Mechanisms Of Biological Evolution Research Articles

Hybrid Genetic Algorithm with an Adaptive Penalty Function for Fitting Multimodal Experimental Data: Application to Exchange-Coupled Non-Kramers Binuclear Iron Active Sites

A Genetic Algorithm (GA) is a stochastic optimization technique based on the mechanisms of biological evolution. These algorithms have been successfully applied in many fields to solve a variety of complex nonlinear problems. While they have been used with some success in chemical problems such as fitting spectroscopic and kinetic data, many have avoided their use due to the unconstrained nature of the fitting process. In engineering, this problem is now being addressed through incorporation of adaptive penalty functions, but their transfer to other fields has been slow. This study updates the Nanakorrn Adaptive Penalty function theory, expanding its validity beyond maximization problems to minimization as well. The expanded theory, using a hybrid genetic algorithm with an adaptive penalty function, was applied to analyze variable temperature variable field magnetic circular dichroism (VTVH MCD) spectroscopic data collected on exchange coupled Fe(II)Fe(II) enzyme active sites. The data obtained are described by a complex nonlinear multimodal solution space with at least 6 to 13 interdependent variables and are costly to search efficiently. The use of the hybrid GA is shown to improve the probability of detecting the global optimum. It also provides large gains in computational and user efficiency. This method allows a full search of a multimodal solution space, greatly improving the quality and confidence in the final solution obtained, and can be applied to other complex systems such as fitting of other spectroscopic or kinetics data.

La dimension biogéographique de l’évolution de la Vie

The role that biogeography plays in the mechanisms of biological evolution is strongly debated. Darwin’s ideas are discussed in the present paper on the basis of a short review of the role of the geographic dimension in speciation processes, and of the basic concepts and classic models of biogeography. Darwin’s model of dispersal from a “centre of origin” was proposed when proofs of continental motion were not yet conclusive. The vicariant model, opposed to the dispersal one, is now supported by plate tectonics and ocean floor expansion. These two classic models of biogeography could correspond to cases of biogeographic convergence and divergence, which are related to palaeogeographic changes.

Prediction of RNA structures containing pseudoknots

This paper describes a genetic algorithm for predicting RNA structures that contain various types of pseudoknots. Pseudoknotted RNA structures are much more difficult to predict by computational methods than RNA secondary structures, as they are more complex and the analysis is time-consuming. We developed an efficient genetic algorithm to predict RNA folding structures containing any type of pseudoknot, as well as a novel initial population method to decrease computational complexity and increase the accuracy of the results. We also used an interaction filter to decrease the size of the possible stem lists for long RNA sequences. We predicted RNA structures using a number of different termination conditions and compared the validity of the results and the times required for the analyses. The algorithm proved able to predict efficiently RNA structures containing various types of pseudoknots. Corresponding Author: Kyungsook Han (Email: khan@inha.ac.kr) This work was supported by the Korea Science and Engineering Foundation (KOSEF) under grant R01-2003000-10461-0. Introduction The prediction of an RNA structure with a pseudoknot using computational methods requires much computation. Predicting the most stable structure with minimal free energy from an RNA sequence is an optimization problem (Lee and Han, 2002; Lee and Han, 2003; Deiman and Pleij, 1997). Computational methods for predicting RNA structure generally make use of two algorithms, one combinatorial the other recursive. The combinatorial algorithm first creates an inventory of all possible stem lists that can be formed by a given RNA sequence, and then determines the combination with the lowest free energy. This algorithm has the advantage that it can include pseudoknot structures, but the number of possible structures increases immensely with sequence length (Rivas and Eddy, 1999; Akutsu, 2000). The recursive algorithm finds the lowest free energy structure from the sub-fragments of a sequence. It makes a systematic search of all sub-fragments for the lowest free energy structure containing at least one base pair. The first sub-fragments considered are those capable of forming a hairpin loop closed by a single base pair. So in a first pass it will find the lowest free energy structures for all pentanucleotides in the sequence. This method always finds the structure with least free energy, but it does not identify structures such as pseudoknots because of their computational complexity. A genetic algorithm (GA) is an optimization procedure that implements the mechanism of biological evolution. It begins with a set of solutions called populations. Solutions are then taken and used to form a new population in the hope that the new population will be superior to the old one. They are selected to generate new solutions according to their fitness; the fitter they are, the more opportunities they have to reproduce. This procedure is repeated until some specified condition is satisfied. Genetic algorithms have been theoretically and empirically proven to provide robust searches in highly complex and uncertain spaces, and they are finding widespread application in commerce, science and engineering. They are computationally simple and powerful search methods, and many workers have used them to predict RNA structures and sequence alignments; they have been used to seek optimal and sub-optimal secondary RNA structures (Benedetti and Morosetti, 1995; Shapiro and Navetta, 1994) and to simulate RNA folding pathways (Gultyaev et al., 1995; Shapiro et al., 2001). Massively parallel genetic algorithms have been employed to predict RNA structures that include pseudoknots (Shapiro and Wu, 1996; Shapiro and Wu, 1997). However the structures predicted contained only H (Hairpin)-type pseudoknots and the computations were extremely complex as they used randomly generated initial populations. Dynamic programming algorithms also used to predict RNA structures including pseudoknots (Rivas and Eddy, 1999) again could only predict structures with H type pseudoknots, and only from short RNA sequences. We have developed a GA that is able to predict efficiently

Evolutionary computing: the rise of electronic breeding

GAs and their relations, which fall under the umbrella term evolutionary computing, are being harnessed to optimize designs of all sorts. GAs mimics the mechanisms of biological evolution. Populations of individuals evolve by means of reproduction, inheritance, mutation, natural selection, and recombination or crossover (two organisms swap a portion of their genetic code). The result is computational methods that build a population of individuals or designs based on a set of criteria and constraints.

Phenomenon of life: between equilibrium and non-linearity.

A model of ordering applicable to biological evolution is presented. It is shown that a steady state (more precisely approaching to a steady state) system of irreversible processes, under conditions of disproportionation of entropy, produces a lower-entropy product, that is, ordering. The ordering is defined as restricting of degrees of freedom: freedom of motion, interactions etc. The model differs from previous ones in that it relates the ordering to processes running not far from equilibrium, described in the linear field of non-equilibrium thermodynamics. It is shown that a system, which includes adenosine triphosphate (ATP) to adenosine diphosphate (ADP) conversion meets the demands of the physical model: it provides energy maintaining steady state conditions, and hydrolysis of ATP proceeding with consumption of water can be tightly conjugated with the most important reactions of synthesis of organic polymers (peptides, nucleotide chains etc.), which proceed with release of water. For these and other reasons ATP seems to be a key molecule of prebiotic evolution. It is argued that the elementary chemical reaction proceeding under control of an enzyme is not necessarily far from equilibrium. The experimental evidence supporting this idea, is presented. It is based on isotope data. Carbon isotope distribution in biochemical systems reveals regularity, which is inherent to steady state systems of chemical reactions, proceeding not far from equilibrium. In living organisms this feature appears at the statistical level, as many completely irreversible and non-linear processes occur in organisms. However not-far-from-equilibrium reactions are inherent to biochemical systems as a matter of principle. They are reconcilable with biochemical behavior. Extant organisms are highly evolved entities which, however, show in their basis the same features, as the simplest chemical systems must have had been involved in the origin of life. Some consequences following from the model, which may be significant for understanding the origin of life and the mechanism of biological evolution, are pointed out.

Internal Structure of Elementary Particle and Possible Deterministic Mechanism of Biological Evolution

The possibility of a complicated internal structure of an elementary particle was analyzed. In this case a particle may represent a quantum computer with many degrees of freedom. It was shown that the probability of new species formation by means of random mutations is negligibly small. Deterministic model of evolution is considered. According to this model DNA nucleotides can change their state under the control of elementary particle internal degrees of freedom.

The splendid feast of reason

Jonathan Singer's witty, erudite book is a celebration of rationality and an urgent call to make use of intelligence and reason to better cope with human problems. Emphasizing the importance of rationality's greatest achievement, modern science, Singer - one of the foremost biologists of our era - argues that for the first time in several million years humanity has at its disposal the tools for an objective understanding of the external world. Singer demonstrates that, today more than ever, the fullest exercise of rationality is essential if humanity is to rein in a runaway technology and control an explosion of the human population that together threaten to devastate life on this planet within only a few more generations. intrusion of reason and rationality into our largely irrational world has been painfully slow, uneven, and often unwelcome. Singer explains that for rationalists the founding of modern science - which took place only a few hundred years ago - has overthrown many of the myths of conventional wisdom and dogmas of traditional religions. Yet these beliefs still hold sway over the irrational world, obstructing efforts to deal sensibly with the problematic future of mankind. core of The Splendid Feast of Reason is an engaging and accessible account of the knowledge that modern science provides. Singer offers an absorbing discussion of how life works, of the nature of reproduction, aging, and death, and of the necessary fragility of the individual life compared to the resilience of life itself. He emphasizes the primary role of the genes in determining the structural organization and the behaviors of living things, including humans. He also stresses the nature and mechanisms of biological evolution, mechanisms that have now been placed in jeopardy because of human ignorance and irrational appetites. Finally, Singer delves into the enigma of the real world with its irrational and chaotic operations and offers suggestions of how a rationalist can not only survive, but thrive in it.

HOW DOES THE TEILHARDIAN VISION OF EVOLUTION COMPARE WITH CONTEMPORARY THEORIES?

Abstract. Teilhard de Chardin's ideas about the mechanisms of biological evolution are revised and their connections with contemporary theories are reported. Teilhard de Chardin's main contribution is the proposal of a new scientific discipline, geobiology—the science of the biosphere evolving as a whole. The main fields of interest of geobiology are reported, and its relationships with contemporary hypotheses, such as Lovelock's Gaia, are discussed. The consequences of this kind of approach are the parallel evolution described as orthogenesis and the presence of canalization phenomena. These Teilhardian hypotheses are discussed in relation to those of the process structuralists and to the novelties of the molecular evolution of the genome. Conclusions are that the mechanisms discussed by Teilhard are presently taken into consideration by contemporary evolutionists in order to construct a new theory of biological evolution.

Equations for biological evolution

In this paper we consider mathematical models inspired by the mechanisms of biological evolution. We take populations which are subject to interaction and mutation. In the cases we consider, the interaction is through competition or through a prey-predator relationship. The models consider the specific characteristics as taking values in real intervals and the equations are of the integro—differential type. In the case of competition, thanks to the fact that some of the equations have solutions which are quite explicit, we succeed in proving the existence of attracting stationary solutions. In the case of prey and predator, using techniques of dynamical systems in infinite-dimensional spaces, we succeed in showing the existence of a global attractor, which in some instances reduces to a point. Our analysis takes into account having δ distributions, corresponding to all individuals having the same characteristics, as possible populations.

The origins of the neutral theory of molecular evolution.

Molecular biology has had a profound impact on the nature of the biological sciences in the twentieth century. Molecular techniques are found now in virtually every area of biology. To reduce the impact of molecular biology to the dissemination of its technologies, however, is to sell it short: it has also dramatically altered researchers' attitudes toward the question which problems or problem areas are of fundamental importance. This shift in attitude is nowhere more evident than in hybrid fields such as molecular evolution. When the first major conferences on molecular evolution were held in 1964,1 the vast majority of evolutionary biologists saw the world through the lens of panselectionism natural selection was accepted as the dominant and most important mechanism of biological evolution.2 This panselectionism was a product of the evolutionary synthesis in the 1930s and 1940s and the related effort to demonstrate the central role of evolution within the biological sciences. Architects of the evolutionary synthesis, such as George G. Simpson and Ernst Mayr, attended these early conferences on molecular evolution and actively promoted the power of natural selection.3 For Mayr and Simpson, molecular biology was threat-

Biological and cultural evolution: Different manifestations of the same principle. A systems-theoretical approach

Sociobiology started as the study of the biological evolution of social behavior. Human behavior, however, is the result of the interaction of both biological and cultural factors. Therefore, a theory of human sociobiology should be focused on the interaction of biological and cultural evolution. The recent development of gene-culture coevolutionary theory is a first step into that direction though it is still far from being applicable to concrete situations. One of the main reasons for this is that so far a comprehensive theory of cultural evolution is lacking, thereby leaving unclear the precise nature of the influence of genetic change on cultural change, and vice versa. To be sure, attempts have been made to explain cultural evolution by simply transferring the mechanisms of biological evolution to a cultural setting. This approach, however, is not satisfactory, mainly because the principles that allow the conservation and transfer of information in cultural evolution are quite different from those in biological evolution. In fact, if both types of evolution are different expressions of the same principle, as the names suggest, it should be possible to formulate a more basic definition of evolution. This definition, then, could be used as the starting point for the construction of a theory of cultural evolution. Using concepts from general systems theory, this paper attempts to provide such a basis by formulating a generalized definition of evolution, which applies both to biological and cultural evolution.