1,721,029 research outputs found
Algorithms and Software for Biological MP Modeling by Statistical and Optimization Techniques
Full text linkI sistemi biologici sono gruppi di entità biologiche (es. molecole ed organismi), che interagiscono producendo specifiche dinamiche. Questi sistemi sono solitamente caratterizzati da una elevata complessità perchè coinvolgono un elevato numero di componenti con molte interconnessioni. La comprensione dei meccanismi che governano i sistemi biologici e la previsione dei loro comportamenti in condizioni normali e patologiche è una sfida cruciale della biologia dei sistemi (in inglese detta systems biology), un'area di ricerca al confine tra biologia, medicina, matematica ed informatica.
In questa tesi i P sistemi metabolici, detti brevemente sistemi MP, sono stati utilizzati come modello discreto per l'analisi di dinamiche biologiche. Essi sono una classe deterministica dei P sistemi classici, che utilizzano regole di riscrittura per rappresentare le reazioni chimiche e "funzioni di regolazioni di flusso" per regolare la reattività di ciascuna reazione rispetto alla quantita' di sostanze presenti istantaneamente nel sistema.
Dopo un excursus sulla letteratura relativa ad alcuni modelli convenzionali (come le equazioni differenziali ed i modelli stocastici proposti da Gillespie) e non-convenzionali (come i P sistemi ed i P sistemi metabolici), saranno presentati i risultati della mia ricerca. Essi riguardano tre argomenti principali: i) l'equivalenza tra sistemi MP e reti di Petri ibride funzionali, ii) le prospettive statistiche e di ottimizzazione nella generazione di sistemi MP a partire da dati sperimentali, iii) lo sviluppo di un laboratorio virtuale chiamato MetaPlab, un software Java basato sui sistemi MP.
L'equivalenza tra i sistemi MP e le reti di Petri ibride funzionali è stata dimostrata per mezzo di due teoremi ed alcuni esperimenti al computer per il caso di studio del meccanismo regolativo del gene operone lac nella pathway glicolitica. Il secondo argomento di ricerca concerne nuovi approcci per la sintesi delle funzioni di regolazione di flusso. La regressione stepwise e le reti neurali sono state impiegate come approssimatori di funzioni, mentre algoritmi di ottimizzazione classici ed evolutivi (es. backpropagation, algoritmi genetici, particle swarm optimization ed algoritmi memetici) sono stati impiegati per l'addestramento dei modelli. Una completo workflow per l'analisi dei dati sperimentali è stato presentato. Esso gestisce ed indirizza l'intero processo di sintesi delle funzioni di regolazione, dalla preparazione dei dati alla selezione delle variabili, fino alla generazione dei modelli ed alla loro validazione. Le metodologie proposte sono state testate con successo tramite esperimenti al computer sui casi di studio dell'oscillatore mitotico negli embrioni anfibi e del non photochemical quenching (NPQ). L'ultimo tema di ricerca è infine piu' applicativo e riguarda la progettazione e lo sviluppo di una architettura Java basata su plugin e di una serie di plugin che consentono di automatizzare varie fasi del processo di modellazione con sistemi MP, come la simulazione di dinamiche, la determinazione dei flussi e la generazione delle funzioni di regolazione.Biological systems are groups of biological entities, (e.g., molecules and organisms), that interact together producing specific dynamics. These systems are usually characterized by a high complexity, since they involve a large number of components having many interconnections. Understanding biological system mechanisms, and predicting their behaviors in normal and pathological conditions is a crucial challenge in systems biology, which is a central research area on the border among biology, medicine, mathematics and computer science.
In this thesis metabolic P systems, also called MP systems, have been employed as discrete modeling framework for the analysis of biological system dynamics. They are a deterministic class of P systems employing rewriting rules to represent chemical reactions and "flux regulation functions" to tune reactions reactivity according to the amount of substances present in the system.
After an excursus on the literature about some conventional (i.e., differential equations, Gillespie's models) and unconventional (i.e., P systems and metabolic P systems)
modeling frameworks, the results of my research are presented. They concern three research topics: i) equivalences between MP systems and hybrid functional Petri nets, ii) statistical and optimization perspectives in the generation of MP models from experimental data, iii) development of the virtual laboratory MetaPlab, a Java software based on MP systems.
The equivalence between MP systems and hybrid functional Petri nets is proved by two theorems and some in silico experiments for the case study of the lac operon gene regulatory mechanism and glycolytic pathway. The second topic concerns new approaches to the synthesis of flux regulation functions. Stepwise linear regression and neural networks are employed as function approximators, and classical/evolutionary optimization algorithms (e.g., backpropagation, genetic algorithms, particle swarm optimization, memetic algorithms) as learning techniques. A complete pipeline for data analysis is also presented, which addresses the entire process of flux regulation function synthesis, from data preparation to feature selection, model generation and statistical validation. The proposed methodologies have been successfully tested by means of in silico experiments on the mitotic oscillator in early amphibian embryos and the non photochemical quenching (NPQ). The last research topic is more applicative, and pertains the design and development of a Java plugin architecture and several plugins which enable to automatize many tasks related to MP modeling, such as, dynamics computation, flux discovery, and regulation function synthesis
On Modeling Signal Transduction Networks - Rapporto di ricerca dell'Universidad de Sevilla, Spain - RGNC REPORT 01/2008
No full textSignal transduction networks are very complex processes employed by the
living cell to suitably react to environmental stimuli. Qualitative and quantitative computational
models play an increasingly important role in the representation of these
networks and in the search of new insights about these phenomena. In this work we analyze
some graph-based models used to discover qualitative properties of such networks.
In turn, we show that MP systems can naturally extend these graph-based models by
adding some qualitative elements. The case study of integrins activation during the lymphocyte
recruitment, a crucial phenomenon in inflammatory processes, is described, and
a first MP graph for this network is designed. Finally, we discuss some open problems
related to the qualitative modeling of signaling networks
Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach
Full text linkPartially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic domains with many actions and long planning horizons is still a major challenge, and a key point to achieve good performance is guiding the action-selection process with domain-dependent policy heuristics which are tailored for the specific application domain. We propose to learn high-quality heuristics from POMDP traces of executions generated by any solver. We convert the belief-action pairs to a logical semantics, and exploit data- and time-efficient Inductive Logic Programming (ILP) to generate interpretable belief-based policy specifications, which are then used as online heuristics. We evaluate thoroughly our methodology on two notoriously challenging POMDP problems, involving large action spaces and long planning horizons, namely, rocksample and pocman. Considering different state-of-the-art online POMDP solvers, including POMCP, DESPOT and AdaOPS, we show that learned heuristics expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specific heuristics within lower computational time. Moreover, they well generalize to more challenging scenarios not experienced in the training phase (e.g., increasing rocks and grid size in rocksample, incrementing the size of the map and the aggressivity of ghosts in pocman)
Hybrid Functional Petri Nets as MP systems
No full textHybrid Functional Petri Nets, shortly HFPN, which are an extension of Petri Nets for biopathways modelling, are formalised and compared 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
A genome analysis based on repeat sharing gene networks
No full textMotivated 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
Metabolic P system flux regulation by artificial neural networks
No full textMetabolic 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 to be overcome. Among them, the flux tuners discovery aims to identify substances and parameters involved in tuning reaction
fluxes. In this paper we propose a new technique for discovering flux tuners by using 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
MP Systems and Hybrid Petri Nets
No full textMetabolic 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
No full textMetabolic 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
Psim: a computational platform for Metabolic P systems
No full textAlthough born as unconventional models of computation, P systems can be conveniently adopted as modeling frameworks for biological systems simulations. This choice brings with it the advantage of producing easier to be devised and understood models than with other formalisms. Nevertheless, the employment of P systems for modeling purposes demands biologically meaningful evolution strategies as well as complete computational tools to run simulations on. In previous papers a strategy of evolution known as the metabolic algorithm has been presented; here a simulation tool called Psim (current version 2.4) is discussed and a case study of its application is also given
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