

Figure 4: Environmental traits setup of favoured traits.

4.4 System Dynamics

The simulation of the agents in the environment is

performed in evolutionary steps, as shown in ﬁgure

5. Agents are born and, either during their develop-

ment phase or their adult life, are subject to regula-

tory phases. Here, by sensing environmental condi-

tions, they regulate their expression. At the core of

Figure 5: EpiAL system cycles.

the evolutionary system is a Darwinian evolution and,

as such, agents are subject to selection, with the best

ones being chosen for reproduction and new agents

being subject to mutation. The agents live for a num-

ber of generations, originate offspring periodically

and die of old age, with the possibility for different

generations to live at the same time in the environ-

ment. The time step of the agents is independent from

the one in the environment. An agent can perform

several internal cycles of regulation or food digestion

during only one environmental cycle.

4.5 Evaluation

As agents do possibly have different performances

during their lifetime, due to their different regulation

status, it is relevant to possess means for this evalua-

tion to take into account not only the immediate per-

formance, but also an account of the past behaviour

of the agent. Moreover, recent values are more im-

portant to evaluate an agent. In order to evaluate the

agents in EpiAL, two measures are used: direct ﬁtness

evaluation, i.e., the immediate ﬁtness of the agents.

This value is obtained by comparing the agents’ trait

values with the favoured ones for the location (cell)

in which the agent currently stands (formula 3). From

the formula, it is clear that the lower the sum of the

module value of the differences, the better. Therefore,

agents attempt to minimize this direct ﬁtness. Dur-

ing the life of the agents, these evaluations are stored.

When one desires to evaluate the global performance

of an agent, the weighted evaluation is performed.

This calculation is based on the exponential moving

average method, proposed in (Achelis, 2000). In sim-

ple terms, this method assigns more weight to recent

evaluations. The weighted sum calculated in formula

4 is used to obtain the weighted ﬁtness, as depicted

in formula 5 ( f (t) is the direct ﬁtness evaluation for

time t). This allows to perceive if the current regula-

tory state of the agent is beneﬁcial or detrimental. At

the same time, allows agents that are born later to be

evaluated without prejudice from the older agents.

EPIAL - An Epigenetic Approach for an Artificial Life Model

Fitness =

∑

i=1

|Phenotype

−CellFavouredTrait

(3)

wSum(t) = w(1) f (1) + w(2) f (2) + . . . + w(t) f (t)

(4)

WeightedFit =



wSum

if wSum > 0.2

5 if wSum <= 0.2

(5)

4.6 Inheritance

During reproduction, crossover operations are used,

so the genetic material of the parents is combined to

form the offspring genetic material. The genotype is

immutable during an agent’s life, but there is, how-

ever, the possibility for the methylation marks to be

inherited by the offspring. In EpiAL, there are three

different mechanisms of epigenetic methylation mark

transmission: (i) faithful transmission, in which the

methylation marks are transmitted entirely to the off-

spring; (ii) complete erasure, where all the marks

are deleted from parent to offspring, being reset in

the new organism; or (iii) partial and stochastic era-

sure, in which some of the marks may be partially

erased. This partial erasure is either performed uni-

formly over the whole genotype or independently for

each gene, according to formulas 6 (DeltaMax be-

ing the maximum erasure value) and 7. Partial in-

heritance of methyl marks is exempliﬁed in ﬁgure 6.

Here, methylation values of genes 1, 4 and 9, for

the ﬁrst offspring, and genes 3, 7 and 9, for the sec-

ond offspring, are slightly decreased, compared to the

original parents’ marks. They have suffered a partial,

stochastic erasure.

∆Erase = Rand(0, DeltaMax) (6)

∀g: methyl(g)

new

= methyl(g)

current

− ∆Erase (7)

5 EXPERIMENTAL RESULTS

Several types of experiments were performed with the

EpiAL model, in order to study the mechanisms that

inﬂuence the evolution of the agents. Agents were

subject to environments where the three conditions

encoded in the world (temperature, light and food)

were modiﬁed, either periodically or non periodically.

Figure 6: Partial transfer of methylation marks.

Populations without epigenetic mechanisms are com-

pared against epigenetic ones. The epigenetic mech-

anisms - encoded in the agents’ epigenotype - remain

static and are not subject to evolution. In table 1 are

shown the main parameters used or tested, while table

2 presents the different major mechanisms that were

also experimented with in our simulations. These val-

ues were applied in several different simulations in

which the environmental setup would vary from mod-

ifying one or more conditions, periodically or non pe-

riodically, with higher or lower modiﬁcation values.

As available space does not permit the exhaustive pre-

sentation of all these results, we present two types of

experiments that demonstrate the typical behavior of

the EpiAL platform.

Table 1: Parameters for the simulations.

Parameter Value

Agent Step 1

Fitness Smooth Factor 0.5

Aging Step 1

Aging Value 0.5/1/1.5

Dying Age Base 100

World Step 1

Mating Step 10

Initial Agents 15

Offsprings (per mating) 6

Agents Initial Age [-10;-5;-1]

Epigenetic Sensing Factor [3;4]

Methyl Base Value [0.1;0.3;0.5;0.7]

Genetic Mutation Rate [0.01; 0.1]

Losers’ Hype (Tournament) [0.05; 0.15]

Figure 7 shows a simple environment where the

temperature is modiﬁed, periodically, each 100 itera-

tions.

The results (average ﬁtness values for sets of 30

runs) for this environment are shown in ﬁgure 8.

Non epigenetic populations, using tournament selec-

tion with a mutation rate of 0.01, perform poorly in

this sort of environment, whereas the epigenetic pop-

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Table 2: Methods for the simulations.

Action Method

Agent Aging Fitness Related

Agent Death Stochastic

Selection Tournament (2 Pair)

Reproduction Sexual (Crossover)

Genetic Crossover Trait Based (Gaps of 3)

Partial Methyl Erasure Overall

Figure 7: Dynamic, periodic temperature.

Figure 8: Average ﬁtness results for different populations,

regarding the environment shown in 7.

ulations, subject to the same evolutionary operations

of selection, crossover and mutation, are able to en-

dure such an environment. Along the evolutionary

time, epigenetic agents adapt to the dynamic envi-

ronment by adapting also to the epigenetic structures

they possess. Agents which evolve to take advan-

tage of the epigenetic layer, regarding the environ-

ment, are preferentially selected to reproduce. This

is performed indirectly, by selecting the agents with

the best weighted ﬁtness - i.e., during the agents’ life-

time, the ones that performed better, by regulating to

in adequate terms with the environment. As such,

along time, the population increases their adaptation

to the dynamic environment. One can, however, ob-

serve different evolutionary behaviors, regarding the

different epigenetic inheritance mechanisms. In the

case where the methylation marks are not inherited,

the ’spiked’ behavior is due to the fact that the new

agents are born from parents that are regulated with

methyl marks for the colder environment, with no

change on the genotype. As the epigenetic marks are

not transmitted, the agents are born with the genotype

for warmer environments - which are the ones actu-

ally coded in the genotype -, but are not regulated to

colder ones. Thus, there is a drop in the mean ﬁtness

values because part of the population is not epigenet-

ically adapted to the colder environment. The case

where there is no methyl mark inheritance is the worst

of the epigenetic variants, while the cases where the

inheritance occurs in full terms, or only partially, are

very similar in terms of performance.

We also experimented with non periodic, more dy-

namic environmental effects, where both temperature

and light values are non periodically modiﬁed. This

is shown in ﬁgure 9.

Figure 9: Dynamic, non periodic temperature and light.

Figure 10: Average ﬁtness results for different populations,

regarding the environment shown in 9.

The results for this scenario are shown in ﬁgure

10. The results are very similar to the previous ones,

with the non epigenetic agents showing bad perfor-

mance, whereas the epigenetic populations are able

to adapt to such an environment. The apparently sig-

niﬁcant difference in the evolutionary behavior of the

agents regarding the possession or absence of epige-

netic mechanisms poses some interesting questions

that can be tackled with the EpiAL model. For in-

stance, we experimented with a basic movement be-

havior, where some of the agents would be able to

move into different locations, while others would be

ﬁxed into the place where they are born. At the same

EPIAL - An Epigenetic Approach for an Artificial Life Model

time, the environment changes not as a whole, but

only in some speciﬁc locations (the environmental

modiﬁcation in these areas occurs as depicted in ﬁg-

ure 7). Some of the agents can, therefore, move out

of unfavorable locations, while others have to endure

the conditions from their birth location. The results

for this sort of simulation is presented in ﬁgure 11.

Figure 11: Average ﬁtness results for different populations,

regarding an environment modiﬁed in some of the areas ac-

cording to dynamics in ﬁgure 7.

Results indicate that the impact of either possess-

ing epigenetic mechanisms or not, regarding popula-

tions that can move is not nearly as signiﬁcant as for

populations that cannot move. The moving popula-

tions have a fairly similar performance whether they

are epigenetic or not. In the case of the grounded pop-

ulations, however, as they cannot move from the mod-

iﬁed areas, they are subject to harsher environments,

unless they regulate their genotypes to the new con-

ditions. This is in agreement with the epigenetic the-

ory, in which plants, organisms that, indeed, cannot

move, posess biological mechanisms that are much

more plastic than the ones in animals. Some of these

mechanisms can be considered epigenetic (Boyko and

Kovalchuk, 2008).

Regarding the results of the model in general, a

legitimate question arises, related to whether there is

a memory role at play, a way in which the agents re-

member the environmental modiﬁcations and inherit

those memories. Memory, in the terms that the agents

remember that cycles occur at a certain time, is dis-

carded by the exposure to non periodic environments

(ﬁgure 10) and the consequent adaptation of the epi-

genetic agents. There are no hard coded mechanisms

that the agents can take advantage of, in order to re-

member timed, cyclic conditions in the environment

and transmit that information to their offspring. Nev-

ertheless, it can be stated that the main role is played

by both regulation and a genetic memory factor. If

we consider that the construction of the methylation

marks are a mechanism of life-time adaptation, and

if those marks are inherited, it is only fair to state

that there is some ’knowledge’ inherited by the off-

spring. However, and because the mechanics were

built around the regulatory theory of epigenetics, that

sort of ’memory’ is derived from regulatory mecha-

nisms. The agents have no means to remember a spe-

ciﬁc state from the environment, unless the epigenetic

components allow them to mark their genotypes with

such knowledge. And, even then, such knowledge has

to evolve (genetically, at least) in order to be of any

use. Methylation, by itself, is worthless if the genetic

elements are not evolved to cooperate with the epi-

genetic mechanisms. Despite some evidence regard-

ing the partial inheritance of these acquired marks (as

studied in (Gorelick, 2004), for instance), the EpiAL

model enables the possibility to consider that this ge-

netic memory does not operate. The results obtained

from the experiments have shown that, although these

results are usually worse than the simulations with

methyl mark inheritance, there can be improved re-

sults from this solely regulatory behavior, compared

with the absence of epigenetic mechanisms at all. As

such, we can consider that the key role of the epige-

netic dimension modelled in EpiAL is mostly regula-

tory, with the genetic memory variants improving on

the results obtained by regulatory mechanisms.

6 CONCLUSIONS AND FUTURE

WORK

EpiAL, the model hereby presented, is designed with

a biological enhancement regarding epigenetic mech-

anisms. The dynamics of the environment are able

to handle multiple conditions, like temperature, light

and food, in cyclic or non periodic modiﬁcations. The

artiﬁcial agents, in turn, possess mechanisms to incor-

porate and process these environmental conditions,

allowing an epigenetic inﬂuence in their coding struc-

tures. This enables the simulation with different me-

chanics regarding the epigenetic effects, ranging from

the regulatory aspects to the inheritance methods. Us-

ing an abstraction for the phenomena of methylation

in the genome, the model is able to represent regula-

tory operations for adapting to different, dynamic en-

vironments and allows different inheritance patterns

to be experimented with. The results show that there

could be a signiﬁcant difference regarding the agents’

possession of epigenetic mechanisms. The non epige-

netic populations ﬁnd it hard to thrive in dynamic en-

vironments, while the epigenetic populations are able

to regulate themselves to dynamic conditions. More-

over, results regarding a simple behavior in the agents,

i.e., movement, have been presented, in the light of

epigenetic constraints. However, different sorts of ex-

perimentations can be performed with the actual im-

plementation of the model. It would also be inter-

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esting to perceive if nocive effects of the epigenetic

mechanisms can be found, namely the application of

mutational events either to the methylation sites or the

epigenetic structures themselves. It would also be in-

teresting to perceive if the application of these mech-

anisms could be used in the scope of problem solving

techniques. This work is but a ﬁrst step, in which we

attempted to show that there is a difference, in evo-

lutionary terms, in considering agents with an epige-

netic variant. By pursuing this work further, we can

hope to achive a better understanding of the ﬁeld of

epigenetics. The subject of epigenetics is young and

still maturing, but, as a hot subject in biology, it pro-

vides an example of an excellent opportunity to capi-

talize over the knowledge achieved in the past years,

regarding the modelling of biological phenomena. We

intend to pursue the continuous development of the

model, with a focus on two dimensions: the ﬁrst is re-

garding the biological knowledge that we believe the

EpiAL model can assist in studying and better under-

standing. The emergent ﬁelds of developmental bi-

ology (evo-devo) tackle this issues and have use for

simulation tools that can assist in theoretical specula-

tion. The other dimension is related to problem solv-

ing techniques. Problems with dynamic environments

are tackeld by several algorithmic approaches, and we

believe that the conceptual basis of the EpiAL model

has some evolutionary and adaptational mechanisms

that could provide a different approach in tackling

these problems. This twofold path is but another hint

that ALife models can be used concurrently with the

discoveries found on the ﬁeld of epigenetics and de-

velopmental biology, providing an actual relation of

biomutualism between the ﬁelds of computation and

biology.

REFERENCES

Achelis, S. (2000). Technical Analysis from A to Z.

McGraw-Hill.

Bender, J. (2004). DNA methylation and epigenetics. An-

nual Reviews Plant Biology, 55:41–68.

Boyko, A. and Kovalchuk, I. (2008). Epigenetic control of

plant stress response. Environmental and Molecular

Mutagenesis, 49:61–72.

Clune, J., Ofria, C., and Pennock, R. (2007). Investigat-

ing the emergence of phenotypic plasticity in evolving

digital organisms. Proceedings of the 9th European

Conference on Artiﬁcial Life, 4648:74–83.

Darwin, C. (1859). The Origin of Species by Means of Nat-

ural Selection. John Murray.

Dawkins, R. (1976). The Selﬁsh Gene. Oxford University

Press.

Gilbert, S. and Epel, D. (2009). Ecological Developmental

Biology. Sinauer.

Gorelick, R. (2004). Evolutionary Epigenetic Theory. PhD

thesis, Arizona State University.

Holliday, R. (1994). Epigenetics: an overview. Develop-

mental Genetics, 15(6):453–457.

Jablonka, E. and Lamb, M. (2005). Evolution in Four Di-

mensions: Genetic, Epigenetic, Behavioral, and Sym-

bolic Variation in the History of Life. MIT Press.

Ofria, C. and Wilke, C. (2004). Avida: a software plat-

form for research in computational evolutionary biol-

ogy. Artif. Life, 10(2):191–229.

Periyasamy, S., Gray, A., and Kille, P. (2008). The epige-

netic algorithm. IEEE World Congress on Computa-

tional Intelligence, pages 3228–3236.

Pigliucci, M. (2001). Phenotypic Plasticity: Beyond Nature

and Nurture. The Johns Hopkins University Press.

Rocha, L. (2007). Reality is stranger than ﬁction -

what can artiﬁcial life do about advances in bi-

ology? http://informatics.indiana.edu/rocha/dis-

cuss ecal07.html, acessed on November 10, 2008.

Rocha, L. and Kaur, J. (2007). Genotype editing and

the evolution of regulation and memory. Proceed-

ings of the 9th European Conference on Artiﬁcial Life,

4648:63–73.

Tanev, I. and Yuta, K. (2008). Epigenetic programming:

Genetic programming incorporating epigenetic learn-

ing through modiﬁcation of histones. Information Sci-

ences, 178(23):4469–4481.

Waddington, C. (1942). The epigenotype. Endeavour.

Youngson, N. and Whitelaw, E. (2008). Transgenerational

epigenetic effects. Annual Review of Genomics and

Human Genetics.

EPIAL - An Epigenetic Approach for an Artificial Life Model