A Framework for Implementing Metaheuristic Algorithms Using Intercellular Communication.

Metaheuristics (MH) are Artificial Intelligence procedures that frequently rely on evolution. MH approximate difficult problem solutions, but are computationally costly as they explore large solution spaces. This work pursues to lay the foundations of general mappings for implementing MH using Synthetic Biology constructs in cell colonies. Two advantages of this approach are: harnessing large scale parallelism capability of cell colonies and, using existing cell processes to implement basic dynamics defined in computational versions. We propose a framework that maps MH elements to synthetic circuits in growing cell colonies to replicate MH behavior in cell colonies. Cell-cell communication mechanisms such as quorum sensing (QS), bacterial conjugation, and environmental signals map to evolution operators in MH techniques to adapt to growing colonies. As a proof-of-concept, we implemented the workflow associated to the framework: automated MH simulation generators for the gro simulator and two classes of algorithms (Simple Genetic Algorithms and Simulated Annealing) encoded as synthetic circuits. Implementation tests show that synthetic counterparts mimicking MH are automatically produced, but also that cell colony parallelism speeds up the execution in terms of generations. Furthermore, we show an example of how our framework is extended by implementing a different computational model: The Cellular Automaton.

Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records.

The present work explores the diagnostic performance for depression of neural network classifiers analyzing the sound structures of laughter as registered from clinical patients and healthy controls. The main methodological novelty of this work is that simple sound variables of laughter are used as inputs, instead of electrophysiological signals or local field potentials (LFPs) or spoken language utterances, which are the usual protocols up-to-date. In the present study, involving 934 laughs from 30 patients and 20 controls, four different neural networks models were tested for sensitivity analysis, and were additionally trained for depression detection. Some elementary sound variables were extracted from the records: timing, fundamental frequency mean, first three formants, average power, and the Shannon-Wiener entropy. In the results obtained, two of the neural networks show a diagnostic discrimination capability of 93.02 and 91.15% respectively, while the third and fourth ones have an 87.96 and 82.40% percentage of success. Remarkably, entropy turns out to be a fundamental variable to distinguish between patients and controls, and this is a significant factor which becomes essential to understand the deep neurocognitive relationships between laughter and depression. In biomedical terms, our neural network classifier-based neuroprosthesis opens up the possibility of applying the same methodology to other mental-health and neuropsychiatric pathologies. Indeed, exploring the application of laughter in the early detection and prognosis of Alzheimer and Parkinson would represent an enticing possibility, both from the biomedical and the computational points of view.

An extended Moran process that captures the struggle for fitness.

When a new type of individual appears in a stable population, the newcomer is typically not advantageous. Due to stochasticity, the new type can grow in numbers, but the newcomers can only become advantageous if they manage to change the environment in such a way that they increase their fitness. This dynamics is observed in several situations in which a relatively stable population is invaded by an alternative strategy, for instance the evolution of cooperation among bacteria, the invasion of cancer in a multicellular organism and the evolution of ideas that contradict social norms. These examples also show that, by generating different versions of itself, the new type increases the probability of winning the struggle for fitness. Our model captures the imposed cooperation whereby the first generation of newcomers dies while changing the environment such that the next generations become more advantageous.

Bacterial computing: a form of natural computing and its applications.

The capability to establish adaptive relationships with the environment is an essential characteristic of living cells. Both bacterial computing and bacterial intelligence are two general traits manifested along adaptive behaviors that respond to surrounding environmental conditions. These two traits have generated a variety of theoretical and applied approaches. Since the different systems of bacterial signaling and the different ways of genetic change are better known and more carefully explored, the whole adaptive possibilities of bacteria may be studied under new angles. For instance, there appear instances of molecular "learning" along the mechanisms of evolution. More in concrete, and looking specifically at the time dimension, the bacterial mechanisms of learning and evolution appear as two different and related mechanisms for adaptation to the environment; in somatic time the former and in evolutionary time the latter. In the present chapter it will be reviewed the possible application of both kinds of mechanisms to prokaryotic molecular computing schemes as well as to the solution of real world problems.

Biometry of stomata in Blechnum species (Blechnaceae) with some taxonomic and ecological implications for the ferns.

Morphological stomatal traits, such as size, form and frequency, have been subject of much literature, including their relationships with environmental factors. However, little effort have focused on ferns, and very few in the genus Blechnum. Stomatal length, width and frequency (as stomatal index) of a number of specimens of fourteen Neotropical species of Blechnum were measured in adult pinnae. The aim of the work was to find biometrical relationships between stomatal traits and between stomatal traits and habit, habitat and ecosystem of the plants. Statistical analyses of data were conducted using Exploratory Data Analysis and Multivariate Statistical Methods. Stomatal length and width showed a very high correlation, suggesting an endogenous, genetic control, thus giving these traits a considerable diagnostic utility. With respect to the relationships between stomatal traits and environment, we found significant statistical relationships between altitude and stomatal index. We also addressed the interpretation of the ecological-selective significance of various assemblages of stomatal traits in a diverse conjunction of habits, habitats and ecosystems.

Biometry of stomata in Blechnum species (Blechnaceae) with some taxonomic and ecological implications for the ferns

Morphological stomatal traits, such as size, form and frequency, have been subject of much literature, including their relationships with environmental factors. However, little effort have focused on ferns, and very few in the genus Blechnum. Stomatal length, width and frequency (as stomatal index) of a number of specimens of fourteen Neotropical species of Blechnum were measured in adult pinnae. The aim of the work was to find biometrical relationships between stomatal traits and between stomatal traits and habit, habitat and ecosystem of the plants. Statistical analyses of data were conducted using Exploratory Data Analysis and Multivariate Statistical Methods. Stomatal length and width showed a very high correlation, suggesting an endogenous, genetic control, thus giving these traits a considerable diagnostic utility. With respect to the relationships between stomatal traits and environment, we found significant statistical relationships between altitude and stomatal index. We also addressed the interpretation of the ecological- selective significance of various assemblages of stomatal traits in a diverse conjunction of habits, habitats and ecosystems. Rev. Biol. Trop. 59 (1): 403-415. Epub 2011 March 01.

Los caracteres morfológicos estomáticos, tales como tamaño, forma y frecuencia, han sido objeto de abundante investigación, incluyendo su relación con los factores ambientales. Sin embargo, poco esfuerzo se ha realizado en esta materia en helechos y menos todavía en el género Blechnum. En este trabajo se midieron la longitud, anchura y frecuencia (como índice estomático) de estomas de pinnas adultas de un número de individuos en catorce especies de Blechnum neotropicales. El objetivo fue encontrar relaciones biométricas entre los caracteres estomáticos, y entre los caracteres estomáticos y el hábito, hábitat y ecosistema de las plantas. Se realizaron análisis estadísticos como Análisis Exploratorios de Datos y Métodos Estadísticos Multivariantes. La longitud y la anchura de los estomas mostraron una muy fuerte correlación, sugiriendo un control genético endógeno que otorga a estos caracteres un considerable valor diagnóstico. Con respecto a las relaciones entre los caracteres estomáticos y el ambiente, encontramos una relación estadísticamente significativa entre la altitud y el índice estomático. También se incluyen interpretaciones de la significación ecológico- selectiva de un conjunto de caracteres estomáticos en diferentes conjuntos de hábitos, hábitats y ecosistemas.

Cellular "bauplans": evolving unicellular forms by means of Julia sets and Pickover biomorphs.

The universe of cellular forms has received scarce attention by mainstream neo-Darwinian views. The possibility that a fundamental trait of biological order may consist upon, or be guided by, developmental processes not completely amenable to natural selection was more akin to previous epochs of biological thought, i.e. the "bauplan" discussion. Thirty years ago, however, Lynn and Tucker studied the biological mechanisms responsible for defining organelles position inside cells. The fact that differentiated structures performing a specific function within the eukaryotic cell (i.e. mitochondrion, vacuole, or chloroplast) were occupying specific positions in the protoplasm was the observational and experimental support of the 'morphogenetic field' notion at the cellular level. In the present paper we study the morphogenetic field evolution yielding from an initial population of undifferentiated cells to diversified unicellular organisms as well as specialized eukaryotic cell types. The cells are represented as Julia sets and Pickover biomorphs, simulating the effect of Darwinian natural selection with a simple genetic algorithm. The morphogenetic field "defines" the locations where cells are differentiated or sub-cellular components (or organelles) become organized. It may be realized by different possibilities, one of them by diffusing chemicals along the Turing model. We found that Pickover cells show a higher diversity of size and form than those populations evolved as Julia sets. Another novelty is the way that cellular organelles and cell nucleus fill in the cell, always in dependence on the previous cell definition as Julia set or Pickover biomorph. Our findings support the existence of specific attractors representing the functional and stable form of a differentiated cell-genuine cellular bauplans. The configuration of the morphogenetic field is "attracted" towards one or another attractor depending on the environmental influences as modeled by a particular fitness function. The model promotes the classical discussions of D'Arcy Thompson and the more recent views of Waddington, Goodwin and others that consider organisms as dynamical systems that evolve through a 'master plan' of transformations, amenable to natural selection. Intriguingly, the model also connects with current developments on mechanobiology, highlighting the informational-developmental role that cytoskeletons may play.

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network.

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.