Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation

Precious ecological information extracted from limnological long-term time series advances the theory on functioning and evolution of freshwater ecosystems. This paper presents results of applications of artificial neural networks (ANN) and evolutionary algorithms (EA) for ordination, clustering, forecasting and rule discovery of complex limnological time-series data of two distinctively different lakes. Ten years of data of the shallow and hypertrophic Lake Kasumigaura (Japan) are utilized in comparison with 13 years of data of the deep and mesotrophic Lake Soyang (Korea). Results demonstrate the potential that: (1) recurrent supervised ANN and EA facilitate 1-week-ahead forecasting of outbreaks of harmful algae or water quality changes, (2) EA discover explanatory rule sets for timing and abundance of harmful outbreaks algal populations, and (3) non-supervised ANN provide clusters to unravel ecological relationships regarding seasons, water quality ranges and long-term environmental changes.     

Extensive evolutionary rate variation in floral color determining genes in the genus Ipomoea

Flower color is an important adaptive trait in many plant species because it determines reproductive success through differential attractiveness to insect pollinators. The genus Ipomoea is a pan tropically distributed plant genus characterized by showy flowers that often differ in color among closely related species. Flower color is determined primarily by products of the anthocyanin biosynthesis pathway. To determine whether flower color evolution among members of the genus is driven by adaptive molecular evolution of the anthocyanin pathway genes, we analyzed data from 10 genes sequenced from 19 species of Ipomoea . Six protein-coding genes from the anthocyanin pathway were drawn for evolutionary analysis in addition to three genes from the unrelated leucine biosynthesis pathway and one MADS box regulatory gene for comparison. The analyses provided: (i) no convincing evidence for positive selection on anthocyanin pathway structural genes, or on the other sampled genes, despite shifts in flower color among species included in the sample; (ii) pathway position correlated weakly with estimates of the intensity of evolutionary constraint on the anthocyanin pathway enzyme coding genes; and (iii) there was substantial gene-specific heterogeneity in the rates of synonymous site evolution. Synonymous rate heterogeneity does not appear to be accounted for by codon bias or local contextual or compositional sequence differences, leading us to implicate heterogeneous rates of mutation among genes as the most probable cause of synonymous rate heterogeneity.     

Robustness and evolvability: a paradox resolved

Understanding the relationship between robustness and evolvability is key to understand how living things can withstand mutations, while producing ample variation that leads to evolutionary innovations. Mutational robustness and evolvability, a system's ability to produce heritable variation, harbour a paradoxical tension. On one hand, high robustness implies low production of heritable phenotypic variation. On the other hand, both experimental and computational analyses of neutral networks indicate that robustness enhances evolvability. I here resolve this tension using RNA genotypes and their secondary structure phenotypes as a study system. To resolve the tension, one must distinguish between robustness of a genotype and a phenotype. I confirm that genotype (sequence) robustness and evolvability share an antagonistic relationship. In stark contrast, phenotype (structure) robustness promotes structure evolvability. A consequence is that finite populations of sequences with a robust phenotype can access large amounts of phenotypic variation while spreading through a neutral network. Population-level processes and phenotypes rather than individual sequences are key to understand the relationship between robustness and evolvability. My observations may apply to other genetic systems where many connected genotypes produce the same phenotypes.     

Occurrence of MIR Elements in the Complete Nucleotide Sequence of Human Chromosome 22

The location of mammalian interspersed repeats (MIRs) and their density have been determined in the complete nucleotide sequence of human chromosome 22. The approach developed by us has allowed detection of 9675 MIRs at a statistically significant level, which by 15% exceeds the MIR number revealed by all previous approaches. It has been demonstrated that a considerable amount of MIRs missed by the algorithms applied earlier occurs in known DNA sequences of the human genome. The study of the MIR density revealed substantial irregularity of their distribution along the chromosome. The data on the MIRs thus found and the computer program searching for diverged sequences are available by E-mail: katrin2@mail.ru or katrin22@mtu-net.ru.     

Fluctuations in neighbourhood fertility generate variable signalling effort

Studies of sexual signalling generally focus on interactions between dyadic pairs, yet communication in natural populations often occurs in the context of complex social networks. The ability to survey social environments and adjust signal production appropriately should be a critical component of success in these systems, but has rarely been documented empirically. Here, we used autonomous recording devices to identify 118 472 songs produced by 26 male common yellowthroats (Geothlypis trichas) over two breeding seasons, coupled with detailed surveys of social conditions on each territory. We found strong evidence that common yellowthroat males adjusted their total song production in response to both changes in within-pair social context and changes in the fertility of neighbouring females up to 400 m away. Within the social pair, males drastically reduced their song production when mated, but the magnitude of this reduction depended on both the time of day and on the fertility status of the social mate. By contrast, when fertile females were present on nearby territories, males increased their song output, especially during daytime singing. At this time, it is unclear whether males actively gathered information on neighbouring female fertility or whether the patterns that we observed were driven by changes in social interactions that varied with neighbourhood fertility. Regardless of the mechanism employed, however, subtle changes in the social environment generated substantial variation in signalling effort.     

Selforganizing memory: active learning of landmarks used for navigation

We propose a memory architecture that is suited to solve a specific task, namely homing, that is finding a not directly visible home place by using visually accessible landmarks. We show that an agent equipped with such a memory structure can autonomously learn the situation and can later use its memory to accomplish homing behaviour. The architecture is based on neuronal structures and grows in a self-organized way depending on experience. The basic architecture consists of three parts, (i) a pre-processor, (ii) a simple, one-layered feed-forward network, called distributor net, and (iii) a full recurrently connected net for representing the situation models to be stored. Apart from Hebbian learning and a local version of the delta-rule, explorative learning is applied that is not based on passive detection of correlations, but is actively searching for interesting hypotheses. Hypotheses are spontaneously introduced and are verified or falsified depending on how well the network representing the hypothesis approaches an internal error of zero. The stability of this approach is successfully tested by removal of one landmark or shifting the position of one or several landmarks showing results comparable to those found in biological experiments. Furthermore, we applied noise in two ways. The trained network was either due to sensory noise or to noise applied to the bias weights describing the memory content. Finally, we tested to what extent learning of the weights is affected by noisy input given to the sensor data. The architecture proposed is discussed to have some at least superficial similarity to the mushroom bodies of insects.     

Epistasis can lead to fragmented neutral spaces and contingency in evolution

In evolution, the effects of a single deleterious mutation can sometimes be compensated for by a second mutation which recovers the original phenotype. Such epistatic interactions have implications for the structure of genome space--namely, that networks of genomes encoding the same phenotype may not be connected by single mutational moves. We use the folding of RNA sequences into secondary structures as a model genotype-phenotype map and explore the neutral spaces corresponding to networks of genotypes with the same phenotype. In most of these networks, we find that it is not possible to connect all genotypes to one another by single point mutations. Instead, a network for a phenotypic structure with n bonds typically fragments into at least 2(n) neutral components, often of similar size. While components of the same network generate the same phenotype, they show important variations in their properties, most strikingly in their evolvability and mutational robustness. This heterogeneity implies contingency in the evolutionary process.     

Recurrent epidemics in small world networks

The effect of spatial correlations on the spread of infectious diseases was investigated using a stochastic susceptible-infective-recovered (SIR) model on complex networks. It was found that in addition to the reduction of the effective transmission rate, through the screening of infectives, spatial correlations have another major effect through the enhancement of stochastic fluctuations, which may become considerably larger than in the homogeneously mixed stochastic model. As a consequence, in finite spatially structured populations significant differences from the solutions of deterministic models are to be expected, since sizes even larger than those found for homogeneously mixed stochastic models are required for the effects of fluctuations to be negligible. Furthermore, time series of the (unforced) model provide patterns of recurrent epidemics with slightly irregular periods and realistic amplitudes, suggesting that stochastic models together with complex networks of contacts may be sufficient to describe the long-term dynamics of some diseases. The spatial effects were analysed quantitatively by modelling measles and pertussis, using a susceptible-exposed-infective-recovered (SEIR) model. Both the period and the spatial coherence of the epidemic peaks of pertussis are well described by the unforced model for realistic values of the parameters.     

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.     

Learning to represent visual input

One of the central problems in computational neuroscience is to understand how the object-recognition pathway of the cortex learns a deep hierarchy of nonlinear feature detectors. Recent progress in machine learning shows that it is possible to learn deep hierarchies without requiring any labelled data. The feature detectors are learned one layer at a time and the goal of the learning procedure is to form a good generative model of images, not to predict the class of each image. The learning procedure only requires the pairwise correlations between the activations of neuron-like processing units in adjacent layers. The original version of the learning procedure is derived from a quadratic ‘energy’ function but it can be extended to allow third-order, multiplicative interactions in which neurons gate the pairwise interactions between other neurons. A technique for factoring the third-order interactions leads to a learning module that again has a simple learning rule based on pairwise correlations. This module looks remarkably like modules that have been proposed by both biologists trying to explain the responses of neurons and engineers trying to create systems that can recognize objects.     

Algorithms in nature: the convergence of systems biology and computational thinking

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high‐level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future.