How morphology of artificial organisms influences their evolution

International audienceThe principle of natural selection implies that variations are transmitted from parents to offsprings. The individuals with advantageous variations have better fitness. Consequently, such variations spread in the population and influence its evolution. This schematic description is conventionally accepted but it jumps over an important step: how variations are related to fitness. In the other words, how the phenotype is related to the reproduction and mortality rates. It is important to note that this relation will not be imposed by the assumptions of the model but it should follow from the morphology of the artificial organisms. In order to study this question, we will introduce in this work virtual populations of artificial organisms and will observe their behavior. The main idea of this study is that we prescribe individual characteristics of the organisms (size, form) but not their behavior in the search for resources. The model presented below will allow us to study on a simple example the interaction between morphology and natural selection, or, in a more general formulation, the evolution of the phenotype. 1.1. Artificial life models Artificial life models are largely used to study behavior of biological organisms at the individual level, their collective behavior and evolution. We will consider a complete life cycle model which includes the genotype of the organisms in its relation to the phenotype, the mechanism of motion and food search determined by the morphology of the organisms, and reproduction (Fig. 1). A B S T R A C T The purpose of this work is to study virtual populations of artificial organisms with their genotype, morphology, mechanism of motion, search and competition for food, reproduction, mutations. The genotype determines the phenotype (morphology), while morphology determines efficiency of motion and success in the search for food in the competition with other individuals; sufficient amount of food allows reproduction. Ensemble of these elements constitutes the minimal model to study natural selection of artificial organisms. Considering only some of them, as it is often the case in artificial life models, can be used for the optimization of some properties (for example, robot's gait or embryo's form) but not to study natural selection in the evolutionary context. Artificial organisms are considered in this work in the form of polygons (triangles) on the plane. Their genotype is given by three positive numbers associated to the vertices and their morphology is determined by the lengths of the sides equal the sum of the numbers in the adjacent vertices. Behavior of the individuals and their success in the search for food depend on their morphology. More efficient individuals will reproduce more than the others and will transmit their advantageous variations to their offsprings. Hence we can observe how natural selection chooses more efficient morphology and how it evolves due to random mutations. We develop an individual based model where the individuals recognize food and move to it with the speed determined by their morphology (and not prescribed in the algorithm). If they have enough food, they survive and reproduce. Therefore morphology and evolution are tightly interconnected and should be studied together. Dynamics of such populations appears to be different from the dynamics described by conventional models of competition and evolution of species. In particular, a new phenotype can emerge due to a different strategy of foraging (related to a different morphology) and not only due to a difference in consumed resources with the existing phenotype. We also observe that realization of Cope's rule (increase of body size in the process of evolution) can depend on parameters of the model.

Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system

Biology has taken strong steps towards becoming a computer science aiming at reprogramming nature after the realisation that nature herself has reprogrammed organisms by harnessing the power of natural selection and the digital prescriptive nature of replicating DNA. Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back. These concepts provide a means to a hierarchical organization thereby blurring previously clear-cut lines between concepts like matter and life, or between tumour types that are otherwise taken as different and may not have however a different cause. This does not diminish the properties of life or make its components and functions less interesting. On the contrary, this approach makes for a more encompassing and integrated view of nature, one that subsumes observer and observed within the same system, and can generate new perspectives and tools with which to view complex diseases like cancer, approaching them afresh from a software-engineering viewpoint that casts evolution in the role of programmer, cells as computing machines, DNA and genes as instructions and computer programs, viruses as hacking devices, the immune system as a software debugging tool, and diseases as an information-theoretic battlefield where all these forces deploy. We show how information theory and algorithmic programming may explain fundamental mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George Ellis (eds.), Cambridge University Pres

Results of Evolution Supervised by Genetic Algorithms

A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.Comment: 6 pages, 1 Table, 2 figure

Ecological Modelling with the Calculus of Wrapped Compartments

The Calculus of Wrapped Compartments is a framework based on stochastic multiset rewriting in a compartmentalised setting originally developed for the modelling and analysis of biological interactions. In this paper, we propose to use this calculus for the description of ecological systems and we provide the modelling guidelines to encode within the calculus some of the main interactions leading ecosystems evolution. As a case study, we model the distribution of height of Croton wagneri, a shrub constituting the endemic predominant species of the dry ecosystem in southern Ecuador. In particular, we consider the plant at different altitude gradients (i.e. at different temperature conditions), to study how it adapts under the effects of global climate change.Comment: A preliminary version of this paper has been presented in CMC13 (LNCS 7762, pp 358-377, 2013

Complexity, BioComplexity, the Connectionist Conjecture and Ontology of Complexity\ud

This paper develops and integrates major ideas and concepts on complexity and biocomplexity - the connectionist conjecture, universal ontology of complexity, irreducible complexity of totality & inherent randomness, perpetual evolution of information, emergence of criticality and equivalence of symmetry & complexity. This paper introduces the Connectionist Conjecture which states that the one and only representation of Totality is the connectionist one i.e. in terms of nodes and edges. This paper also introduces an idea of Universal Ontology of Complexity and develops concepts in that direction. The paper also develops ideas and concepts on the perpetual evolution of information, irreducibility and computability of totality, all in the context of the Connectionist Conjecture. The paper indicates that the control and communication are the prime functionals that are responsible for the symmetry and complexity of complex phenomenon. The paper takes the stand that the phenomenon of life (including its evolution) is probably the nearest to what we can describe with the term “complexity”. The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity. With life and its evolution as the substrate, the paper develops ideas towards the ontology of complexity. The paper introduces new complexity theoretic interpretations of fundamental biomolecular parameters. The paper also develops ideas on the methodology to determine the complexity of “true” complex phenomena.\u