Relating bisimulations with attractors in boolean network models

When studying a biological regulatory network, it is usual to use boolean network models. In these models, boolean variables represent the behavior of each component of the biological system. Taking in account that the size of these state transition models grows exponentially along with the number of components considered, it becomes important to have tools to minimize such models. In this paper, we relate bisimulations, which are relations used in the study of automata (general state transition models) with attractors, which are an important feature of biological boolean models. Hence, we support the idea that bisimulations can be important tools in the study some main features of boolean network models.We also discuss the differences between using this approach and other well-known methodologies to study this kind of systems and we illustrate it with some examples.

Global transcriptome analysis of Lactococcus garvieae strains in response to temperature

Lactococcus garvieae is an important fish and an opportunistic human pathogen. The genomic sequences of several L. garvieae strains have been recently published, opening the possibility of global studies on the biology of this pathogen. In this study, a whole genome DNA microarray of two strains of L. garvieae was designed and validated. This DNA microarray was used to investigate the effects of growth temperature (18°C and 37°C) on the transcriptome of two clinical strains of L. garvieae that were isolated from fish (Lg8831) and from a human case of septicemia (Lg21881). The transcriptome profiles evidenced a strain-specific response to temperature, which was more evident at 18°C. Among the most significant findings, Lg8831 was found to up-regulate at 18°C several genes encoding different cold-shock and cold-induced proteins involved in an efficient adaptive response of this strain to low-temperature conditions. Another relevant result was the description, for the first time, of respiratory metabolism in L. garvieae, whose gene expression regulation was temperature-dependent in Lg21881. This study provides new insights about how environmental factors such as temperature can affect L. garvieae gene expression. These data could improve our understanding of the regulatory networks and adaptive biology of this important pathogen.

Plant Genes Related to Gibberellin Biosynthesis and Signaling Are Differentially Regulated during the Early Stages of AM Fungal Interactions

Dear Editor, Phytohormones are essential regulators of plant development, but their role in the signaling processes between plants and fungi during arbuscular mycorrhizal (AM) establishment is far from being understood (Ludwig-Müller, 2010). AM colonization leads to extensive effects on host metabolism, as revealed by transcriptome studies of AM plants (Hogekamp et al., 2011). Some genes have been specified as an AM core set, since they are mycorrhizal-responsive, irrespective of the identity of the plant, of the fungus, and of the investigated organ. These data support the idea that, on colonization, plants activate a wide reprogramming of their major regulatory networks and argue that mobile factors of fungal or plant origin are involved in such generalized metabolic changes. In this context, hormones may be good candidates (Bonfante and Genre, 2010). However, the emerging picture of the interaction between phytohormones and AMs is very patchy, and information on gibberellin (GA) involvement is still more limited (García-Garrido et al., 2010). The role of GA during nodulation is instead known to control the nodulation signaling pathway (Ferguson et al., 2011).

Genetic networks controlling retinal injury.

PURPOSE: The present study defines genomic loci underlying coordinate changes in gene expression following retinal injury. METHODS: A group of acute phase genes expressed in diverse nervous system tissues was defined by combining microarray results from injury studies from rat retina, brain, and spinal cord. Genomic loci regulating the brain expression of acute phase genes were identified using a panel of BXD recombinant inbred (RI) mouse strains. Candidate upstream regulators within a locus were defined using single nucleotide polymorphism databases and promoter motif databases. RESULTS: The acute phase response of rat retina, brain, and spinal cord was dominated by transcription factors. Three genomic loci control transcript expression of acute phase genes in brains of BXD RI mouse strains. One locus was identified on chromosome 12 and was highly correlated with the expression of classic acute phase genes. Within the locus we identified the inhibitor of DNA binding 2 (Id2) as a candidate upstream regulator. Id2 was upregulated as an acute phase transcript in injury models of rat retina, brain, and spinal cord. CONCLUSIONS: We defined a group of transcriptional changes associated with the retinal acute injury response. Using genetic linkage analysis of natural transcript variation, we identified regulatory loci and candidate regulators that control transcript levels of acute phase genes.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (C) 2009 Elsevier B.V. All rights reserved.

Host genetic background affects regulatory T-cell activity that influences the magnitude of cellular immune response against Mycobacterium tuberculosis

Using two mouse strains with different abilities to generate interferon (IFN)-gamma production after Mycobacterium tuberculosis infection, we tested the hypothesis that the frequency and activity of regulatory T (Treg) cells are influenced by genetic background. Our results demonstrated that the suppressive activity of spleen Treg cells from infected or uninfected BALB/c mice was enhanced, inhibiting IFN-gamma and interleukin (IL)-2 production. Infected C57BL/6 mice exhibited a decrease in the frequency of lung Treg cells and an increased ratio CD4(+):CD4(+)Foxp3(+) cells compared with infected BALB/c mice and uninfected C57BL/6 mice. Moreover, infected C57BL/6 mice also had a decrease in the immunosuppressive capacity of spleen Treg cells, higher lung IFN-gamma and IL-17 production, and restricted the infection better than BALB/c mice. Adoptive transfer of BALB/c Treg cells into BALB/c mice induced an increase in bacterial colony-forming unit (CFU) counts. Furthermore, BALB/c mice treated with anti-CD25 antibody exhibited lung CFU counts significantly lower than mice treated with irrelevant antibody. Our results show that in BALB/c mice, the Treg cells have a stronger influence than that in C57BL/6 mice. These data suggest that BALB/c and C57BL/6 mice may use some different mechanisms to control M. tuberculosis infection. Therefore, the role of Treg cells should be explored during the development of immune modulators, both from the perspective of the pathogen and the host. Immunology and Cell Biology (2011) 89, 526-534; doi:10.1038/icb.2010.116; published online 19 October 2010

The evolution of controlled multitasked gene networks: The role of introns and other noncoding RNAs in the development of complex organisms

Eukaryotic phenotypic diversity arises from multitasking of a core proteome of limited size. Multitasking is routine in computers, as well as in other sophisticated information systems, and requires multiple inputs and outputs to control and integrate network activity. Higher eukaryotes have a mosaic gene structure with a dual output, mRNA (protein-coding) sequences and introns, which are released from the pre-mRNA by posttranscriptional processing. Introns have been enormously successful as a class of sequences and comprise up to 95% of the primary transcripts of protein-coding genes in mammals. In addition, many other transcripts (perhaps more than half) do not encode proteins at all, but appear both to be developmentally regulated and to have genetic function. We suggest that these RNAs (eRNAs) have evolved to function as endogenous network control molecules which enable direct gene-gene communication and multitasking of eukaryotic genomes. Analysis of a range of complex genetic phenomena in which RNA is involved or implicated, including co-suppression, transgene silencing, RNA interference, imprinting, methylation, and transvection, suggests that a higher-order regulatory system based on RNA signals operates in the higher eukaryotes and involves chromatin remodeling as well as other RNA-DNA, RNA-RNA, and RNA-protein interactions. The evolution of densely connected gene networks would be expected to result in a relatively stable core proteome due to the multiple reuse of components, implying,that cellular differentiation and phenotypic variation in the higher eukaryotes results primarily from variation in the control architecture. Thus, network integration and multitasking using trans-acting RNA molecules produced in parallel with protein-coding sequences may underpin both the evolution of developmentally sophisticated multicellular organisms and the rapid expansion of phenotypic complexity into uncontested environments such as those initiated in the Cambrian radiation and those seen after major extinction events.

Role of genetic and social factors in modulating the efficiency of division of labour in social insects

Summary : Division of labour is one of the most fascinating aspects of social insects. The efficient allocation of individuals to a multitude of different tasks requires a dynamic adjustment in response to the demands of a changing environment. A considerable number of theoretical models have focussed on identifying the mechanisms allowing colonies to perform efficient task allocation. The large majority of these models are built on the observation that individuals in a colony vary in their propensity (response threshold) to perform different tasks. Since individuals with a low threshold for a given task stimulus are more likely to perform that task than individuals with a high threshold, infra-colony variation in individual thresholds results in colony division of labour. These theoretical models suggest that variation in individual thresholds is affected by the within-colony genetic diversity. However, the models have not considered the genetic architecture underlying the individual response thresholds. This is important because a better understanding of division of labour requires determining how genotypic variation relates to differences in infra-colony response threshold distributions. In this thesis, we investigated the combined influence on task allocation efficiency of both, the within-colony genetic variability (stemming from variation in the number of matings by queens) and the number of genes underlying the response thresholds. We used an agent-based simulator to model a situation where workers in a colony had to perform either a regulatory task (where the amount of a given food item in the colony had to be maintained within predefined bounds) or a foraging task (where the quantity of a second type of food item collected had to be the highest possible). The performance of colonies was a function of workers being able to perform both tasks efficiently. To study the effect of within-colony genetic diversity, we compared the performance of colonies with queens mated with varying number of males. On the other hand, the influence of genetic architecture was investigated by varying the number of loci underlying the response threshold of the foraging and regulatory tasks. Artificial evolution was used to evolve the allelic values underlying the tasks thresholds. The results revealed that multiple matings always translated into higher colony performance, whatever the number of loci encoding the thresholds of the regulatory and foraging tasks. However, the beneficial effect of additional matings was particularly important when the genetic architecture of queens comprised one or few genes for the foraging task's threshold. By contrast, higher number of genes encoding the foraging task reduced colony performance with the detrimental effect being stronger when queens had mated with several males. Finally, the number of genes determining the threshold for the regulatory task only had a minor but incremental effect on colony performance. Overall, our numerical experiments indicate the importance of considering the effects of queen mating frequency, genetic architecture underlying task thresholds and the type of task performed when investigating the factors regulating the efficiency of division of labour in social insects. In this thesis we also investigate the task allocation efficiency of response threshold models and compare them with neural networks. While response threshold models are widely used amongst theoretical biologists interested in division of labour in social insects, our simulation reveals that they perform poorly compared to a neural network model. A major shortcoming of response thresholds is that they fail at one of the most crucial requirement of division of labour, the ability of individuals in a colony to efficiently switch between tasks under varying environmental conditions. Moreover, the intrinsic properties of the threshold models are that they lead to a large proportion of idle workers. Our results highlight these limitations of the response threshold models and provide an adequate substitute. Altogether, the experiments presented in this thesis provide novel contributions to the understanding of how division of labour in social insects is influenced by queen mating frequency and genetic architecture underlying worker task thresholds. Moreover, the thesis also provides a novel model of the mechanisms underlying worker task allocation that maybe more generally applicable than the widely used response threshold models. Resumé : La répartition du travail est l'un des aspects les plus fascinants des insectes vivant en société. Une allocation efficace de la multitude de différentes tâches entre individus demande un ajustement dynamique afin de répondre aux exigences d'un environnement en constant changement. Un nombre considérable de modèles théoriques se sont attachés à identifier les mécanismes permettant aux colonies d'effectuer une allocation efficace des tâches. La grande majorité des ces modèles sont basés sur le constat que les individus d'une même colonie diffèrent dans leur propension (inclination à répondre) à effectuer différentes tâches. Etant donné que les individus possédant un faible seuil de réponse à un stimulus associé à une tâche donnée sont plus disposés à effectuer cette dernière que les individus possédant un seuil élevé, les différences de seuils parmi les individus vivant au sein d'une même colonie mènent à une certaine répartition du travail. Ces modèles théoriques suggèrent que la variation des seuils des individus est affectée par la diversité génétique propre à la colonie. Cependant, ces modèles ne considèrent pas la structure génétique qui est à la base des seuils de réponse individuels. Ceci est très important car une meilleure compréhension de la répartition du travail requière de déterminer de quelle manière les variations génotypiques sont associées aux différentes distributions de seuils de réponse à l'intérieur d'une même colonie. Dans le cadre de cette thèse, nous étudions l'influence combinée de la variabilité génétique d'une colonie (qui prend son origine dans la variation du nombre d'accouplements des reines) avec le nombre de gènes supportant les seuils de réponse, vis-à-vis de la performance de l'allocation des tâches. Nous avons utilisé un simulateur basé sur des agents pour modéliser une situation où les travailleurs d'une colonie devaient accomplir une tâche de régulation (1a quantité d'une nourriture donnée doit être maintenue à l'intérieur d'un certain intervalle) ou une tâche de recherche de nourriture (la quantité d'une certaine nourriture doit être accumulée autant que possible). Dans ce contexte, 'efficacité des colonies tient en partie des travailleurs qui sont capable d'effectuer les deux tâches de manière efficace. Pour étudier l'effet de la diversité génétique d'une colonie, nous comparons l'efficacité des colonies possédant des reines qui s'accouplent avec un nombre variant de mâles. D'autre part, l'influence de la structure génétique a été étudiée en variant le nombre de loci à la base du seuil de réponse des deux tâches de régulation et de recherche de nourriture. Une évolution artificielle a été réalisée pour évoluer les valeurs alléliques qui sont à l'origine de ces seuils de réponse. Les résultats ont révélé que de nombreux accouplements se traduisaient toujours en une plus grande performance de la colonie, quelque soit le nombre de loci encodant les seuils des tâches de régulation et de recherche de nourriture. Cependant, les effets bénéfiques d'accouplements additionnels ont été particulièrement important lorsque la structure génétique des reines comprenait un ou quelques gènes pour le seuil de réponse pour la tâche de recherche de nourriture. D'autre part, un nombre plus élevé de gènes encodant la tâche de recherche de nourriture a diminué la performance de la colonie avec un effet nuisible d'autant plus fort lorsque les reines s'accouplent avec plusieurs mâles. Finalement, le nombre de gènes déterminant le seuil pour la tâche de régulation eu seulement un effet mineur mais incrémental sur la performance de la colonie. Pour conclure, nos expériences numériques révèlent l'importance de considérer les effets associés à la fréquence d'accouplement des reines, à la structure génétique qui est à l'origine des seuils de réponse pour les tâches ainsi qu'au type de tâche effectué au moment d'étudier les facteurs qui régulent l'efficacité de la répartition du travail chez les insectes vivant en communauté. Dans cette thèse, nous étudions l'efficacité de l'allocation des tâches des modèles prenant en compte des seuils de réponses, et les comparons à des réseaux de neurones. Alors que les modèles basés sur des seuils de réponse sont couramment utilisés parmi les biologistes intéressés par la répartition des tâches chez les insectes vivant en société, notre simulation montre qu'ils se révèlent peu efficace comparé à un modèle faisant usage de réseaux de neurones. Un point faible majeur des seuils de réponse est qu'ils échouent sur un point crucial nécessaire à la répartition des tâches, la capacité des individus d'une colonie à commuter efficacement entre des tâches soumises à des conditions environnementales changeantes. De plus, les propriétés intrinsèques des modèles basés sur l'utilisation de seuils conduisent à de larges populations de travailleurs inactifs. Nos résultats mettent en évidence les limites de ces modèles basés sur l'utilisation de seuils et fournissent un substitut adéquat. Ensemble, les expériences présentées dans cette thèse fournissent de nouvelles contributions pour comprendre comment la répartition du travail chez les insectes vivant en société est influencée par la fréquence d'accouplements des reines ainsi que par la structure génétique qui est à l'origine, pour un travailleur, du seuil de réponse pour une tâche. De plus, cette thèse fournit également un nouveau modèle décrivant les mécanismes qui sont à l'origine de l'allocation des tâches entre travailleurs, mécanismes qui peuvent être appliqué de manière plus générale que ceux couramment utilisés et basés sur des seuils de réponse.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

A parallel genetic algorithm for the Steiner Problem in Networks

This paper presents a parallel genetic algorithm to the Steiner Problem in Networks. Several previous papers have proposed the adoption of GAs and others metaheuristics to solve the SPN demonstrating the validity of their approaches. This work differs from them for two main reasons: the dimension and the characteristics of the networks adopted in the experiments and the aim from which it has been originated. The reason that aimed this work was namely to build a comparison term for validating deterministic and computationally inexpensive algorithms which can be used in practical engineering applications, such as the multicast transmission in the Internet. On the other hand, the large dimensions of our sample networks require the adoption of a parallel implementation of the Steiner GA, which is able to deal with such large problem instances.