Leonardo Bacelar Lima Santos

A cellular automata model for avascular solid tumor growth under the effect of therapy

Tumor growth has long been a target of investigation within the context of mathematical and computer modeling. The objective of this study is to propose and analyze a two-dimensional stochastic cellular automata model to describe avascular solid tumor growth, taking into account both the competition between cancer cells and normal cells for nutrients and/or space and a time-dependent proliferation of cancer cells. Gompertzian growth, characteristic of some tumors, is described and some of the features of the time-spatial pattern of solid tumors, such as compact morphology with irregular borders, are captured. The parameter space is studied in order to analyze the occurrence of necrosis and the response to therapy. Our findings suggest that transitions exist between necrotic and non-necrotic phases (no-therapy cases), and between the states of cure and non-cure (therapy cases). To analyze cure, the control and order parameters are, respectively, the highest probability of cancer cell proliferation and the probability of the therapeutic effect on cancer cells. With respect to patterns, it is possible to observe the inner necrotic core and the effect of the therapy destroying the tumor from its outer borders inwards.

Detecting Network Communities: An Application to Phylogenetic Analysis

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis.

Comparative protein analysis of the chitin metabolic pathway in extant organisms: A complex network approach

Chitin is a structural endogenous carbohydrate, which is a major component of fungal cell walls and arthropod exoskeletons. A renewable resource and the second most abundant polysaccharide in nature after cellulose, chitin is currently used for waste water clearing, cosmetics, medical, and veterinary applications. This work comprises data mining of protein sequences related to the chitin metabolic pathway of completely sequenced genomes of extant organisms pertaining to the three life domains, followed by meta-analysis using traditional sequence similarity comparison and complex network approaches. Complex networks involving proteins of the chitin metabolic pathway in extant organisms were constructed based on protein sequence similarity. Several usual network indices were estimated in order to obtain information on the topology of these networks, including those related to higher order neighborhood properties. Due to the assumed evolutionary character of the system, we also discuss issues related to modularity properties, with the concept of edge betweenness playing a particularly important role in our analysis. Complex network approach correctly identifies clusters of organisms that belong to phylogenetic groups without any a priori knowledge about the biological features of the investigated protein sequences. We envisage the prospect of using such a complex network approach as a high-throughput phylogenetic method.

Phylogeography of foot-and-mouth disease virus serotype O in Ecuador

Foot-and-mouth disease virus (FMDV) is the causative agent of the most important disease of domestic cattle, foot-and-mouth disease. In Ecuador, FMDV is maintained at an endemic state, with sporadic outbreaks. To unravel the tempo and mode of FMDV spread within the country we conducted a Bayesian phylogeographic analysis using a continuous time Markov chain (CTMC) to model the diffusion of FMDV between Ecuadorian provinces. We implement this framework through Markov chain Monte Carlo available in the BEAST package to study 90 FMDV serotype O isolates from 17 provinces in the period 2002-2010. The Bayesian approach also allowed us to test hypotheses on the mechanisms of viral spread by incorporating environmental and epidemiological data in our prior distributions and perform Bayesian model selection. Our analyses suggest an intense flow of viral strains throughout the country that is possibly coupled to animal movements and ecological factors, since most of inferred viral spread routes were in Coast and Highland regions. Moreover, our results suggest that both short- and long-range spread occur within Ecuador. The province of Esmeraldas, in the border with Colombia and where most animal commerce is done, was found to be the most probable origin of the circulating strains, pointing to a transboundary behavior of FMDV in South America. These findings suggest that uncontrolled animal movements can create a favorable environment for FMDV maintenance and pose a serious threat to control programmes. Also, we show that phylogeographic modeling can be a powerful tool in unraveling the spatial dynamics of viruses and potentially in controlling the spread of these pathogens.

A framework for modeling and simulating aedes aegypti and dengue fever dynamics

Dengue fever represents a great challenge for many countries, and methodologies to prevent and/or control its transmission have been largely discussed by the research community. Modeling is a powerful tool to understand epidemic dynamics and to evaluate costs, benefits and effectiveness of control strategies. In order to assist decision-makers and researchers in the evaluation of different methodologies, we developed DengueME, a collaborative open source platform to simulate dengue disease and its vectors dynamics. DengueME provides a series of compartmental and individual-based models, implemented over a GIS database, that represents the Aedes aegyptis life cycle, human demography, human mobility, urban landscape and dengue transmission. The platform is designed to allow easy simulation of intervention scenarios. A GUI was developed to facilitate model configuration and data input.

Comparação entre uma neuroprevisão (empírica) e um modelo físico simplificado para estimação hidrológica

O monitoramento de desastres naturais no Brasil e responsabilidade do Centro Nacional de Monitoramento e Alertas de Desastres Naturais (Cemaden), que trabalha em colaboracao com diversos orgaos e conta com uma divisao dedicada a pesquisa cientifica. Modelos empiricos baseados em dados utilizam tecnicas estatisticas e/ou de aprendizado de maquina para, dado um banco de dados para treinamento, promover estimacoes frente a novos padroes de entrada. O produto neuroprevisao consiste em uma Rede Neural Artificial aplicada para prever o nivel de um dado rio. Por outro lado, modelos fisicos utilizam equacoes referentes ao fenomeno modelado, e os parâmetros de tais equacoes podem ser estimados com base em dados observacionais. O produto Modelagem hidrologica rapida e baseado na equacao do tempo de translado. Este trabalho promove comparacoes entre diferentes abordagens em fase de testes operacionais no Cemaden.