Marco Dimas Gubitoso

An environment for knowledge discovery in biology

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported.

A robust structural PGN model for control of cell-cycle progression stabilized by negative feedbacks

The cell division cycle comprises a sequence of phenomena controlled by a stable and robust genetic network. We applied a probabilistic genetic network (PGN) to construct a hypothetical model with a dynamical behavior displaying the degree of robustness typical of the biological cell cycle. The structure of our PGN model was inspired in well-established biological facts such as the existence of integrator subsystems, negative and positive feedback loops, and redundant signaling pathways. Our model represents genes interactions as stochastic processes and presents strong robustness in the presence of moderate noise and parameters fluctuations. A recently published deterministic yeast cell-cycle model does not perform as well as our PGN model, even upon moderate noise conditions. In addition, self stimulatory mechanisms can give our PGN model the possibility of having a pacemaker activity similar to the observed in the oscillatory embryonic cell cycle.

Reconstruction of Gray-Scale Images

We present an algorithm to reconstruct gray scale images corrupted by noise. We use a Bayesian approach. The unknown original image is assumed to be a realization of a Markov random field on a finite two dimensional region Λ ⊂ Z2. This image is degraded by some noise, which is assumed to act independently in each site of Λ and to have the same distribution on all sites. For the estimator we use the mode of the posterior distribution: the so called maximum a posteriori (MAP) estimator. The algorithm, that can be used for both gray-scale and multicolor images, uses the binary decomposition of the intensity of each color and recovers each level of this decomposition using the identification of the problem of finding the two color MAP estimator with the min-cut max-flow problem in a binary graph, discovered by Greig et al. (1989). Experimental results and a detailed example are given in the text. We also provide a web page where additional information and examples can be found.

Simulator for gene expression networks

This paper presents a simulator for gene expression networks, based on the model of chain dynamical systems (CDS). It gives the definition of CDS, describes the simulator architecture, the language adopted for describing CDS, and the available outputs. Finally, a real genetic network is studied: a subsystem of the genetic network that controls cell cycle of adrenocortical cells of the Y1 cultured cell line.

Modeling temporal morphological systems via lattice dynamical systems

This paper introduces the family of Finite Lattice Dynamical Systems (FLDS), that includes, for example, the family of finite chain dynamical systems. It also gives a constructive algebraic representation for these systems, based on classical lattice operator morphological representations, and formalizes the problem of FLDS identification from stochastic initial condition, input and ideal output. Under acceptable practical conditions, the identification problem reduces to a set of problems of lattice operator design from observed input-output data, that has been extensively studied in the context of designing morphological image operators. Finally, an application of this technique for the identification of Boolean Networks (i.e., Boolean lattice dynamical systems) from simulated data is presented and analyzed.

A New Approach for Identification of Cancer-related Pathways using Protein Networks and Genomic Data.

Cancer cells have anomalous development and proliferation due to disturbances in their control systems. The study of the behavior of cellular control system requires high-throughput dynamical data. Unfortunately, this type of data is not largely available. This fact motivates the main issue of this article: how to use static omics data and available biological knowledge to get new information about the elements of the control system in cancer cells. Two important measures to access the state of the cellular control system are the gene expression profile and the signaling pathways. This article uses a combination of these two static omics data to gain insights on the states of a cancer cell. To extract information from this kind of data, a statistical computational model was formalized and implemented. In order to exemplify the application of some aspects of the developed conceptual framework, we verified the hypothesis that different types of cancer cells have different disturbed signaling pathways. To this end, we developed a method that recovers small protein networks, called motifs, which are differentially represented in some subtypes of breast cancer. These differentially represented motifs are enriched with specific gene ontologies as well as with new putative cancer genes.

Application of Graph Database in the Storage of Heterogeneous Omics Data for the Treatment in Bioinformatics

Cancer is driven by changes in the patterns of gene expressions due to the accumulation of mutations or epigenetic changes. One of the approaches used for detecting new oncogenes is the extraction of subgraphs with four vertices from a protein-protein interaction network containing the transcriptions of the genes expressions. Due to the large amount of subgraphs formed by this dense net- work of interaction, the processing of this information by using regular files and relational databases consumes dozens of hours in the search and classification of these subgraphs; whereas a graph database stores this information by using nodes and relationships, has a shorter data query time, and the increase in the information amount does not impact on the query time of this information. The purpose of this work is the improvement of available methods for search and detection of four vertex subgraphs, aiming for higher performance through the application of a graph database. The work proposes a strategy of specification and implementation for the data import and reproduction of the methods used in the detection of new oncogenes, exposing and comparing the obtained results. The results showed that there is a significant gain in the processing performance of these data when using a graph database in the detection of motifs.

Goalkeeper Game: A new assessment instrument in neurology showed higher predictive power than MoCA for gait performance in people with parkinson\'s disease

Objective To investigate the use of the Goalkeeper Game (GG) to assess gait automaticity decline under dual task conditions in people with Parkinson’s disease (PPD) and compare its predictive power with the one of the MoCA test. Materials and Methods 74 PPD (H&Y stages: 23 in stage 1; 31 in stage 2; 20 in stage 3), without dementia (MoCA cut-off 23), tested in ON period with dopaminergic medication were submitted to single individual cognitive/motor evaluation sessions. The tests applied were: MoCA, GG, dynamic gait index (DGI) task and timed up and go test (TUG) under single and dual-task (DT) conditions. GG test resulted in 9 measures extracted via a statistical model. The predictive power of the GG measures and the MoCA score with respect to gait performance, as assessed by DGI and DT-TUG, were compared. Results The predictive models based on GG measures and MoCA score obtained, respectively, sensitivities of 65% and 56% for DGI scores and 59% and 57% for DT-TUG cost at a 50% specificity. GG application proved to be feasible and aroused more motivation in PPDs than MoCa. Conclusion GG, a friendly and ludic game, was able to reach a good power of gait performance prediction in people at initial and intermediate stages of PD evolution.

A Framework for Discrete Modeling of Juxtacrine Signaling Systems

Juxtacrine signaling is intercellular communication, in which the receptor of the signal (typically a protein) as well as the ligand (also typically a protein, responsible for the activation of the receptor) are anchored in the plasma membranes, so that in this type of signaling the activation of the receptor depends on direct contact between the membranes of the cells involved. Juxtacrine signaling is present in many important cellular events of several organisms, especially in the development process. We propose a generic formal model (a modeling framework) for juxtacrine signaling systems that is a class of dynamic discrete systems. It possesses desirable characteristics in a good modeling framework, such as: a) structural similarity with biological models, b) capacity of operating in different scales of time and c) capacity of explicitly treating both the events and molecular elements that occur in the membrane, and those that occur in the intracellular environment and are involved in the juxtacrine signaling process. We implemented this framework and used to develop a new discrete model for the neurogenic network and its participation in neuroblast segregation

Delay Behavior in Domain Decomposition Applications

This paper addresses the problem of estimating the total execution time of a parallel program based on a domain decomposition strategy. When the execution time of each processor may vary, the total execution time is non deterministic, specially if the communication to exchange boundary data is asynchronous. We consider the situation where a single iteration on each processor can take two different execution times. We show that the total time depends on the topology of the interconnection network and provide a lower bound for the ring and the grid. This analysis is supported further by a set of simulations and comparisons of specific cases.