Adaptive walks in a gene network model of morphogenesis: insights into the Cambrian explosion

The emergence of complex patterns of organization close to the Cambrian boundary is known to have happened over a (geologically) short period of time. It involved the rapid diversification of body plans and stands as one of the major transitions in evolution. How it took place is a controversial issue. Here we explore this problem by considering a simple model of pattern formation in multicellular organisms. By modeling gene network-based morphogenesis and its evolution through adaptive walks, we explore the question of how combinatorial explosions might have been actually involved in the Cambrian event. Here we show that a small amount of genetic complexity including both gene regulation and cell-cell signaling allows one to generate an extraordinary repertoire of stable spatial patterns of gene expression compatible with observed anteroposterior patterns in early development of metazoans. The consequences for the understanding of the tempo and mode of the Cambrian event are outlined.Comment: to appear in International Journal of Developmental Biology, special issue on Evo-Devo (2003

Necessary Conditions for Open-Ended Evolution

Evolution on Earth is widely considered to be an effectively endless process. Though this phenomenon of open-ended evolution (OEE) has been a topic of interest in the artificial life community since its beginnings, the field still lacks an empirically validated theory of what exactly is necessary to reproduce the phenomenon in general (including in domains quite unlike Earth). This dissertation (1) enumerates a set of conditions hypothesized to be necessary for OEE in addition to (2) introducing an artificial life world called Chromaria that incorporates each of the hypothesized necessary conditions. It then (3) describes a set of experiments with Chromaria designed to empirically validate the hypothesized necessary conditions. Thus, this dissertation describes the first scientific endeavor to systematically test an OEE framework in an alife world and thereby make progress towards solving an open question not just for evolutionary computation and artificial life, but for science in general

Contextualising primate origins - an ecomorphological framework

Ecomorphology - the characterisation of the adaptive relationship between an organism's morphology and its ecological role - has long been central to theories of the origin and early evolution of the primate order. This is exemplified by two of the most influential theories of primate origins: Matt Cartmill's Visual Predation Hypothesis, and Bob Sussman's Angiosperm Co-Evolution Hypothesis. However, the study of primate origins is constrained by the absence of data directly documenting the events under investigation, and has to rely instead on a fragmentary fossil record and the methodological assumptions inherent in phylogenetic comparative analyses of extant species. These constraints introduce particular challenges for inferring the ecomorphology of primate origins, as morphology and environmental context must first be inferred before the relationship between the two can be considered. Fossils can be integrated in comparative analyses and observations of extant model species and laboratory experiments of form-function relationships are critical for the functional interpretation of the morphology of extinct species. Recent developments have led to important advancements, including phylogenetic comparative methods based on more realistic models of evolution, and improved methods for the inference of clade divergence times, as well as an improved fossil record. This contribution will review current perspectives on the origin and early evolution of primates, paying particular attention to their phylogenetic (including cladistic relationships and character evolution) and environmental (including chronology, geography, and physical environments) contextualisation, before attempting an up-to-date ecomorphological synthesis of primate origins

The Decoupling Hypothesis: A new idea for the origin of hominid bipedalism

Theoretical adaptive landscapes and mathematical representations of key constraints of evolutionary and primate biology are used to propose a new hypothesis for the origin of hominid bipedalism. These constraints suggest that the selective pressure that produced this novel form of locomotion was the need for effective suspensory and terrestrial movement. This testable hypothesis, termed the Decoupling Hypothesis, posits that bipedalism is an adaptation that enables the shoulder to maintain a high degree of mobility, a feature important to suspensory behaviors, in the face of significant demands for a high degree of stability, a feature important for highly effective terrestrial quadrupedism. Activity budgets and locomotor and postural behaviors of 18 primate groups, derived from published literature, were used to test a prediction of the Decoupling Hypothesis that bipedalism is a predictable behavior in primates which is correlated with intense demands for shoulder mobility and stability. Time was used as a proxy for estimating conflicting demands for shoulder stability and mobility. Bipedalism, as a proportion of all above-substrate locomotion, was predicted using logistic regression including seven linear variables and four two-way interaction terms. All possible regressions, using R2 and Mallow’s Cp as criterion, and stepwise variable selection procedures were used to determine significant variables. The model with a relatively high R2 (0.86) and the lowest Mallow’s Cp (-1.62), contained the following predictor variables: shoulder-abduction locomotion (p \u3c 0.0001), shoulder-abduction posture (p = 0.0003), and an interaction terms, shoulder-abduction locomotion by above-substrate locomotion (p = 0.011). The significant interaction term, predicted by the Decoupling Hypothesis, supports the hypothesis and suggests that further consideration is warranted

Material properties affect evolution's ability to exploit morphological computation in growing soft-bodied creatures

The concept of morphological computation holds that the body of an agent can, under certain circumstances, exploit the interaction with the environment to achieve useful behavior, potentially reducing the computational burden of the brain/controller. The conditions under which such phenomenon arises are, however, unclear. We hypothesize that morphological computation will be facilitated by body plans with appropriate geometric, material, and growth properties, while it will be hindered by other body plans in which one or more of these three properties is not well suited to the task. We test this by evolving the geometries and growth processes of soft robots, with either manually-set softer or stiffer material properties. Results support our hypothesis: we find that for the task investigated, evolved softer robots achieve better performances with simpler growth processes than evolved stiffer ones. We hold that the softer robots succeed because they are better able to exploit morphological computation. This four-way interaction among geometry, growth, material properties and morphological computation is but one example phenomenon that can be investigated using the system here introduced, that could enable future studies on the evolution and development of generic soft-bodied creatures

Harnessing the Power of Collective Intelligence: the Case Study of Voxel-based Soft Robots

The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the research community, there is a strong feeling that a paradigm change has become necessary to disentangle ER. In particular, a solution has emerged from ideas from Collective Intelligence (CI). In CI---which has many relevant examples in nature---behavior emerges from the interaction between several components. In the absence of central intelligence, collective systems are usually more adaptable. In this thesis, we set out to harness the power of CI, focusing on the case study of simulated Voxel-based Soft Robots (VSRs): they are aggregations of homogeneous and soft cubic blocks that actuate by altering their volume. We investigate two axes. First, the morphologies of VSRs are intrinsically modular and an ideal substrate for CI; nevertheless, controllers employed until now do not take advantage of such modularity. Our results prove that VSRs can truly be controlled by the CI of their modules. Second, we investigate the spatial and time scales of CI. In particular, we evolve a robot to detect its global body properties given only local information processing, and, in a different study, generalize better to unseen environmental conditions through Hebbian learning. We also consider how evolution and learning interact in VSRs. Looking beyond VSRs, we propose a novel soft robot formalism that more closely resembles natural tissues and blends local with global actuation.The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the research community, there is a strong feeling that a paradigm change has become necessary to disentangle ER. In particular, a solution has emerged from ideas from Collective Intelligence (CI). In CI---which has many relevant examples in nature---behavior emerges from the interaction between several components. In the absence of central intelligence, collective systems are usually more adaptable. In this thesis, we set out to harness the power of CI, focusing on the case study of simulated Voxel-based Soft Robots (VSRs): they are aggregations of homogeneous and soft cubic blocks that actuate by altering their volume. We investigate two axes. First, the morphologies of VSRs are intrinsically modular and an ideal substrate for CI; nevertheless, controllers employed until now do not take advantage of such modularity. Our results prove that VSRs can truly be controlled by the CI of their modules. Second, we investigate the spatial and time scales of CI. In particular, we evolve a robot to detect its global body properties given only local information processing, and, in a different study, generalize better to unseen environmental conditions through Hebbian learning. We also consider how evolution and learning interact in VSRs. Looking beyond VSRs, we propose a novel soft robot formalism that more closely resembles natural tissues and blends local with global actuation

Rafting: A Post-Flood Biogeographic Dispersal Mechanism

Although biogeography has contributed important data to the debate on biological origins for centuries, global biogeographic models have had poor success at explaining biogeographic data. Heretofore, the best models (evolutionary biogeography models) have neither successfully explained the multi-taxon concurrence of trans-oceanic range disjunctions nor why areas of endemism exist where they do. Here a creationist dispersal mechanism is suggested. It is proposed that plants and animals rafted across oceans on and among masses of logs, plant debris, and vegetation mats in the immediate post-Flood world. United with post-Flood models of geology, climatology, and biology, a uniquely young-age creationist model of biogeography is generated which seems to explain not only the data explained by the best evolutionary models, but also data which such models fail to explain. Also introduced are eighteen biogeographic tests of the model