A.V.Spirov, A.B.Kazansky
The Sechenov Institute of Evolutionary Physiology and Biochemistry
Thorez ave. 44, St.Petersburg, 194223, Russia.
Evolutionary Biology and Evolutionary Computations:
Parasitic Mobile Genetic Elements in Artifical Evolution
Аннотация. Мобильные Генные Элементы (МГЭ) - транспозоны сродни компьютерным вирусам, это - автономные генетические программы, передающиеся по горизонтали и вертикали, взаимодействующие с гентическими программами хозяина и
нацеленные преимущественно на умножение числа собственных копий. Эти характерные черты транспозонов использованы нами для выработки новых алгоритмов
эволюционного поиска на основе техники МГЭ. Техника МГЭ была использована
нами в компьютерных экспериментах по самопроизвольному усложнению самоорганизующихся сетей генов - контроллеров развития в процессе их коэволюции с искусственными транспозонами. Обсуждаются песпективы развития новой ветви Генетических Aлгоритмов на базе предложенного подхода.
If we can't engineer a computer that will be proud of us, we may have to evolve it.
Kelly Kevin, Out of Control.
An accident, a random change in any delicate mechanism can hardly be expected to improve it. Poking a stick into the machinery of one's watch or one's radio set will seldom
make it work better.
Theodosius Dobzhansky, Heredity and the Nature of Man.
1. INTRODUCTION
Progress in the theory and modelling of biological evolution always has an important applied side effect, stimulating development of new Evolutionary Computations techniques and
new algorithms of Artificial Life in general. Particularly, biological concept of competitive coevolution attracted great interest of A-Lifers and specialists in evolutionary computations (Menshutkin, Kazansky, 1986; Kazansky, 1988; Cliff and Miller, 1995; Maley, 1995). Well-known
artificial evolving biosystem“Tierra” (Ray, 1991) and it’s decendants such as “Bags”, “Evita” are
based on mechanisms of co-evolution of species, competing for resourses.
In context of global optimization problem, co-evolutionary systems of competing agents are
appealing because continuous changing of fitness landscape, caused by the competitive struggle
of each species can prevent stagnation of evolutionary search in the vicinity of local maxima
(Maley, 1995). Hillis (1990) reported on significant rising of the efficiency of evolution of
sorting programs after introduction of co-evolving parasites (programs deciding the test conditions for the sorting programs).
Nowadays, specialists in evolutionary computations and Genetic Algorithms are pinning
hopes on development of co-evolutionary genetic systems. Recently we contributed to this field
by proposing Mobile Genetic Elements (MGE) technique, (Spirov,1996a,b; Spirov, Samsonova,
1997; Spirov, Kazansky, Kadyrov, 1998). It is based on co-evolution of self-organizing regulatory genetic networks , controlling process of individual development and of artificial genomic
parasites - transposons. This new approach to modelling of evolutionary processes was formulated in our resent works on Drosophila embrio early development. It has been successfully tested on artificial ant problem as well. Formalization and generalization of the approach gave us
possible to give rise to a new prospective line of evolutionary computations development.
In this work we reveal biological background of new approach and set forth MGE technique. We also present some results of application of the technique to computer modelling of
Drosophila regulatory genetic networks evolution. Some promising for evolutionary computations properties of proposed artificial co-evolving system such as it's great evolvability are dicussed as well.
2. BIOLOGICAL BACKGROUND
The essential progress in study of molecular mechanisms of individual development results in
discovery of a hundreds genes, which sole function consist in the control and regulation of other
genes activity (Jackle et al., 1992). These genes comprise so-called self-organizing genetic networks (Kauffman,1993,1995). The network of regulatory genes serve to orchestrate the genome
activity during embryo development.
It is universally recognized, that the evolutionary self-organization of genetic ensembles occurs by means of genes duplications (Altenberg, 1994; Li and Noll, 1994; Wagner, 1994). The
typical story of gene origin is as follows: gene duplication (1); its fixation in the population
through selection or drift (2); maintenance of gene function by selection (3); gene evolution under mutation and selection (4).
In the 1940s, a Nobel prize winner, Barbara McClintock, predicted the existence of pieces of
DNA which could jump in and out of chromosomes - 'jumping genes'. Mobile genetic elements,
transposons, have this intra-genome self-replicating properties. It has been estimated that 80% of
spontaneous mutations are caused by transposons. Many repeated sequences in genomes may
have originated as potential transposons, favoured by selection on genetic level.
Recently the data were obtained, which evidence, that gene transpositions are involved in the
process of co-evolution with host genetic network. Some transposons may have co-evolved with
genome of their host in a result of selection at the organismic or population level. This type of
selection favours transposons which introduce useful variation through gene rearrangement. Really, it can be interpreted as an evolving macromutational mechanism.
A set of possible strategies of interrelation between Transposable Elements (TE) and Drosophila' genome is being discussed in biological literature. First of all, the destabilization of host's genome by transposons looks very promising in a context of modelling of evolution. McClintock
characterised these genetic phenomena as "genomic shock" (McClintock, 1984). Particularly, it
is worth to mark the phenomenon of hybrid dysgenesis (Lozovskaya, 1995), in which multiple
unrelated TEs are mobilised simultaneously via host genome destabilisation. As a rule, TEs re-
main silent in Drosophila genome until some stress factor (temperature, irradiation, DNA damage, the introduction of foreign chromatin, viruses, etc.) activates their elements. The insertion of
activated TEs into a number of loci leads to alteration of gene expression pattern.
It is this burst of transpositions that we realised in our computer experiments. In other words,
we simulate the situation of disbalanced system of TE - Drosophila genome. An estimated typical rate of transpositions in natural populations of Drosophila (number of transpositions per element per generation) is of the order of 10^-4 (Charlesworth, 1992). Rate of transpositions, realized in our computer experiments is two or three orders of magitude higher.
3. MODELLING OF GENETIC NETWORKS EVOLUTION
Gene ensembles, controlling the segmentation in insects (Patel, 1994) were chosen as a very
convenient modelling object. The characteristic feature of organization of genetic networks in
these organisms is the control of genes activity at early and late stages of embryogenesis by different regulatory elements, as well as tissue- and stage-specific activation of transposons (Ding
and Lipshitz, 1994; Smith and Corces, 1995). Therefore besides genes, acting at early embryogenesis stages, i.e. at stage of segmentation, we incorporate into our model several genes, functioning only at the subsequent developmental stages of development. We suppose, that the activation of these genes at the stage of segmentation could take place as the result of transposable
elements insertion. An approach to simulation of the evolution of genetic network is based on the
scheme of population dynamics viz. repeating cycles of mutations and/or crossover, selection
and reproduction. It is common scheme, generally used in the GA-and GP-techniques. We proposed new, Transposable Elements technique (Spirov, Kazansky, Kadyrov, 1998), which is expanded classical scheme. Namely, we use the following non-classic elements and operators: variable-length genomes and corresponding variable-length operators of duplication, elimination
and random addition (1); parasitic mobile genetic elements in evolving genomes (2); operators of
competition and co-operation between parasitic genetic elements (3); operators of transposition
(4).
VARIABLE-LENGTH GENOMES. We use non-traditional for the classical GA-approach (but
inherent in GP - technique) variable-length genome (chromosome). As well, as in GP-technique,
the growth of genome in our algorithm is limited. Specifically, every genome can have up to nine
separated variable-length strings-chromosomes . Each string can include up to six four-letter
words separated by spacers. We use three-letter alphabet (A, T, C). In toto we have 81 different
4-letter words. Each gene recognises nine target sites in other genes. Each word corresponds to
the number which are used for evaluation of Hill's law parameters.
In our concrete model every chromosome contain only one gene. Variable-length operators
of duplication, elimination and random addition can be applied independently to every chromosome (gene). The list of operators, acting on chromosomes include the mentioned above variable-length operators, the classical operators of point mutations and recombinations, as well as
operators of transposition, defined below.
VARABLE-LENGTH OPERATORS. The action of these operators on genome will result in duplication, addition of fragment or deletion of chosen at random chromosome (i.e. gene).
SIMULATIONS OF RECRUITMENT OF A NEW GENES INTO GENE NETWORK
We named the process of incorporation of new gene into network as the gene recruitment. At
the initial state the genotypes of individuals include one active gene (gene A). The gradient of M
factor (morphogene) controlling A-gene expression in concentration-dependent manner is predetermined in initial population. Thus low concentration of M factor activate gene A, while high
concentration repress it. The target sites (4-letter words) for M in the A become the first "shooting mark" for the attack of transposable elements. Pool of free genes includes genes unusable at
initial stage.
By definition, gene can regulate only those genes, which contain the target sites for its product. On the other hand, from generation to generation population is infected with site-specific
mobile genetic elements, coming from external "source". The infection is possible only provided
that gene A contain one of the sites-targets for the morphogen M. So, from the very beginning
the whole population can be infected with this "virus". In the model under discussion only the
stabilizing selection of A-gene pattern is used. But a specially introduced MGE is able for insertion only into the regulatory region of initial, "wild" type of A-gene. After the insertion of
deleter, an infected individual can live no longer than 9 cycles of reproduction, leaving only
scanty progeny. The transposon can be transmitted both horizontally ( to the other host) and vertically (to the next generation).
In this situation the indirect (conditional) selective advantage could have mutants with the
transformed network of regulatory relationships. We mean the individuals with a new recruited
gene and with the entirely substituted regulatory region of A-gene. In this computer model the
indirect selective pressure causes the recruiting of gene into network and to closing of "reserved"
cascade of regulation of gene A by gene-neophyte B and later on, by gene D as well. It is clear,
that lack of site for deleter's incertion garantee the selective advantage to the mutants, which
have this property. The mutants emerge very soon in the course of computer evolution, but, in
view of indirect character of selection they begin to prevail in population only after the laps of
many hundreds cycles of reproduction. Moreover, the indirect pressure of selection results in
high heterogeneity of mutants, because they are selected in the model not only by score, but by
the resistance to the MGE as well.
Furthermore, the variability of deleter's site-specificity is allowed in the model of genetic network evolution. Though, this variability is realised with very low frequency. In so doing, the insertion of the MGE is possible in regulatory regions of gene A or gene B (provided the suitable
sequence for insertion is present). In a result, if suitable new lines of this genetic parasite emerges late enough in the course of computer evolution, than the prospects for selection of mutants
with the new gene-recruit D and with the closure of the new, "reserved" cascade of regulation by
genes B and D will appear. Genes A, B and later on, gene D are the activators of their own targets, working in accordance with the discussed earlier concentration-dependent mechanism.
RESULTS AND DISCUSSION
Analysing the results of set of computer experiments with the model of genomic networks
evolution, we came to following conclusions:
1. The probability of spontaneous emergence of structurally redundant genomic networks with
several genes - neophyts is very high at the initial stages of artificial evolution. (This effect is one
of possible candidates for explanation of so-called Cambrian explosion, the well-known in the
history of biosphere biodiversity outburst, started about 500 million years ago);
2. Populations of genomic networks with one-two and afterwards, with three- four new recruited
genes appear for a very short period of “big explosion”;
3. Emergence of mutants of genomic parasites always accompany the explosion of new forms of
the hosts;
4. After the lapse of several tens of reproductive cycles of computer evolution, the numbers of
practically all redundant genomic networks and of their genetic parasites is abruptly reduces. As
a rule, only two forms of genomic networks ( initial network and one of the redundant networks)
dominate for the very long subsequent time period, lasting for thousands of reproductive cycles;
5. Sporadic emergence of fluctuations of networks-hosts and their parasites abundances are characteristic for this long period of gradual development;
6. There is high probability of the repetition of the explosion of diversity of redundant forms and
their parasites in a thousands of reproductive cycles after the last explosion.
7. In general, artificial evolution has features of quasy-periodical process of alternation of short
periods of diversity explosion and long periods of relatively gradual development.
The successive complication of functional organization of genome in the course of computer
evolution is evident. It is essential, that this complication is the result of indirect selection pressure action. We have not prescribed any explicit criteria of selective advantages for genomes,
which formed these selforganizing genomic regulatory networks. The succession of events in
computer evolution of genomic networks looks very realistic from biological point of view. It
corresponds to our knowledge about the inter-relationships between host's genome and MGE and
looks promising as for application in the field of evolutionary computations.
Recruitment of genes and functional complication of networks in our model is possible only
due to mobile parasitic elements’ activity.
The coevolution of programs-hosts and subroutines-parasites provided a new insight into the
evolutionary process in general. The coevolution-evolution cycle of host and parasite system has
new features, not reducible to the sum of evolutions of a single species taken sepately.
These simulations also contribute to the old evolutionary debates of gradualism versus punctuated equilibrium adherents. Gradualism maintained that evolutionary changes were small but
constant between generations of organisms; punctuated equilibrium proposed that evolutionary
changes came suddenly, spurred on by large environmental disturbances. Our computer experiments demonstrate typically non-graduate character of computer evolution, where long quasiequilibrium dynamics suddenly, from time to time bursts with many new complicated forms
(evolutionary outbursts).
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