Alberto Castellini

MetaPlab: A Computational Framework for Metabolic P Systems

In this work the formalism of metabolic P systems is employed as a basis of a new computational framework for modeling biological networks. The proposed software is a virtual laboratory, called MetaPlab, which supports the synthesis of metabolic P systems by means of an extensible plugin-based architecture. The Java implementation of the software is outlined and a specific plugin at work is described to highlight the internal functioning of the whole architecture.

Data analysis pipeline from laboratory to MP models

A workflow for data analysis is introduced to synthesize flux regulation maps of a Metabolic P system from time series of data observed in laboratory. The procedure is successfully tested on a significant case study, the photosynthetic phenomenon called NPQ, which determines plant accommodation to environmental light. A previously introduced MP model of such a photosynthetic process has been improved, by providing an MP system with a simpler regulative network that reproduces the observed behaviors of the natural system. Two regression techniques were employed to find out the regulation maps, and interesting experimental results came out in the context of their residual analysis for model validation.

A dictionary based informational genome analysis.

BackgroundIn the post-genomic era several methods of computational genomics are emerging to understand how the whole information is structured within genomes. Literature of last five years accounts for several alignment-free methods, arisen as alternative metrics for dissimilarity of biological sequences. Among the others, recent approaches are based on empirical frequencies of DNA k-mers in whole genomes.ResultsAny set of words (factors) occurring in a genome provides a genomic dictionary. About sixty genomes were analyzed by means of informational indexes based on genomic dictionaries, where a systemic view replaces a local sequence analysis. A software prototype applying a methodology here outlined carried out some computations on genomic data. We computed informational indexes, built the genomic dictionaries with different sizes, along with frequency distributions. The software performed three main tasks: computation of informational indexes, storage of these in a database, index analysis and visualization. The validation was done by investigating genomes of various organisms. A systematic analysis of genomic repeats of several lengths, which is of vivid interest in biology (for example to compute excessively represented functional sequences, such as promoters), was discussed, and suggested a method to define synthetic genetic networks.ConclusionsWe introduced a methodology based on dictionaries, and an efficient motif-finding software application for comparative genomics. This approach could be extended along many investigation lines, namely exported in other contexts of computational genomics, as a basis for discrimination of genomic pathologies.

Hybrid Functional Petri Nets as MP systems

In this work we give a formalization of Hybrid Functional Petri Nets, shortly HFPN, an extension of Petri Nets for biopathways modelling, and we compare them with Metabolic P Systems. An introduction to both the formalisms is given, together with highlights about respective similarities and differences. Their equivalence is thus proved by means of a theorem which holds under quite general hypotheses. The case study of the lac operon gene regulatory mechanism in the glycolytic pathway of Escherichia coli is modeled by an MP system which provides the same dynamics of an equivalent HFPN model.

Toward a Representation of Hybrid Functional Petri Nets by MP Systems

In this work we analyse and compare Hybrid Functional Petri Nets [10], an extension of Petri Nets [12] for biopathways simulation, and Metabolic P Systems [8,9]. An introduction to both of them is given, together with highlights about respective similarities and differences for biopathways modelling. The case study of glycolytic pathway with the lac operon gene regulatory mechanism was modeled by traditional Petri Nets in [6] and recently by Hybrid Functional Petri Nets in [10,4]. This model is here mapped into an MP system having the same dynamics.

A genome analysis based on repeat sharing gene networks

Motivated by an interest to understand how information is organized within genomes, and how genes communicate between each other in the transcription process, in this paper we propose a novel network based methodology for genomic sequence analysis, specifically applied to three organisms: Nanoarchaeum equitans, Escherichia coli, and Saccaromyces cerevisiae. A dictionary based approach previously introduced is here continued through a repeat analysis in genic and intergenic regions. Key results of this work have been found in a biological and computational analysis of novel parametrized gene networks, defined by means of motifs of fixed length occurring inside multiple genes. Cliques emerge as groups of genes sharing a long repeat with a clear biological interpretation, while a (complete, paralog) cluster analysis has outlined some unexpected regularity. Repeat sharing gene networks may be applied in contexts of comparative genomics, as an investigation methodology for a comprehension of evolutional and functional properties of genes.

MP Systems and Hybrid Petri Nets

Metabolic P systems are a special class of P systems developed to model dynamics of biological phenomena related to metabolism and signaling transduction in the living cell. The main target of this model is to give an intuitive representation of biochemical pathways in order to facilitate the understanding of biological mechanisms. A new notation of MP graphs [16] will be defined as a graphical representation of MP systems and the graphical user interface we devised to draw MP graphs while working with our MP simulator Psim [4] will be described. We will propose also a comparison between MP systems and Hybrid Functional Petri Nets (HFPN) [19], which are an extension of Petri nets for biopathways simulation, to highlight several similarities between the two formalisms. Finally, a definition of equivalence between MP systems and HFPN will conclude the paper.

An evolutionary procedure for inferring MP systems regulation functions of biological networks

Metabolic P systems are a modeling framework for metabolic, regulatory and signaling processes. The key point of MP systems are flux regulation functions, which determine the evolution of a system from a given initial state. This paper presents important improvements to a technique, based on genetic algorithms and multiple linear regression, for inferring regulation functions that reproduce observed behaviors (time series datasets). An accurate analysis of three case studies, namely the mitotic oscillator in early amphibian embryos, the Lodka–Volterra predator-prey model and the chaotic logistic map show that this methodology can provide, from observed data, significant knowledge about the regulation mechanisms underlying biological processes.

Towards an MP Model for B Lymphocytes Maturation

A first dynamical model is given to explain maturation steps of B limphocytic cells in human body, based on Metabolic P systems with genetic regression of regulation maps. In humans, B cell development constitutes the steps that lead to B cell commitment and to expression of surface immunoglobulin, which is essential for B cell survival and function. Mature and fully functional B cells population (CD19+) include phenotypically and functionally different subgroups which persist during all stages of B-cell maturation. Quantities of eight different subgroups of B cells, identified by presence or absence of given receptors, were measured in about six thousands patients of all ages. Here we present a long work of preparation and analysis of (ex-vivo) data, and a preliminary model to explain the eight statistically refined time series, which opened up interesting questions about network inference and methodologies to analyze cross sectional data.

Metabolic p system flux regulation by artificial neural networks

Metabolic P systems are an extension of P systems employed for modeling biochemical systems in a discrete and deterministic perspective. The generation of MP models from observed data of biochemical system dynamics is a hard problem which requires to solve several subproblems. Among them, flux tuners discovery aims to identify substances and parameters involved in tuning each reaction flux. In this paper we propose a new technique for discovering flux tuners by means of neural networks. This methodology, based on backpropagation with weight elimination for neural network training and on an heuristic algorithm for computing tuning indexes, has achieved encouraging results in a synthetic case study.