GALANT: a Cytoscape plugin for visualizing data as functional landscapes projected onto biological networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

Valor prognóstico e preditivo de genes candidatos selecionados após análise de oligoarrays em carcinomas mamários

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Construção, caracterização global e local de redes biológicas

A Biologia Sistêmica visa a compreensão da vida através de modelos integrativos que enfatizem as interações entre os diferentes agentes biológicos. O objetivo é buscar por leis universais, não nas partes componentes dos sistemas mas sim nos padrões de interação dos elementos constituintes. As redes complexas biológicas são uma poderosa abstração matemática que permite a representação de grandes volumes de dados e a posterior formulação de hipóteses biológicas. Nesta tese apresentamos as redes biológicas integradas que incluem interações oriundas do metabolismo, interação física de proteínas e regulação. Discutimos sua construção e ferramentas para sua análise global e local. Apresentamos também resultados do uso de ferramentas de aprendizado de máquina que nos permitem compreender a relação entre propriedades topológicas e a essencialidade gênica e a previsão de genes mórbidos e alvos para drogas em humanos

Caracterização topológica dos motivos das redes biológicas integradas da Escherichia coli e da Saccharomyces cerevisiae

The reducionism method has helped in the clari cation of functioning of many biological process. However, such process are extremely complex and have emergent properties that can not be explained or even predicted by reducionism methods. To overcome these limits, researchers have been used a set of methods known as systems biology, a new area of biology aiming to understand the interactions between the multiple components of biological processes. These interactions can be represented by a mathematical object called graph or network, where the interacting elements are represented by a vertex and the interactions by edges that connect a pair of vertexes. Into graphs it is possible to nd subgraphs, occurring in complex networks at numbers that are signi cantly higher than those in randomized networks, they are de ned as motifs. As motifs in biological networks may represent the structural units of biological processess, their detection is important. Therefore, the aim of this present work was detect, count and classify motifs present in biological integrated networks of bacteria Escherichia coli and yeast Saccharomyces cere- visiae. For this purpose, we implemented codes in MathematicaR and Python environments for detecting, counting and classifying motifs in these networks. The composition and types of motifs detected in these integrated networks indicate that such networks are organized in three main bridged modules composed by motifs in which edges are all the same type. The connecting bridges are composed by motifs in which the types of edges are diferent

Determinação de comunidades em redes biológicas integradas

To understand how biological phenomena emerge, the nonlinear interactions among the components envolved in these and the correspondent connected elements, like genes, proteins, etc., can be represented by a mathematical object called graph or network, where interacting elements are represented by edges connecting pairs of nodes. The analysis of various graph-related properties of biological networks has revealed many clues about biological processes. Among these properties, the community structure, i.e. groups of nodes densely connected among themselves, but sparsely connected to other groups, are important for identifying separable functional modules within biological systems for the comprehension of the high-level organization of the cell. Communities' detection can be performed by many algorithms, but most of them are based on the density of interactions among nodes of the same community. So far, the detection and analysis of network communities in biological networks have only been pursued for networks composed by one type of interaction (e.g. protein-protein interactions or metabolic interactions). Since a real biological network is simultaneously composed by protein-protein, metabolic and transcriptional regulatory interactions, it would be interesting to investigate how communities are organized in this type of network. For this purpose, we detected the communities in an integrated biological network of the Escherichia coli and Saccharomyces cerevisiae by using the Clique Percolation Method and we veri ed, by calculating the frequency of each type of interaction and its related entropy, if components of communities... (Complete abstract click electronic access below)

Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Invasive Africanized honeybees change the structure of native pollination networks in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Biodiversity, Species Interactions and Ecological Networks in a Fragmented World

Biodiversity is organised into complex ecological networks of interacting species in local ecosystems, but our knowledge about the effects of habitat fragmentation on such systems remains limited. We consider the effects of this key driver of both local and global change on both mutualistic and antagonistic systems at different levels of biological organisation and spatiotemporal scales.There is a complex interplay of patterns and processes related to the variation and influence of spatial, temporal and biotic drivers in ecological networks. Species traits (e.g. body size, dispersal ability) play an important role in determining how networks respond to fragment size and isolation, edge shape and permeability, and the quality of the surrounding landscape matrix. Furthermore, the perception of spatial scale (e.g. environmental grain) and temporal effects (time lags, extinction debts) can differ markedly among species, network modules and trophic levels, highlighting the need to develop a more integrated perspective that considers not just nodes, but the structural role and strength of species interactions (e.g. as hubs, spatial couplers and determinants of connectance, nestedness and modularity) in response to habitat fragmentation.Many challenges remain for improving our understanding: the likely importance of specialisation, functional redundancy and trait matching has been largely overlooked. The potentially critical effects of apex consumers, abundant species and supergeneralists on network changes and evolutionary dynamics also need to be addressed in future research. Ultimately, spatial and ecological networks need to be combined to explore the effects of dispersal, colonisation, extinction and habitat fragmentation on network structure and coevolutionary dynamics. Finally, we need to embed network approaches more explicitly within applied ecology in general, because they offer great potential for improving on the current species-based or habitat-centric approaches to our management and conservation of biodiversity in the face of environmental change.

Artificial immune systems applied to optimal capacitor placement in radial distribution networks

The capacitor placement problem for radial distribution networks aims to determine capacitor types, sizes, locations and control scheme. This is a combinatorial problem that can be formulated as a mixed integer nonlinear program. The paper presents an algorithm inspired in artificial immune systems and developed for this specific problem. A good performance was obtained through experimental tests applied to known systems. © 2006 IEEE.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Reconstruction of molecular networks involved in citokine-induced myotubes atrophy integrating microRNA and mRNA expression

Pós-graduação em Ciências Biológicas (Genética) - IBB

Decay of interspecific avian flock networks along a disturbance gradient in Amazonia

Our understanding of how anthropogenic habitat change shapes species interactions is in its infancy. This is in large part because analytical approaches such as network theory have only recently been applied to characterize complex community dynamics. Network models are a powerful tool for quantifying how ecological interactions are affected by habitat modification because they provide metrics that quantify community structure and function. Here, we examine how large-scale habitat alteration has affected ecological interactions among mixed-species flocking birds in Amazonian rainforest. These flocks provide a model system for investigating how habitat heterogeneity influences non-trophic interactions and the subsequent social structure of forest-dependent mixed-species bird flocks. We analyse 21 flock interaction networks throughout a mosaic of primary forest, fragments of varying sizes and secondary forest (SF) at the Biological Dynamics of Forest Fragments Project in central Amazonian Brazil. Habitat type had a strong effect on network structure at the levels of both species and flock. Frequency of associations among species, as summarized by weighted degree, declined with increasing levels of forest fragmentation and SF. At the flock level, clustering coefficients and overall attendance positively correlated with mean vegetation height, indicating a strong effect of habitat structure on flock cohesion and stability. Prior research has shown that trophic interactions are often resilient to large-scale changes in habitat structure because species are ecologically redundant. By contrast, our results suggest that behavioural interactions and the structure of non-trophic networks are highly sensitive to environmental change. Thus, a more nuanced, system-by-system approach may be needed when thinking about the resiliency of ecological networks.

A method using artificial neural networks to morphologically assess mouse blastocyst quality

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)