Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 7 de 7
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Sci Rep ; 10(1): 13303, 2020 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-32764598

RESUMO

All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.

2.
J Chem Inf Model ; 60(2): 537-545, 2020 Feb 24.
Artigo em Inglês | MEDLINE | ID: mdl-31917570

RESUMO

In this work, we report an ab initio investigation based on density functional theory calculations within van der Waals D3 corrections to investigate the adsorption properties and activation of CO2 on transition-metal (TM) 13-atom clusters (TM = Ru, Rh, Pd, Ag), which is a key step for the development of subnano catalysts for the conversion of CO2 to high-value products. From our analyses, which include calculations of several properties and the Spearman correlation analysis, we found that CO2 adopts two distinct structures on the selected TM13 clusters, namely, a bent CO2 configuration in which the OCO angle is about 125 to 150° (chemisorption), which is the lowest energy CO2/TM13 configuration for TM = Ru, Rh, Pd. As in the gas phase, the linear CO2 structure yields the lowest energy for CO2/Ag13 and several higher energy configurations for TM = Ru, Rh, Pd. The bent CO2 (activated) is driven by a chemisorption CO2-TM13 interaction due to the charge transfer from the TM13 clusters toward CO2, while a weak physisorption interaction is obtained for the linear CO2 on the TM13 clusters. Thus, the CO2 activation occurs only in the first case and it is driven by charge transfer from the TM13 clusters to the CO2 molecule (i.e., CO2-δ), which is confirmed by our Bader charge analysis and vibrational frequencies.

3.
Sci Rep ; 6: 25570, 2016 05 09.
Artigo em Inglês | MEDLINE | ID: mdl-27158092

RESUMO

A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.

4.
Neural Netw ; 24(1): 54-64, 2011 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-20884173

RESUMO

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system.


Assuntos
Atenção/fisiologia , Mapeamento Encefálico , Encéfalo/fisiologia , Simulação por Computador , Redes Neurais de Computação , Reconhecimento Visual de Modelos/fisiologia , Diagnóstico por Imagem , Humanos , Dinâmica não Linear , Estimulação Luminosa
5.
Chem Biol Drug Des ; 75(6): 632-40, 2010 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-20565477

RESUMO

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher's weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.


Assuntos
Antipsicóticos/química , Canabinoides/química , Redes Neurais de Computação , Antipsicóticos/farmacologia , Canabinoides/farmacologia , Cannabis/química , Modelos Químicos , Modelos Moleculares , Relação Quantitativa Estrutura-Atividade , Teoria Quântica
6.
Neural Netw ; 22(5-6): 728-37, 2009.
Artigo em Inglês | MEDLINE | ID: mdl-19595565

RESUMO

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.


Assuntos
Atenção , Redes Neurais de Computação , Periodicidade , Percepção Visual , Algoritmos , Simulação por Computador , Humanos , Estimulação Luminosa , Fatores de Tempo
7.
Chaos ; 18(3): 033107, 2008 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-19045445

RESUMO

In many real situations, randomness is considered to be uncertainty or even confusion which impedes human beings from making a correct decision. Here we study the combined role of randomness and determinism in particle dynamics for complex network community detection. In the proposed model, particles walk in the network and compete with each other in such a way that each of them tries to possess as many nodes as possible. Moreover, we introduce a rule to adjust the level of randomness of particle walking in the network, and we have found that a portion of randomness can largely improve the community detection rate. Computer simulations show that the model has good community detection performance and at the same time presents low computational complexity.


Assuntos
Algoritmos , Tomada de Decisões , Técnicas de Apoio para a Decisão , Modelos Estatísticos , Dinâmica não Linear , Simulação por Computador
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA