431 research outputs found
Algorithms and Software for Biological MP Modeling by Statistical and Optimization Techniques
No full textI 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
Airo 2019 artificial intelligence and robotics
No full textThe goal of the Italian workshop series on Artificial Intelligence and RObotics(AIRO) is to present, discuss and assess recent advances in the deployment ofArtificial Intelligence (AI) methods in Robotics. AI principles and methods playa crucial role in several areas of the robotics research (e.g. field, service, socialrobotics, etc.) and are pervasively exploited at various levels of robot archi-tectures for different purposes: sensing and perception, reasoning and decision,learning, intelligent control, adaptive and social behavior, verification and valida-tion methods, etc. Starting from these diverse -yet intertwined- research fields,the AIRO workshop series aims at providing an established long-term Italianforum where the AI community and the Robotics community may find an inter-esting and stimulating common ground. This volume contains the proceedingsof the sixth edition of the AIRO workshop1, which was held in Rende, Italy,on November 22 2019. This edition of the AIRO workshop accepted 9 papersinvolving 33 authors. The program was structured into three sectionsHuman-robot interaction,Planning and Robotics, andMobile Robots. The contributionscovered several aspects of AI and Robotics in the areas of Industrial, Service andSocial Robotics and mainly concerned with the following research topics: human-robot interaction, robot learing, semantic mapping, brain computer interefaces,planning and scheduling, architectures and interfaces for robot control.The workshop program opened with the keynote talk of Prof. Daniele Nardi,full professor at Sapienza, Universit`a di Roma, titledS-AvE: Semantic ActiveVision Exploration and Mapping for Mobile Robots in Indoor Environments.The research topics and the results collected in these proceedings illustratethe work of an active and multidisciplinary research community and confirmthe growing interest for a forum where AI and Robotics researchers can find acommon ground
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
Online Monte Carlo Planning for Autonomous Robots: Exploiting Prior Knowledge on Task Similarities
No full textPlanning in large state spaces is a key problem in robot autonomy applications. In this paper we evaluate an extended version of the Partially Observable Monte Carlo Planning (POMCP) algorithm on simulated (Gazebo) and realenvironments for instances of Rocksample, where a TurtleBot is used as an agent. The extended POMCP planner exploits prior knowledge about task similarities to reduce the explored state space improving robot performance. Results show that the proposed method significantly outperforms the standard POMCP with an improvement of average discounted return up to 60.7%. This improvement implies reduced number of steps performed by the robot, shorter path lengths, reduced total running times and better energy management in long-term deployments. The main contributions are the integration of the extended POMCP planner into simulated and real robotic platforms, and performance comparison between standardand extended POMCP planners in these environments
Learning regulation functions of metabolic systems by artificial neural networks
No full textMetabolic P systems, also called MP systems, are discrete dynamical systems which proved to be effective for modeling biological systems. Their dynamics is generated by means of a metabolic algorithm based on “flux regulation functions”.
A significant problem related to the generation of MP models from experimental data concerns the synthesis of these functions. In this paper we introduce a new approach to the synthesis of MP fluxes relying on neural networks as universal function approximators, and on evolutionary algorithms as learning techniques. This methodology is successfully tested in the case study of mitotic oscillator in early amphibian embryos
MetaPlab: A Computational Framework for Metabolic P Systems
No full textIn 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
A dictionary based informational genome analysis
No full textAbstract Background In 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. Results Any 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. Conclusions We 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.</p
Rule-based shielding for Partially Observable Monte-Carlo Planning
No full textPartially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding complete policy representation. The lack of an explicit representation however hinders policy interpretability and makes policy verification very complex. In this work, we propose two contributions. The first is a method for identifying unexpected actions selected by POMCP with respect to expert prior knowledge of the task. The second is a shielding approach that prevents POMCP from selecting unexpected actions. The first method is based on Satisfiability Modulo Theory (SMT). It inspects traces (i.e., sequences of belief-action-observation triplets) generated by POMCP to compute the parameters of logical formulas about policy properties defined by the expert. The second contribution is a module that uses online the logical formulas to identify anomalous actions selected by POMCP and substitutes those actions with actions that satisfy the logical formulas fulfilling expert knowledge. We evaluate our approach on Tiger, a standard benchmark for POMDPs, and a real-world problem related to velocity regulation in mobile robot navigation. Results show that the shielded POMCP outperforms the standard POMCP in a case study in which a wrong parameter of POMCP makes it select wrong actions from time to time. Moreover, we show that the approach keeps good performance also if the parameters of the logical formula are optimized using trajectories containing some wrong actions
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