Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

Evolvability: What Is It and How Do We Get It?

Biological organisms exhibit spectacular adaptation to their environments. However, another marvel of biology lurks behind the adaptive traits that organisms exhibit over the course of their lifespans: it is hypothesized that biological organisms also exhibit adaptation to the evolutionary process itself. That is, biological organisms are thought to possess traits that facilitate evolution. The term evolvability was coined to describe this type of adaptation. The question of evolvability has special practical relevance to computer science researchers engaged in longstanding efforts to harness evolution as an algorithm for automated design. It is hoped that a more nuanced understanding of biological evolution will translate to more powerful digital evolution techniques. This thesis presents a theoretical overview of evolvability, illustrated with examples from biology and evolutionary computing

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

State Dependent Regulation of the Neural Circuit for C. Elegans Feeding

Rhythmic muscular contractions are essential for many different behaviors, from locomotion to respiration. These behaviors are modulated by changes in the external environment, such as temperature shifts and presence of predators, and by internal states, such as hunger and sleep. The roundworm Caenorhabditis elegans feeds on bacteria through rhythmic contraction and relaxation of its pharynx, a neuromuscular pump innervated by a nearly independent network of just 20 neurons. Feeding rate is modulated by many environmental and physiological factors, but feeding generally persists throughout the life of the worm, ceasing only during sleep. The mechanisms by which the pharyngeal nervous system controls feeding during wake and sleep are poorly understood. I used optogenetics, genetics, and pharmacology to define the cholinergic pharyngeal circuitry that regulates feeding rate during wake, and then used similar approaches to examine how feeding is inhibited during sleep. I identified a four-neuron circuit that regulates feeding rate during wake and found that it is degenerate, meaning that multiple different classes of neurons can stimulate feeding in a similar manner. I also found that feeding quiescence is generated by distinct mechanisms during two behaviorally indistinguishable sleep states: cholinergic motor neurons are inhibited during stress-induced sleep, while the muscle is directly inhibited during developmentally timed sleep. Thus, as in mammals and despite its behavioral homogeneity, sleep in C. elegans is not a physiologically homogenous state. These results provide insight into the function of a highly conserved neural circuit that generates robust rhythmic behavior, and illustrate how this circuit can be altered in different ways to produce the same behavioral output during two distinct sleep states

Rethinking emotion: New research in emotion and recent debates in cognitive science.

Cognitive science is currently the scene of a number of exciting debates. The so-called 'classical' approach, which has dominated the field since the 1950s, is increasingly being challenged on various fronts. Evolutionary psychologists and researchers in artificial life accuse classical cognitive scientists of ignoring the fact that natural cognition is not designed to solve abstract problems and prove theorems but to solve particular adaptive problems. Those working with a 'situated' view of the mind are challenging the classical commitment to internalism. Finally, proponents of dynamical approaches claim that the discrete models favoured by the classical approach are too coarse-grained and impute too much internal structure to the mind. In this thesis I argue that the 'non-classical' approaches are compatible with classical cognitive science, with the important proviso that compatibility comes in different kinds. In the final chapter I outline a vision of a comprehensive 'integrated non-classical cognitive science' that combines the three non-classical approaches into a single conceptual bundle. I illustrate these claims about cognitive science in general with reference to a particular field of research: the emotions. Emotions were ignored by most classical cognitive scientists, though some models of emotion were developed within the classical framework. These models, however, provided no way of distinguishing emotion from cognition. I argue that the non-classical approaches remedy this problem, and together provide a new way of thinking about the emotions which I dub 'the interruption theory'. Since the interruption theory borrows insights from all three of the non-classical forms of cognitive science, it serves as a good example of the integrated non-classical approach that I recommend for cognitive science in general

Models in molecular evolution: the case of toyLIFE

Mención Internacional en el título de doctorThis thesis set out to contribute to the growing body of knowledge pertaining models of the genotype-phenotype map. In the process, we proposed and studied a new computational model, toyLIFE, and a new metaphor for molecular evolution —adaptive multiscapes. We also studied functional promiscuity and the evolutionary dynamics of shifting environments. The first result of this thesis was the definition of toyLIFE, a simplified model of cellular biology that incorporated toy versions of genes, proteins and regulation as well as metabolic laws. Molecules in toyLIFE interact between each other following the laws of the HP protein folding model, which endows toyLIFE with a simplified chemistry. From these laws, we saw how something reminiscent of cell-like behavior emerged, with complex regulatory and metabolic networks that grew in complexity as the genome increased. toyLIFE is, to our knowledge, the first multi-level model of the genotype- phenotype map, compared to previous models studied in the literature, such as RNA, proteins, gene regulatory networks (GRNs) or metabolic networks. All of these models either disregarded cellular context when assigning phenotype and function (RNA and proteins) or omitted genome dynamics, by defining their genotypes from high-level abstractions (GRNs and metabolic networks). toyLIFE shares the same features exhibited by all genotype-phenotype maps studied so far. There is strong degeneracy in the map, with many genotypes mapping into the same phenotype. This degeneracy translates into the existence of neutral networks, that span genotype space as soon as the genotype contains more than two genes. There is also a strong asymmetry in the size distribution of phenotypes: most phenotypes were rare, while a few of them covered most genotypes. Moreover, most common phenotypes are easily accessed from each other. We also studied the prevalence of functional promiscuity (the ability to perform more than one function) in computational models of the genotypephenotype map. In particular, we studied RNA, Boolean GRNs and toy- LIFE. Our results suggest that promiscuity is the norm, rather than the exception. These results prompt us to rethink our understanding of biology as a neatly functioning machine. One of the most interesting results of this thesis came from studying the evolutionary dynamics of shifting environments in populations showing functional promiscuity: our results show that there is an optimal frequency of change that minimizes the time to extinction of the population. Finally, we presented a new metaphor for molecular evolution: adaptive multiscapes. This framework intends to update the fitness landscape metaphor proposed by Sewall Wright in the 1930s. Adaptive multiscapes include many features that we have learned from computational studies of the genotype-phenotype map, and that have been discussed throughout the thesis. The existence of neutral networks, the asymmetry in phenotype sizes -and the concomitant asymmetry in phenotype accessibility- and the presence of functional promiscuity all alter the original fitness landscape picture.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Joshua Levy Payne.- Secretario: Saúl Arés García.- Vocal: Jacobo Aguirre Arauj

Co-evolution of morphology and control in developing structures

The continuous need to increase the efficiency of technical systems requires the utilization of complex adaptive systems which operate in environments which are not completely predictable. Reasons are often random nature of the environment and the fact that not all phenomena which influence the performance of the system can be explained in full detail. As a consequence, the developer often gets confronted with the task to design an adaptive system with the lack of prior knowledge about the problem at hand. The design of adaptive systems, which react autonomously to changes in their environment, requires the coordinated generation of sensors, providing information about the environment, actuators which change the current state of the system and signal processing structures thereby generating suitable reactions to changed conditions. Within the scope of the thesis, the new system growth method has been introduced. It is based on the evolutionary optimization design technique, which can automatically produce the efficient systems with optimal initially non-defined configuration. The final solutions produced by the novel growth method have low dimensional perception, actuation and signal processing structures optimally adjusted to each other during combined evolutionary optimization process. The co-evolutionary system design approach has been realized by the concurrent development and gradual complexification of the sensory, actuation and corresponding signal processing systems during entire optimization. The evolution of flexible system configuration is performed with the standard evolutionary strategies by means of adaptable representation of variable length and therewith variable complexity of the system which it can represent in the further optimization progress. The co-evolution of morphology and control of complex adaptive systems has been successfully performed for the examples of a complex aerodynamic problem of a morphing wing and a virtual intelligent autonomously driving vehicle. The thesis demonstrates the applicability of the concurrent evolutionary design of the optimal morphological configuration, presented as sensory and actuation systems, and the corresponding optimal system controller. Meanwhile, it underlines the potentials of direct genotype – phenotype encodings for the design of complex engineering real-world applications. The thesis argues that often better, cheaper, more robust and adaptive systems can be developed if the entire system is the design target rather than its separate functional parts, like sensors, actuators or controller structure. The simulation results demonstrate that co-evolutionary methods are able to generate systems which can optimally adapt to the unpredicted environmental conditions while at the same time shedding light on the precise synchronization of all functional system parts during its co-developmental process