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Made available in DSpace on 2018-12-11T17:03:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-06-01 Alpha solenoid proteins play a key role in regulating the classical nuclear import pathway, recognizing a target protein and transporting it into the nucleus. Importin-α (Impα) is the solenoid responsible for cargo protein recognition, and it has been extensively studied by X-ray crystallography to understand the binding specificity. To comprehend the main motions of Impα and to extend the information about the critical interactions during carrier-cargo recognition, we surveyed different conformational states based on molecular dynamics (MD) and normal mode (NM) analyses. Our model of study was a crystallographic structure of Impα complexed with the classical nuclear localization sequence (cNLS) from nucleoplasmin (Npl), which was submitted to multiple 100 ns of MD simulations. Representative conformations were selected for calculating the 87 lowest frequencies NMs of vibration, and a displacement approach was applied along each NM. Based on geometric criteria, using the radius of curvature and inter-repeat angles as the reference metrics, the main motions of Impα were described. Moreover, we determined the salt bridges, hydrogen bonds and hydrophobic interactions in the Impα-NplNLS interface. Our results show the bending and twisting motions participating in the recognition of nuclear proteins, allowing the accommodation and adjustment of a classical bipartite NLS sequence. The essential contacts for the nuclear import were also described and were mostly in agreement with previous studies, suggesting that the residues in the cNLS linker region establish important contacts with Impα adjusting the cNLS backbone. The MD simulations combined with NM analysis can be applied to the Impα-NLS system to help understand interactions between Impα and cNLSs and the analysis of non-classic NLSs. Laborat�rio de Bioinform�tica e Biof�sica Computacional Departamento de F�sica e Biof�sica Instituto de Bioci�ncias de Botucatu UNESP - Universidade Estadual Paulista Instituto de Biotecnologia (IBTEC) UNESP - Universidade Estadual Paulista Laborat�rio de Biologia Computacional e Bioinform�tica Centro de Ci�ncias Naturais e Humanas UFABC - Universidade Federal Do ABC Laborat�rio de Bioinform�tica e Biof�sica Computacional Departamento de F�sica e Biof�sica Instituto de Bioci�ncias de Botucatu UNESP - Universidade Estadual Paulista Instituto de Biotecnologia (IBTEC) UNESP - Universidade Estadual Paulista

Figure S1. The distributions of node strength for essential and nonessential protein. Figure S2. The distributions of co-expression weights for IBEPs and Non-IBEPs. Figure S3. Performance comparison of five centrality measures (BC, CC, DC, EC, and SC) on two yeast PINs (PIN24K and PIN76K) using uniform thresholding strategy ((a)-(h)). Figure S4. Relationship between the number of nonzero-degree nodes (or proteins) in PINs and the thresholds for generating the corresponding PINs using absolute thresholding strategy ((a)-(b)). Figure S5. Relationship between the number of nonzero-degree nodes (or proteins) in PINs and the thresholds for generating the PINs using uniform thresholding strategy ((a)-(b)). Figure S6. Performance comparison of five centrality measures (BC, CC, DC, EC, and SC) with their corresponding ensemble methods (absolute thresholding strategy) with different sample sizes or weights on two yeast PINs. Figure S7. Comparison of the number of essential proteins detected by each ensemble method using uniform thresholding strategy with different voting weights on two yeast PINs. Table S1. The number of common predicted proteins (overlap) among the top 100 proteins ranked by PCC-weighted methods. Table S2. The number of common predicted proteins (overlap) among the top 100 proteins ranked by single PCC-threshold methods (thrâ =â 0.75). Table S3. Correlation between centrality measures based on their top 100 ranked proteins. Table S4. Correlation between ensemble methods based on their top 100 ranked proteins. (PDF 1294 kb)

O presente trabalho apresenta, através da utilização de Algoritmos Genéticos (AGs), um método para realizar projeto de transformadores. Ao contrário do método convencional de projetos, as etapas de otimização das características de viabilidade física e financeira são efetuadas simultaneamente. Para realizar o projeto do transformador foi empregado um modelo analítico baseado nas relações estabelecidas pela equação geral das máquinas elétricas. A otimização é obtida pelo estabelecimento de relações de equilíbrio no custo do material das partes constituídas de cobre e ferro. O aumento de temperatura decorrente da perda no núcleo de ferro e nos enrolamentos, bem como o rendimento energético e a possibilidade de execução são tratados como restrições. O emprego do Algoritmo Genético obteve resultados melhores em termos de custo e desempenho que os métodos convencionais, obtendose redução de custo de até 10% quando comparado à sistemática de projeto comumente empregada.

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.

Made available in DSpace on 2018-12-11T16:59:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-01-01 The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although, the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs, and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges. Department of Electrical and Computer Engineering Iowa State University Department of Physics and Biophysics Institute of Biosciences of Botucatu UNESP - São Paulo State University Department of Physics and Biophysics Institute of Biosciences of Botucatu UNESP - São Paulo State University

Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.

In this work we propose an alternative DNA sequence analysis tool based on graph theoretical concepts. The methodology investigates the path topology of an organism genome through a triplet network. In this network, triplets in DNA sequence are vertices and two vertices are connected if they occur juxtaposed on the genome. We characterize this network topology by measuring the clustering coefficient. We test our methodology against two main bias: the guanine–cytosine (GC) content and 3-bp (base pairs) periodicity of DNA sequence. We perform the test constructing random networks with variable GC content and imposed 3-bp periodicity. A test group of some organisms is constructed and we investigate the methodology in the light of the constructed random networks. We conclude that the clustering coefficient is a valuable tool since it gives information that is not trivially contained in 3-bp periodicity neither in the variable GC content.

The proteome, a set of proteins expressed in an organism, is organized in an intricate web called the protein–protein interaction (PPI) network. In this article we propose a topological parameter called damage, that measures the consequences of the deletion of a protein from the network. We investigate different PPI data sets using this parameter and also traditional ones: the connectivity and the clusterization coefficient. We show that damage histogram obeys a power law in all data sets, and that proteins that cause a large damage are, with high probability, essential. For data sets that consider physical interactions the PPI network is a hierarchical scale-free network, while for data sets that consider functional interactions the PPI network is a scale-free graph.

In this work we apply a bioinformatics approach to determine the most important enzymes of the metabolic network of Eucalyptus to determine the coverage of the genome in the FORESTS library. We conclude that the library does not cover completely the metabolism of the organism. However, some important pathways could be analyzed, especially the lignin synthesis. We found that four of the most important enzymes predicted are involved in this pathway.

Complex systems may show strongly non-exponential relaxation, the origins of these behavior seem to be related to the fractal structure of the phase space. In this article, we consider the diffusion on a dilute hypercube, this stochastic process is a coarse-grained model for the time evolution of a glassy system, where both the dynamics and the structure of the phase space was considered as simple as possible. We characterize this process using two quantities: a memory function and the Tsallis entropy. The first one is a measure of mixing while the second is related to non-extensivity. We characterize quantitatively their relationship. Finally, we discuss the implications for other glassy and complex systems.

Understanding the architecture of physiological functions from annotated genome sequences is a major task for postgenomic biology. From the annotated genome sequence of the microbe Escherichia coli, we propose a general quantitative definition of enzyme importance in a metabolic network. Using a graph analysis of its metabolism, we relate the extent of the topological damage generated in the metabolic network by the deletion of an enzyme to the experimentally determined viability of the organism in the absence of that enzyme. We show that the network is robust and that the extent of the damage relates to enzyme importance. We predict that a large fraction (91%) of enzymes causes little damage when removed, while a small group (9%) can cause serious damage. Experimental results confirm that this group contains the majority of essential enzymes. The results may reveal a universal property of metabolic networks.

We investigate the production of Tsallis entropy for the probability distribution of random walkers on a hypercube with a fraction p of allowed sites. We consider two regimes, p=1 and from p= 1 2 until the percolation limit pc. In the first regime, we show that the entropy rate can be obtained from the Boltzmann–Gibbs–Shannon entropy time evolution. On the second regime, the same numerical experiments indicate a trivial limit for this quantity, while by measuring the production of Tsallis entropy for different q values there exists a particular value for the entropic index q that adequately describe the relaxation. These results are compared to similar numerical experiments performed with non-linear maps and indicate an analogy between the phase space of both systems.

We present a unified model for the growth of a population of cells. We propose that sigmoidal growth in cellular systems is a self-organised process due to long-range interactions among the cells. The interaction is mediated through diffusive substances produced by them. The model considers a competition between cell drive to replicate and inhibitory interactions that are modeled by a power law of the distance between the cells. The different classes of solutions (Logistic, Richards-like, Gompertz, and Exponential) are determined by a relation between the interaction length and the fractal dimension of the cellular structure.

Made available in DSpace on 2016-07-07T12:35:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2014. Added 1 bitstream(s) on 2016-07-07T12:44:33Z : No. of bitstreams: 1 ISSN2318-5279-2014-02-01-15-18.pdf: 386817 bytes, checksum: 356a8c00342ac131aab7bb1f4278985a (MD5) In this study, Matching Pursuit (MP) procedure is applied to the detection and analysis of EEG sleep spindles in patients evaluated for suspected OSAS. Elements having the frequency of EEG sleep spindles are selected from different dictionary sizes, with and without a frequency modulation function (chirp) for signal description. This procedure was done with high computational cost in order to find best parameters for real EEG data description. At the end we used the atom parameters as input for a decision tree-based classifier, making possible to obtain a classification according to apnea-hypopnea index group and allowing to see how atom parameters such as frequency and amplitude are affected by the presence of sleep apnea. Universidade de Caxias do Sul (UCS), Caxias do Sul, RS, Brasil Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Instituto de Biociências (IBB), Departamento de Física e Biofísica, Botucatu, SP, Brasil Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brasil Universidade Federal de Sergipe (UFS), São Cristóvão, SE, Brasil Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Instituto de Biociências (IBB), Departamento de Física e Biofísica, Botucatu, SP, Brasil

We investigate the adaptation of random boolean networks that are a model for regulatory gene networks. The model considers a general genetic algorithm and a fitness function that takes into account the full network dynamical behavior. We propose a mathematical function to quantify the complexity catastrophe. We also find that the latter occurs when the task complexity increases, i.e., using networks with longer periods. Finally, we discuss a scenario that describes the adaptation on the proposed fitness landscape.

We present a simple model to study micellization of amphiphiles condensed on a rodlike polyion. Although the mean field theory leads to a first order micellization transition for sufficiently strong hydrophobic interactions, the simulations show that no such thermodynamic phase transition exists. Instead, the correlations between the condensed amphiphiles can result in a structure formation very similar to micelles., 8 pages, 7 figures

We study random walks on the dilute hypercube using an exact enumeration Master equation technique, which is much more efficient than Monte Carlo methods for this problem. For each dilution $p$ the form of the relaxation of the memory function $q(t)$ can be accurately parametrized by a stretched exponential $q(t)=\exp(-(t/\tau)^\beta)$ over several orders of magnitude in $q(t)$. As the critical dilution for percolation $p_c$ is approached, the time constant $\tau(p)$ tends to diverge and the stretching exponent $\beta(p)$ drops towards 1/3. As the same pattern of relaxation is observed in wide class of glass formers, the fractal like morphology of the giant cluster in the dilute hypercube is a good representation of the coarse grained phase space in these systems. For these glass formers the glass transition can be pictured as a percolation transition in phase space., Comment: 5 pages and 4 figures

Obstructive Sleep Apnea (OSA) is a pathological condition characterized by repeated episodes of interruption in the upper airways ventilation (apnea and hipoapnea) during [1]. This pathology affects 4% of men and 2% of women of working age [1]. In electroencephalographic studies, sleep spindle is an element strongly associated to OSA and certainly the most studied graphoelement in the sleep study. Sleep spindles are oscillations defined by the appearance and disappearance of spindle waves between 12-14Hz at sequential intervals of 0.5-2 seconds. The purpose of this study was to analyze the percentage of correlations between different channels in electroencephalographic examinations in normal and (mild) OSA subjects.

We present simulations for random walks on a giant cluster in a randomly occupied hypercube. A new algorithm does away with the need for large memory requirements. The data can be fitted to high precision using stretched exponentials; confirming earlier conclusions, the results underline the strong resemblance between the decay behaviour in this mathematical model and that observed numerically or experimentally in glassy systems.

We demonstrate numerically that for Ising spins on square lattices with ferromagnetic second neighbour interactions and random near neighbour interactions, two dimensional Ising spin glass order with a non-zero freezing temperature can occur. We compare some of the physical properties of these spin glasses with those of standard spin glasses in higher dimensions., Comment: 9 page latex file and 9 ps figures. To appear in Phys. Rev. Lett

We study the generalization capability of an extreme and asymmetrically diluted version of the Hopfield model through analytical and simulation techniques. Generalization is the ability of the system for grouping a given set of correlated patterns (the examples) in distinct classes (concepts), in such a way that each concept represents the common features of a set of examples and are attractors of the network dynamics. The dynamics of the system can be solved exactly and the generalization error for the long-time regime can be evaluated. As occur for the storage capacity, here it is shown that dilution improves the performance of the network as a categorization device when confronted with the fully connected Hopfield model.

A dynamical system of coupled neurons with variable thresholds is solved analytically and compared with numerical simulations. The system behaviour is extremely rich presenting intermittence, chaos, crises etc. depending on the parameters.

The asymmetric diluted Hopfield model is studied through numerical simulations and reliable results are obtained even when the network sizes are not exponentially large. The mean final overlap, the mean convergence time and a quantity that gives information on the relative importance of spurious states are measured as a function of the load capacity alpha .

Made available in DSpace on 2013-08-28T14:09:29Z (GMT). No. of bitstreams: 1 WOS000298666200001.pdf: 402150 bytes, checksum: 3723ce8567931f389f45074a49611911 (MD5) Made available in DSpace on 2013-09-30T18:37:20Z (GMT). No. of bitstreams: 2 WOS000298666200001.pdf: 402150 bytes, checksum: 3723ce8567931f389f45074a49611911 (MD5) WOS000298666200001.pdf.txt: 56655 bytes, checksum: 10d0e581654ecca834117e332f3f69ca (MD5) Previous issue date: 2011-12-20 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:49:40Z No. of bitstreams: 2 WOS000298666200001.pdf: 402150 bytes, checksum: 3723ce8567931f389f45074a49611911 (MD5) WOS000298666200001.pdf.txt: 56655 bytes, checksum: 10d0e581654ecca834117e332f3f69ca (MD5) Made available in DSpace on 2014-05-20T13:49:40Z (GMT). No. of bitstreams: 2 WOS000298666200001.pdf: 402150 bytes, checksum: 3723ce8567931f389f45074a49611911 (MD5) WOS000298666200001.pdf.txt: 56655 bytes, checksum: 10d0e581654ecca834117e332f3f69ca (MD5) Previous issue date: 2011-12-20 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Correlated mutation analysis has a long history of interesting applications, mostly in the detection of contact pairs in protein structures. Based on previous observations that, if properly assessed, amino acid correlation data can also provide insights about functional sub-classes in a protein family, we provide a complete framework devoted to this purpose. An amino acid specific correlation measure is proposed, which can be used to build networks summarizing all correlation and anti-correlation patterns in a protein family. These networks can be submitted to community structure detection algorithms, resulting in subsets of correlated amino acids which can be further assessed by specific parameters and procedures that provide insight into the relationship between different communities, the individual importance of community members and the adherence of a given amino acid sequence to a given community. By applying this framework to three protein families with contrasting characteristics (the Fe/Mn-superoxide dismutases, the peroxidase-catalase family and the C-type lysozyme/alpha-lactalbumin family), we show how our method and the proposed parameters and procedures are related to biological characteristics observed in these protein families, highlighting their potential use in protein characterization and gene annotation. Univ Fed Minas Gerais, Inst Ciencias Biol, Dept Bioquim & Imunol, Belo Horizonte, MG, Brazil Univ Estadual Paulista, Dept Fis & Biofis, Botucatu, SP, Brazil Univ São Paulo, Inst Fis Sao Carlos, Dept Fis & Informat, Sao Carlos, SP, Brazil Univ Estadual Paulista, Dept Fis & Biofis, Botucatu, SP, Brazil FAPESP: 08/58734-1 FAPESP: 98/14138-2

Diffusion on a diluted hypercube has been proposed as a model for glassy relaxation and is an example of the more general class of stochastic processes on graphs. In this article we determine numerically through large scale simulations the eigenvalue spectra for this stochastic process and calculate explicitly the time evolution for the autocorrelation function and for the return probability, all at criticality, with hypercube dimensions $N$ up to N=28. We show that at long times both relaxation functions can be described by stretched exponentials with exponent 1/3 and a characteristic relaxation time which grows exponentially with dimension $N$. The numerical eigenvalue spectra are consistent with analytic predictions for a generic sparse network model., 16 pages, 7 figures

We propose a two-dimensional particle-cluster aggregation model which considers explicitly linear and angular momentum conservation. Each aggregate is grown from a seed of given mass and given initial angular velocity, to which particles of unitary mass and ballistic trajectories are added. The fractal dimensions of the resulting aggregates are calculated and Fourier transform analysis is performed. Very different features are found, as initial conditions are varied. Particularly, spiral structures are found for a seed of high mass and high initial angular velocity

Nep1-like proteins (NLPs) are a novel family of microbial elicitors of plant necrosis that induce a hypersensitive-like response in dicot plants. The spatial structure and role of these proteins are yet unknown. In a paper published in BMC Plant Biology (2008; 8:50) we have proposed that the core region of Nep1-like proteins (NLPs) belong to the Cupin superfamily. Based on what is known about the Cupin superfamily, in this addendum to the paper we discuss how NLPs could form oligomers.

Made available in DSpace on 2013-08-28T14:11:24Z (GMT). No. of bitstreams: 1 WOS000256220600001.pdf: 1138898 bytes, checksum: 2474852121cf36ec39ac8141f323c3b2 (MD5) Made available in DSpace on 2013-09-30T18:37:16Z (GMT). No. of bitstreams: 2 WOS000256220600001.pdf: 1138898 bytes, checksum: 2474852121cf36ec39ac8141f323c3b2 (MD5) WOS000256220600001.pdf.txt: 64277 bytes, checksum: d1aa3622048f8dd9e63c261a1744a1dd (MD5) Previous issue date: 2008-04-30 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:49:38Z No. of bitstreams: 2 WOS000256220600001.pdf: 1138898 bytes, checksum: 2474852121cf36ec39ac8141f323c3b2 (MD5) WOS000256220600001.pdf.txt: 64277 bytes, checksum: d1aa3622048f8dd9e63c261a1744a1dd (MD5) Made available in DSpace on 2014-05-20T13:49:38Z (GMT). No. of bitstreams: 2 WOS000256220600001.pdf: 1138898 bytes, checksum: 2474852121cf36ec39ac8141f323c3b2 (MD5) WOS000256220600001.pdf.txt: 64277 bytes, checksum: d1aa3622048f8dd9e63c261a1744a1dd (MD5) Previous issue date: 2008-04-30 Background: NEP1-like proteins (NLPs) are a novel family of microbial elicitors of plant necrosis. Some NLPs induce a hypersensitive-like response in dicot plants though the basis for this response remains unclear. In addition, the spatial structure and the role of these highly conserved proteins are not known.Results: We predict a 3d-structure for the beta-rich section of the NLPs based on alignments, prediction tools and molecular dynamics. We calculated a consensus sequence from 42 NLPs proteins, predicted its secondary structure and obtained a high quality alignment of this structure and conserved residues with the two Cupin superfamily motifs. The conserved sequence GHRHDWE and several common residues, especially some conserved histidines, in NLPs match closely the two cupin motifs. Besides other common residues shared by dicot Auxin-Binding Proteins (ABPs) and NLPs, an additional conserved histidine found in all dicot ABPs was also found in all NLPs at the same position.Conclusion: We propose that the necrosis inducing protein class belongs to the Cupin superfamily. Based on the 3d-structure, we are proposing some possible functions for the NLPs. UNESP, Dept Fis & Biofis, Botucatu, SP, Brazil Unisinos, Programa Posgrad Comp Aplicada, Sao Leopoldo, Brazil UCS, Inst Biotecnol, Caxias do Sul, Brazil IB UNICAMP, Dept Genet & Evolucao, Campinas, Brazil UFPampa UFSM, Ctr Ciencias Rurais, Sao Gabriel, Brazil UNESP, Dept Fis & Biofis, Botucatu, SP, Brazil

Made available in DSpace on 2015-10-21T13:11:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-06-24. Added 1 bitstream(s) on 2015-10-22T09:52:22Z : No. of bitstreams: 1 WOS000356932500122.pdf: 1169921 bytes, checksum: 2104756b5592801f5b3376f68a76400e (MD5) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Mitochondrial inner membrane uncoupling proteins (UCP) dissipate the proton electrochemical gradient established by the respiratory chain, thus affecting the yield of ATP synthesis. UCP overexpression in plants has been correlated with oxidative stress tolerance, improved photosynthetic efficiency and increased mitochondrial biogenesis. This study reports the main transcriptomic responses associated with the overexpression of an UCP (AtUCP1) in tobacco seedlings. Compared to wild-type (WT), AtUCP1 transgenic seedlings showed unaltered ATP levels and higher accumulation of serine. By using RNA-sequencing, a total of 816 differentially expressed genes between the investigated overexpressor lines and the untransformedWT control were identified. Among them, 239 were up-regulated and 577 were down-regulated. As a general response to AtUCP1 overexpression, noticeable changes in the expression of genes involved in energy metabolism and redox homeostasis were detected. A substantial set of differentially expressed genes code for products targeted to the chloroplast and mainly involved in photosynthesis. The overall results demonstrate that the alterations in mitochondrial function provoked by AtUCP1 overexpression require important transcriptomic adjustments to maintain cell homeostasis. Moreover, the occurrence of an important cross-talk between chloroplast and mitochondria, which culminates in the transcriptional regulation of several genes involved in different pathways, was evidenced. UNESP, Inst Biociencias, Dept Genet, Botucatu, SP, Brazil UNESP, Inst Biociencias, Dept Fis &Biofis, Botucatu, SP, Brazil Univ Sao Paulo, Dept Genet, Escola Super Agr Luiz de Queiroz, Piracicaba, SP, Brazil UNESP, Inst Biociencias, Dept Parasitol, Botucatu, SP, Brazil UNESP, Instituto de Biociências, Departamento de Genética, Botucatu, SP, Brazil UNESP, Instituto de Biociências, Departamento de Física e Biofísica, Botucatu, SP, Brazil UNESP, Instituto de Biociências, Departamento de Parasitologia, Botucatu, SP, Brazil FAPESP: 2013/12947-2

The description of the complex molecular network responsible for cell behavior requires new tools to integrate large quantities of experimental data in the design of biological information systems. These tools could be used in the characterization of these networks and in the formulation of relevant biological hypotheses. The building of an ontology is a crucial step because it integrates in a coherent framework the concepts necessary to accomplish such a task. We present MONET (molecular network), an extensible ontology and an architecture designed to facilitate the integration of data originating from different public databases in a single- and well-documented relational database, that is compatible with MONET formal definition. We also present an example of an application that can easily be implemented using these tools.

We present the MOlecular NETwork (MONET) ontology as a model to integrate data from different networks that govern cell function. To achieve this, different existing ontologies were analyzed and an integrated ontology was built in a way to make it possible to share and reuse knowledge, support interoperability between systems, and also allow the formulation of hypotheses through inferences. By studying the cell as an entity of a myriad of elements and networks of interactions, we aim to offer a means to understand the large-scale characteristics responsible for the behavior of the cell and to enable new biological insights.

Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters, where are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying promoters is a complex task. In silico approaches are used to recognize theses regions. Nevertheless, they confront the absence of a large set of promoters to identify conserved patterns among the species. Hence, a methodology able to predict them on any genome is a challenge. This work proposes a methodology based on Hidden Markov Models (HMMs), Decision Threshold Estimation and Discrimination Analysis. For three investigated prokaryotic species, the mainly results are: a reduction in 44.96% of recognition error rate compared with previous works on Escherichia coli, an accuracy of 95% on recognition and 78% on prediction for Bacillus subtilis. However, it was found a large number of false positives on Helicobacter pylori.

Summary: Network-level visualization of functional data is a key aspect of both analysis and understanding of biological systems. In a continuing effort to create clear and integrated visualizations that facilitate the gathering of novel biological insights despite the overwhelming complexity of data, we present here the GrAph LANdscape VisualizaTion (GALANT), a Cytoscape plugin that builds functional landscapes onto biological networks. By using GALANT, it is possible to project any type of numerical data onto a network to create a smoothed data map resembling the network layout. As a Cytoscape plugin, GALANT is further improved by the functionalities of Cytoscape, the popular bioinformatics package for biological network visualization and data integration. Availability: http://www.lbbc.ibb.unesp.br/galant. Contact: esther@ibb.unesp.br Supplementary Information: Supplementary data are available at Bioinformatics online.

In this work we apply a bioinformatics approach to determine the most important enzymes of the metabolic network of mycoplasmas. The genomes of several mycoplasmas shared predicted important enzymes. Our method allows us to determine both enzymes that are isolated from the metabolic network of the organism and those that are redundant. We also compare the similarities of the mycoplasmas metabolic networks with the phylogenetic relationships predicted from their 16s rRNA sequences.

In this work we propose a quantitative definition of enzyme importance in a metabolic network. Using a graph analysis, we investigate the metabolism of the Escherichia coli to determine a relation between the damage generated on the metabolic network by the deletion of an enzyme and the experimentally determined viability of the organism in the absence of the enzyme. We predict that a large fraction (91%) of enzymes cause a small damage and have a low probability of lethality according to experimental results, while a small fraction of them (9%) cause a high damage and have a high probability of lethality. We obtain a quantitative correlation between damage and its probability of lethality. Our results may be a universal property of the metabolic network of other organisms.

We show using extensive simulation results and physical arguments that an Ising system on a two dimensional square lattice, having interactions of random sign between first neighbors and ferromagnetic interactions between second neighbors, presents a phase transition at a non-zero temperature., 10 pages, Latex and 10 figures on eps. Submitted to J. Phys. A

Numerical simulations on Ising Spin Glasses show that spin glass transitions do not obey the usual universality rules which hold at canonical second order transitions. On the other hand the dynamics at the approach to the transition appear to take up a universal form for all spin glasses. The implications for the fundamental physics of transitions in complex systems are addressed., 4 pages (Latex) with 3 figures (postscript), accepted for publication in Physica A

Made available in DSpace on 2014-12-03T13:10:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-10-25Bitstream added on 2014-12-03T13:22:57Z : No. of bitstreams: 1 WOS000326155400041.pdf: 1362323 bytes, checksum: 15a9fd413757e700b9377edcfce7622c (MD5) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype. Univ Estadual Paulista, Dept Phys & Biophys, Botucatu Biosci Inst, Sao Paulo, Brazil Univ Estadual Paulista, Dept Phys & Biophys, Botucatu Biosci Inst, Sao Paulo, Brazil FAPESP: 10/20684-3 FAPESP: 12/13450-1 FAPESP: 12/00741-8 FAPESP: 13/02018-4

Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-27T11:29:33Z No. of bitstreams: 0Bitstream added on 2014-05-27T14:37:35Z : No. of bitstreams: 1 2-s2.0-84878583033.pdf: 1869522 bytes, checksum: 41242063707c215dd5911d7f59f9fa67 (MD5) Made available in DSpace on 2014-05-27T11:29:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-05-31 Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al. Department of Morphology Universidade Estadual Paulista (UNESP), Botucatu, Sao Paulo Department of Physics and Biophysics Universidade Estadual Paulista (UNESP), Botucatu, Sao Paulo Department of Morphology Universidade Estadual Paulista (UNESP), Botucatu, Sao Paulo Department of Physics and Biophysics Universidade Estadual Paulista (UNESP), Botucatu, Sao Paulo

Made available in DSpace on 2013-08-28T14:09:13Z (GMT). No. of bitstreams: 1 WOS000309928200001.pdf: 2298410 bytes, checksum: 38203979f50665f33f32c909723f1482 (MD5) Made available in DSpace on 2013-09-30T18:37:21Z (GMT). No. of bitstreams: 2 WOS000309928200001.pdf: 2298410 bytes, checksum: 38203979f50665f33f32c909723f1482 (MD5) WOS000309928200001.pdf.txt: 31228 bytes, checksum: 2b21e508ad7d91931af43a84448a03d4 (MD5) Previous issue date: 2012-08-17 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:49:41Z No. of bitstreams: 2 WOS000309928200001.pdf: 2298410 bytes, checksum: 38203979f50665f33f32c909723f1482 (MD5) WOS000309928200001.pdf.txt: 31228 bytes, checksum: 2b21e508ad7d91931af43a84448a03d4 (MD5) Made available in DSpace on 2014-05-20T13:49:41Z (GMT). No. of bitstreams: 2 WOS000309928200001.pdf: 2298410 bytes, checksum: 38203979f50665f33f32c909723f1482 (MD5) WOS000309928200001.pdf.txt: 31228 bytes, checksum: 2b21e508ad7d91931af43a84448a03d4 (MD5) Previous issue date: 2012-08-17 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Background: The modeling of interactions among transcription factors (TFs) and their respective target genes (TGs) into transcriptional regulatory networks is important for the complete understanding of regulation of biological processes. In the case of experimentally verified human TF-TG interactions, there is no database at present that explicitly provides such information even though many databases containing human TF-TG interaction data have been available. In an effort to provide researchers with a repository of experimentally verified human TF-TG interactions from which such interactions can be directly extracted, we present here the Human Transcriptional Regulation Interactions database (HTRIdb).Description: The HTRIdb is an open-access database that can be searched via a user-friendly web interface and the retrieved TF-TG interactions data and the associated protein-protein interactions can be downloaded or interactively visualized as a network through the web version of the popular Cytoscape visualization tool, the Cytoscape Web. Moreover, users can improve the database quality by uploading their own interactions and indicating inconsistencies in the data. So far, HTRIdb has been populated with 284 TFs that regulate 18302 genes, totaling 51871 TF-TG interactions. HTRIdb is freely available at http://www.lbbc.ibb.unesp.br/htri.Conclusions: HTRIdb is a powerful user-friendly tool from which human experimentally validated TF-TG interactions can be easily extracted and used to construct transcriptional regulation interaction networks enabling researchers to decipher the regulation of biological processes. Univ Estadual Paulista, UNESP, Inst Biociencias Botucatu, Dept Fis & Biofis, BR-18618970 São Paulo, Brazil Univ Estadual Paulista, UNESP, Inst Biociencias Botucatu, Dept Fis & Biofis, BR-18618970 São Paulo, Brazil FAPESP: 09/10382-2 FAPESP: 10/20684-3

Universidade Federal do Rio Grande do Sul (UFRGS), Sleep Lab, Hosp Clin Porto Alegre, Porto Alegre, RS, Brazil

The dynamics of an extremely diluted neural network with high order synapses acting as corrections to the Hopfield model is investigated. As in the fully connected case, the high order terms may strongly improve the storage capacity of the system. The dynamics displays a very rich behavior, and in particular a new chaotic phase emerges depending on the weight of the high order connections $\epsilon$, the noise level $T$ and the network load defined as the rate between the number of stored patterns and the mean connectivity per neuron $\alpha =P/C$., Comment: 16 pages, LaTeX (11 figures upon request), IFUFRGS-JJA-9303

Unesp Univ Estadual Paulista, Inst Biociencias Botucatu, Dept Phys & Biophys, Botucatu, SP, Brazil

Made available in DSpace on 2013-08-28T14:13:41Z (GMT). No. of bitstreams: 1 WOS000311166100001.pdf: 735940 bytes, checksum: 341769f3d1c3b6eb175c4a024b1e4403 (MD5) Made available in DSpace on 2013-09-30T18:37:22Z (GMT). No. of bitstreams: 2 WOS000311166100001.pdf: 735940 bytes, checksum: 341769f3d1c3b6eb175c4a024b1e4403 (MD5) WOS000311166100001.pdf.txt: 62690 bytes, checksum: 4f82814e1307c833e3fdb2ca9a77acf1 (MD5) Previous issue date: 2012-07-31 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:49:41Z No. of bitstreams: 2 WOS000311166100001.pdf: 735940 bytes, checksum: 341769f3d1c3b6eb175c4a024b1e4403 (MD5) WOS000311166100001.pdf.txt: 62690 bytes, checksum: 4f82814e1307c833e3fdb2ca9a77acf1 (MD5) Made available in DSpace on 2014-05-20T13:49:41Z (GMT). No. of bitstreams: 2 WOS000311166100001.pdf: 735940 bytes, checksum: 341769f3d1c3b6eb175c4a024b1e4403 (MD5) WOS000311166100001.pdf.txt: 62690 bytes, checksum: 4f82814e1307c833e3fdb2ca9a77acf1 (MD5) Previous issue date: 2012-07-31 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Background: Sleep spindles, as detected on scalp electroencephalography (EEG), are considered to be markers of thalamo-cortical network integrity. Since obstructive sleep apnea (OSA) is a known cause of brain dysfunction, the aim of this study was to investigate sleep spindle frequency distribution in OSA. Seven non-OSA subjects and 21 patients with OSA (11 mild and 10 moderate) were studied. A matching pursuit procedure was used for automatic detection of fast (>= 13Hz) and slow(< 13Hz) spindles obtained from 30min samples of NREM sleep stage 2 taken from initial, middle and final night thirds (sections I, II and III) of frontal, central and parietal scalp regions.Results: Compared to non-OSA subjects, Moderate OSA patients had higher central and parietal slow spindle percentage (SSP) in all night sections studied, and higher frontal SSP in sections II and III. As the night progressed, there was a reduction in central and parietal SSP, while frontal SSP remained high. Frontal slow spindle percentage in night section III predicted OSA with good accuracy, with OSA likelihood increased by 12.1% for every SSP unit increase (OR 1.121, 95% CI 1.013 - 1.239, p=0.027).Conclusions: These results are consistent with diffuse, predominantly frontal thalamo-cortical dysfunction during sleep in OSA, as more posterior brain regions appear to maintain some physiological spindle frequency modulation across the night. Displaying changes in an opposite direction to what is expected from the aging process itself, spindle frequency appears to be informative in OSA even with small sample sizes, and to represent a sensitive electrophysiological marker of brain dysfunction in OSA. Univ Estadual Paulista UNESP, Inst Biosci, Dept Phys & Biophys, Botucatu, SP, Brazil Hosp Clin Porto Alegre, Div Pulm Med, Sleep Lab, BR-90035003 Porto Alegre, RS, Brazil Univ Fed Sergipe, Dept Phys, Sao Cristovao, Brazil Univ Caxias do Sul, Dept Phys & Chem, BR-95001970 Caxias do Sul, Brazil Univ Estadual Paulista UNESP, Inst Biosci, Dept Phys & Biophys, Botucatu, SP, Brazil FAPESP: 09/10382-2

Made available in DSpace on 2013-08-28T14:07:17Z (GMT). No. of bitstreams: 1 WOS000287915800009.pdf: 1189727 bytes, checksum: c3209d82f7da76a81107636ca8e57377 (MD5) Made available in DSpace on 2013-09-30T18:37:19Z (GMT). No. of bitstreams: 2 WOS000287915800009.pdf: 1189727 bytes, checksum: c3209d82f7da76a81107636ca8e57377 (MD5) WOS000287915800009.pdf.txt: 71639 bytes, checksum: 4efb9ab57540588702bec7b27806cb02 (MD5) Previous issue date: 2010-12-22 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:49:39Z No. of bitstreams: 2 WOS000287915800009.pdf: 1189727 bytes, checksum: c3209d82f7da76a81107636ca8e57377 (MD5) WOS000287915800009.pdf.txt: 71639 bytes, checksum: 4efb9ab57540588702bec7b27806cb02 (MD5) Made available in DSpace on 2014-05-20T13:49:39Z (GMT). No. of bitstreams: 2 WOS000287915800009.pdf: 1189727 bytes, checksum: c3209d82f7da76a81107636ca8e57377 (MD5) WOS000287915800009.pdf.txt: 71639 bytes, checksum: 4efb9ab57540588702bec7b27806cb02 (MD5) Previous issue date: 2010-12-22 Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability. Univ Estadual Paulista, UNESP, Inst Biociencias Botucatu, Dept Fis & Biofis, BR-18618970 São Paulo, Brazil Univ Estadual Paulista, UNESP, Inst Biociencias Botucatu, Dept Fis & Biofis, BR-18618970 São Paulo, Brazil

A proteína de membrana CD38 (ADP-ribosil ciclase / ADP-ribose cíclico hidrolase 1) pertence a uma família de proteínas evolutivamente conservada que desempenha papel crucial na fisiologia humana. Primeiramente identificada como molécula de superfície de leucócitos, CD38 atualmente é considerada uma proteína multifuncional amplamente expressa dentro e fora do sistema imunológico, tendo função enzimática e como receptor. Entre os produtos de sua ação enzimática estão potentes mobilizadores dos estoques de cálcio intracelular (cADPR e NAADP) envolvidos na regulação da homeostase deste íon em muitos tipos celulares. A proteína CD38 mostra-se um importante marcador para doenças como diabetes, leucemia, mieloma, asma e na patogênese da infecção pelo HIV. O gene da proteína CD38 está localizado na banda p15 do cromossomo 4 (4p15) humano, sendo constituído por 8 exons. Mais de 98% de cerca de 77 kb do gene da CD38 é representado por sequências intrônicas. A proteína CD38 foi relatada como uma única cadeia de 45 Kda com cadeias de oligossacarídeo ligadas a resíduos de asparagina, desempenhando importante papel na estabilização de sua estrutura. Composta por um pequeno domínio citoplasmático, um domínio transmembrana e um grande domínio extracelular que contém seu sítio catalítico, a CD38 humana já tem sido demonstrada, por peso molecular, existindo na forma dimérica e tetramérica, sendo sugerido que a justaposição transmembranar de dois ou quatro monômeros da molécula possa gerar um canal cataliticamente ativo para a formação seletiva e influxo de seu produto enzimático. Tendo em vista a relevância da proteína CD38 na homeostase celular e a importância da oligomerização em sua diversidade funcional, o presente trabalho teve por objetivo prever a orientação preferencial da tetramerização da CD38 humana com sua cadeia polipeptídica da porção extracelular por meio de docking molecular. Foi utilizado um método de docking não-direcionado (ClusPro) gerando estruturas, tanto diméricas quanto tetraméricas, incompatíveis com a associação na membrana celular. Um método de docking direcionado (HADDOCK 2.2) foi utilizado e, neste, informações experimentais da literatura e cristais da proteína CD38 foram de grande importância no direcionamento. Como resultado, foi demonstrada a importância das regiões de hélice 1 na formação de dímeros e das hélices 2, 4, 6 e 9, além da região de alça entre a cadeia 5 e hélice 8 na formação de dímeros e tetrâmeros compatíveis com a associação na membrana e energeticamente possíveis, como classificado pelo método HADDOCK 2.2. As estruturas tetraméricas, criadas pelo docking direcionado com HADDOCK 2.2, formam um bolsão entre os dímeros. Tal região, na proteína inserida na membrana, poderia ser importante para o influxo do produto catalítico da proteína CD38. Esta região de bolsão foi proposta na literatura por meio de dados experimentais e funcionais. Considera-se a importância de estudos, tanto experimentais quanto in silico, que analisem a formação de tetrâmeros e sua estrutura tridimensional, além de experimentos que possam identificar, como por meio de anticorpos conformacionais, os dímeros e tetrâmeros da proteína CD38. The membrane protein CD38 (ADP-ribosyl cyclase / ADP-ribose cyclase hydrolase 1) belongs to an evolutionarily conserved protein family that plays a crucial role in human physiology. First identified as a leukocyte surface molecule, CD38 is currently considered a multifunctional protein widely expressed inside and outside the immune system, holding an enzymatic and receptor function. Among the products of its enzymatic action are potent mobilizers of intracellular calcium stocks (cADPR and NAADP) involved in regulating the homeostasis of this ion in many cell types. CD38 protein is an important marker for diseases such as diabetes, leukemia, myeloma, asthma and the pathogenesis of HIV infection. The CD38 protein gene is located in the p15 band of the human chromosome 4 (4p15), consisting of 8 exons. More than 98% of about 77 kb of the CD38 gene is represented by intronic sequences. CD38 protein has been reported as a single 45 kDa chain with oligosaccharide chains attached to asparagine residues, playing an important role in stabilizing its structure. Compounded by a small cytoplasmic domain, a transmembrane domain and a large extracellular domain containing its catalytic site, human CD38 has already been demonstrated, by molecular weight, existing in the dimeric and tetrameric form, and it is suggested that the transmembrane juxtaposition of two or four monomers of the molecule can generate a catalytically active channel for the selective formation and influx of its enzymatic product. Recognizing the relevance of the CD38 protein in cellular homeostasis and the importance of oligomerization in its functional diversity, the present work aimed to predict the preferential orientation of human CD38 tetramerization with its extracellular polypeptide chain by means of molecular docking. A non-directed docking method (ClusPro) was applied to generate structures, but both dimeric and tetrameric structures were incompatible with an association in the cell membrane. A directed docking method (HADDOCK 2.2) was applied and in this, experimental information from the literature and crystals of the CD38 protein were of high importance in targeting the active residues. As a result, was demonstrated the importance of the -helix 1 regions in the formation of dimmers and -helices 2, 4, 6 and 9, as well as the loop region between the 5 chain and -helix 8, in the formation of tetramers compatible with the membrane association and energetically possible, as classified by the HADDOCK 2.2 method. The tetrameric structures, created by the docking directed with HADDOCK 2.2, form a pocket between the dimmers. Such a region, in the membrane-inserted protein, could be important for the influx of the CD38 protein\'s catalytic product. This pocket region was proposed in the literature through experimental and functional data. It is considered the importance of studies, both experimental and in silico, that analyze the formation of tetramers and their three-dimensional structure, in addition to experiments that can identify, as by means of conformational antibodies, the dimmers and tetramers of the CD38 protein.

O uso de programas de computador para simular a formação de complexos entre proteínas é uma abordagem importante para melhor compreensão de como estas moléculas interagem. A representação paramétrica e a representação em polinômios tridimensionais de Zernike são ambas descrições compactas de superfícies moleculares baseadas em harmônicos esféricos utilizadas para visualização e comparação de superfícies moleculares e para atracamento de proteínas. Entretanto, apresentam limitações como restrição à topologia da superfície e dificuldade de representação de funções arbitrárias. Neste estudo, procurou-se refinar a capacidade de representação destes métodos para obtenção de elevada qualidade de reprodução e aplicabilidade a superfícies de topologia arbitrária. Através da análise de diversos algoritmos de suas etapas, foi possível identificar os estágios de cálculo de malha triangular de superfície molecular e de mapeamento esférico como os mais influentes na qualidade da representação paramétrica em harmônicos esféricos, e a alta sensibilidade a mudança de valores nas funções projetadas na qualidade da representação em Zernike 3D. A incorporação de um método de cálculo de superfícies que gera uma malha com elevada regularidade, aliado a um moderno algoritmo de mapeamento esférico garantiu baixo nível de distorções e obtenção de superfícies reconstruídas de alta qualidade. Uma técnica de detecção de similaridade entre superfícies de diferentes conformações de um ensemble permitiu compartilhamento de partes da descrição entre várias superfícies, com correspondente redução de volume de dados e de demanda por processamento, e com influência controlável nas distorções introduzidas. Um modo diferente de organização dos dados de entrada causou melhoria na qualidade de reconstrução de funções gerais em Zernike 3D, embora com a introdução de um mapa para restauração da função. Os resultados obtidos indicam aplicação promissora dos métodos em docking de proteínas com alto nível de detalhes. Using computer programs to simulate protein complex formation is an important approach for better comprehension of the interaction mechanisms of such molecules. The parametric representation and the Zernike polynomial method are both compact representations of molecular surfaces based on spherical harmonics, used for visualization and comparison of molecules and for protein-protein docking. They pose, however, limitations regarding surface topology and difficulty in representing arbitrary functions. In this study, the representation capacity of such methods were refined to attain high quality reproductions and applicability to arbitrary topologies. Throughout the analysis of several algorithms, the stages of surface mesh calculation and spherical mapping were identified as highly influential on the quality of the spherical harmonics parametric representation, while high sensibility to changes in the function values were identified as an influential factor for projections in 3D Zernike. A surface calculation method that generates a highly regular mesh was adopted and paired with a modern spherical mapping algorithm to yield reconstructions with low level of distortions and high quality surfaces. Similarity among surfaces from different structures in a conformational ensemble were detected to allow sharing of portions of the description among several surfaces, with corresponding reduction in data volume and processing demand and controllable influence of distortions. A new input data organization method improved the reconstruction quality of general functions in 3D Zernike, although introducing a map to restore the function. Results indicate promising application of the methods in highly detailed protein-protein docking.

Células estomáticas e reações metabólicas de plantas foram modelados por meio da teoria dos grafos neste trabalho; a distância entre estômatos vizinhos na folha foi adotada como parâmetro utilizado para a conectividade em redes onde os estômatos foram definidos como nodos. A direção da formação de produtos e substratos em reações metabólicas determinou a conectividade nas redes metabólicas, onde cada metabólito foi definido como um nodo. As redes de estômatos foram capazes de gerar uma grande quantidade de informação geométrica associada à distância entre os estômatos. Estas medidas se mostraram uma poderosa ferramenta para avaliar a plasticidade fenotípica em folhas de plantas. A adaptação de plantas a condições ambientais extremas, como altas taxas de umidade e grandes variações no tempo de exposição à luz, puderam ser quantificadas por parâmetros de redes. Parâmetros topológicos globais das redes metabólicas mostraram que elas possuem propriedades estatísticas e topológicas de redes livre escala, como nos seres vivos em geral. Entretanto, alguns parâmetros topológicos locais das redes como a medida hub-score, geram vetores de características que, se comparados entre plantas, geram informação filogenética. Além disso, nós comprovamos que é possível construir modelos que sugerem uma organização geral para o metabolismo, por meio de algorítmos de conectividade hierárquica. O algorítmo de k-cores foi usado para gerar camadas de conectividade nas redes metabólicas. A atribuição química dos metabólitos ao longo das camadas k-core, mostra que a hierarquia de conexões está associada a especialização do metabolismo. Isto sugere que o algorítimo também gera informação sobre a evolução da maquinaria metabólica. Portanto, o modelo para conectar elementos de uma rede metabólica adotado neste trabalho, traz informações naturais sobre as plantas, o que sugere que exista parâmetros físicos das reações metabólicas representados pelo modelo. Stomatic cells and metabolic reactions were modeled by graph theory in this work. The distance between stomata was adopted as connectivity parameter in the networks where stomata were defined as nodes. The direction of formation from products and substrates in the metabolic reactions, determined the connectivity from the metabolic networks, where each metabolite was defined as a node. The networks of stomata were able to generate a large amount of geometric information based at distance between the stomata. These measures represented a powerful tool to evaluate the phenotypic plasticity in leaves of plants. Global topological parameters from plant the metabolic networks revealed that plant metabolic networks has the topology of free scale networks, as in living beings in general. However, some local topological parameters of the networks such as the hub-score, can be organized as characteristic vectors with differential phylogenetic information. In addition, we have shown that it is possible to construct models that suggest a general organization for the metabolism through algorithms with iterative percolation from network connectivity. The k-cores algorithm was used to generate layers of connectivity in the metabolic networks. The chemical assignment of the metabolites along the k-core layers shows that the hierarchy of connections is associated with specialization of metabolism. This suggests that the algorithm also generates information about the evolution of the metabolic machinery. Therefore, the model used to connect elements of the metabolic networks adopted in this work, brings natural information about the plants, which suggests that there are physical parameters of the metabolic reactions represented by the model.

Em sistemas biológicos, o estudo da estabilidade das redes de regulação gênica é visto como uma contribuição importante que a Matemática pode proporcionar a pesquisas sobre câncer e outras doenças genéticas. Neste trabalho, utilizamos o conceito de ``canalização\'\' como sinônimo de estabilidade em uma rede biológica. Como as características de uma rede de regulação canalizada ainda são superficialmente compreendidas, estudamos esse conceito sob o ponto de vista computacional: propomos um modelo matemático simplificado para descrever o fenômeno e realizamos algumas análises sobre o mesmo. Mais especificamente, a estabilidade da maior bacia de atração das redes Booleanas - um clássico paradigma para a modelagem de redes de regulação - é analisada. Os resultados indicam que a estabilidade da maior bacia de atração está relacionada com dados biológicos sobre o crescimento de colônias de leveduras e que considerações sobre a interação entre as funções Booleanas e a topologia da rede devem ser realizadas conjuntamente na análise de redes estáveis. In biological systems, the study of gene regulatory networks stability is seen as an important contribution that Mathematics can make to cancer research and that of other genetic diseases. In this work, we consider the concept of ``canalization\'\' as a consequence of stability in gene regulatory networks. The characteristics of canalized regulatory networks are superficially understood. Hence, we study the canalization concept under a computational framework: a simplified model is proposed to describe the phenomenon using Boolean Networks - a classical paradigm to modeling regulatory networks. Specifically, the stability of the largest basin of attraction in gene regulatory networks is analyzed. Our results indicate that the stability of the largest basin of attraction is related to biological data on growth of yeast colonies, and that thoughts about the interaction between Boolean functions and network topologies must be given in the analysis of stable networks.