Developmental selection and self-organization

Natural selection. V. How to read the fundamental equations of evolutionary change in terms of information theory

The equations of evolutionary change by natural selection are commonly expressed in statistical terms. Fisher's fundamental theorem emphasizes the variance in fitness. Quantitative genetics expresses selection with covariances and regressions. Population genetic equations depend on genetic variances. How can we read those statistical expressions with respect to the meaning of natural selection? One possibility is to relate the statistical expressions to the amount of information that populations accumulate by selection. However, the connection between selection and information theory has never been compelling. Here, I show the correct relations between statistical expressions for selection and information theory expressions for selection. Those relations link selection to the fundamental concepts of entropy and information in the theories of physics, statistics and communication. We can now read the equations of selection in terms of their natural meaning. Selection causes populations to accumulate information about the environment.

Evolvable neuronal paths: a novel basis for information and search in the brain

We propose a previously unrecognized kind of informational entity in the brain that is capable of acting as the basis for unlimited hereditary variation in neuronal networks. This unit is a path of activity through a network of neurons, analogous to a path taken through a hidden Markov model. To prove in principle the capabilities of this new kind of informational substrate, we show how a population of paths can be used as the hereditary material for a neuronally implemented genetic algorithm, (the swiss-army knife of black-box optimization techniques) which we have proposed elsewhere could operate at somatic timescales in the brain. We compare this to the same genetic algorithm that uses a standard 'genetic' informational substrate, i.e. non-overlapping discrete genotypes, on a range of optimization problems. A path evolution algorithm (PEA) is defined as any algorithm that implements natural selection of paths in a network substrate. A PEA is a previously unrecognized type of natural selection that is well suited for implementation by biological neuronal networks with structural plasticity. The important similarities and differences between a standard genetic algorithm and a PEA are considered. Whilst most experiments are conducted on an abstract network model, at the conclusion of the paper a slightly more realistic neuronal implementation of a PEA is outlined based on Izhikevich spiking neurons. Finally, experimental predictions are made for the identification of such informational paths in the brain.

Chronic fatigue syndrome defies the mind-body-schism of medicine. New perspectives on a multiple realisable developmental systems disorder

The article maintains that chronic fatigue syndrome can be properly understood only by taking an integrated perspective in which evolutionary, developmental and ecological aspects are considered. The integrative approach, supplemented by a complexity theory and psychoneuroimmunological research, is capable of explaining why there are so few structural aberrations to be found in chronic fatigue syndrome and why specific treatment is so difficult to establish. A major outcome of the investigation, that all individuals with chronic fatigue syndrome are diseased in their own way, emphasises the need to study the development of personalised life histories. It also highlights an ethical dimension; personalised disease defies essentialist thinking on patient management. Another major outcome, which follows from the developmental systems perspective, is the dissolution of ontological mind-body dualism. This in turn allows for a methodological complementation of the biological and phenomenological approaches to knowledge. New research strategies that may help to resolve chronic fatigue syndrome, grounded in the revised perspective on individual development, are suggested.

The evolution of signaling complexity suggests a mechanism for reducing the genomic search space in human association studies

The size complexity of the human genome has been traditionally viewed as an obstacle that frustrates efforts aimed at identifying the genetic correlates of complex human phenotypes. As such complex phenotypes are attributed to the combined action of numerous genomic loci, attempts to identify the underlying multi-locus interactions may produce a combinatorial sum of false positives that drown out the real signal. Faced with such grim prospects for successfully identifying the genetic basis of complex phenotypes, many geneticists simply disregard epistatic interactions altogether. However, the emerging picture from systems biology is that the cellular programs encoded by the genome utilize nested signaling hierarchies to integrate a number of loosely coupled, semiautonomous, and functionally distinct genetic networks. The current view of these modules is that connections encoding inter-module signaling are relatively sparse, while the gene-to-gene (protein-to-protein) interactions within a particular module are typically denser. We believe that each of these modules is encoded by a finite set of discontinuous, sequence-specific, genomic intervals that are functionally linked to association rules, which correlate directly to features in the environment. Furthermore, because these environmental association rules have evolved incrementally over time, we explore theoretical models of cellular evolution to better understand the role of evolution in genomic complexity. Specifically, we present a conceptual framework for (1) reducing genomic complexity by partitioning the genome into subsets composed of functionally distinct genetic modules and (2) improving the selection of coding region SNPs, which results in an increased probability of identifying functionally relevant SNPs. Additionally, we introduce the notion of 'genomic closure,' which provides a quantitative measure of how functionally insulated a specific genetic module might be from the influence of the rest of the genome. We suggest that the development and use of theoretical models can provide insight into the nature of biological systems and may lead to significant improvements in computational algorithms designed to reduce the complexity of the human genome.

Aggregate statistical measures and metapopulation dynamics

There are two main types of metapopulation models. Spatially implicit models are analytically tractable but neglect spatial heterogeneities. Spatially explicit models are more realistic but too complex. In this paper, I build a bridge between both approximations. I derive a new metapopulation model using a well-known technique in population genetics. Spatial heterogeneities are captured by an aggregate statistical measure of spatial correlation. When this correlation is zero, i.e., space is homogeneous, the model becomes the well-known Levins' model. As spatial correlation increases, equilibrium patch occupancy decreases from what would be expected under the spatially homogeneous assumption. I proceed by testing how well spatial complexities from a spatially explicit simulation can be encapsulated by such an aggregate statistical measure.

The pattern of variation in centipede segment number as an example of developmental constraint in evolution

The range of animal morphologies observed in nature is partly determined by natural selection. However, there is no agreement yet regarding whether it is also partly determined by developmental constraint. Testing for the effects of constraint has been difficult due to the lack of both an appropriate null model and a sufficiently simple system capable of yielding unambiguous results regarding the model's plausibility. Here we examine the case of variation in segment number in geophilomorph centipedes. Curiously, while this ranges between 29 and 191, there are no species in which an even number of segments is observed, in contrast to about 1000 species with odd numbers of segments. It seems unlikely that this distribution of character values is determined by selection alone. Using an approach based on Bayesian inference, we attempt to quantify the probability of obtaining the observed distribution of values given a null model in which developmental constraint is absent. Since this probability is in the region of 10(-20), we conclude that constraint must be involved. We discuss various implications of this conclusion, and comment on the unexpected absence of neoteny and progenesis in centipede evolution. Copyright 1999 Academic Press.

Hereditary stability and variation in evolution and development

Evolution and development are both lineage processes but are often conceptualized as occurring by different and mutually exclusive mechanisms. It is conventionally asserted that evolution occurs via the random generation of diversity and the subsequent survival of those that pass selection. On the other hand, development is too often presented as proceeding via the unfolding of a deterministic program encoded in the DNA sequence. In biology, universal generalizations are rare and dogmas are often wrong for particular cases. Deterministic mechanisms contribute some of the new DNA sequences that subsequently become substrates for natural selection. Conversely, stochastic and selective mechanisms are intrinsic to development, and also to maintenance of the immune, and possibly, nervous systems. Cancer appears to be another process that straddles distinctions between evolutionary and developmental modes of hereditary change and stabilization. DNA sequence changes are an essential feature of many cancers, but there are also aspects of the disease similar to developmental lineage gone awry. The literature suggests that the cellular changes that give rise to cancer occur by mechanisms commonly associated with both evolutionary and developmental lineage pathways.