Inverse Dynamical Problems: An Algebraic Formulation Via MP Grammars

Metabolic P grammars are a particular class of multiset rewriting grammars introduced in the MP systems' theory for modelling metabolic processes. In this paper, a new algebraic formulation of inverse dynamical problems, based on MP grammars and Kronecker product, is given, for further motivating the correctness of the LGSS (Log-gain Stoichiometric Stepwise) algorithm, introduced in 2010s for solving dynamical inverse problems in the MP framework. At the end of the paper, a section is included that introduces the problem of multicollinearity, which could arise during the execution of LGSS, and that de nes an algorithm, based on a hierarchical clustering technique, that solves it in a suitable way

MP Modeling of Glucose-Insulin Interactions in the Intravenous Glucose Tolerance Test

The Intra Venous Glucose Tolerance Test (IVGTT) is an experimental pro- cedure in which a challenge bolus of glucose is administered intra-venously and plasma glucose and insulin concentrations are then frequently sampled. An open problem is to construct a model representing simultaneously the entire control system. In the last three decades, several models appeared in the literature. One of the mostly used one is known as the minimal model, which has been challenged by the dynamical model. However, both the models have not escape from criticisms and drawbacks. In this paper we apply Metabolic P systems theory for developing new physiologically based models of the glucose-insulin system which can be applied to the Intra Venous Glucose Tolerance Test. We considered ten data-sets obtained from literature and for each of them we found an MP model which ts the data and explains the regulations of the dynamics. Finally, further analysis are planned in order to de ne common patterns which explain, in general, the action of the glucose-insulin control system

Algorithms and Software for Biological MP Modeling by Statistical and Optimization Techniques

I sistemi biologici sono gruppi di entit\ue0 biologiche (es. molecole ed organismi), che interagiscono producendo specifiche dinamiche. Questi sistemi sono solitamente caratterizzati da una elevata complessit\ue0 perch\ue8 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 \ue8 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 reattivit\ue0 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 \ue8 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 \ue8 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 \ue8 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

The solvent extraction of Cobalt with tertiary amines

Imperial Users onl

Frontiers of Membrane Computing: Open Problems and Research Topics

This is a list of open problems and research topics collected after the Twelfth Conference on Membrane Computing, CMC 2012 (Fontainebleau, France (23 - 26 August 2011), meant initially to be a working material for Tenth Brainstorming Week on Membrane Computing, Sevilla, Spain (January 30 - February 3, 2012). The result was circulated in several versions before the brainstorming and then modified according to the discussions held in Sevilla and according to the progresses made during the meeting. In the present form, the list gives an image about key research directions currently active in membrane computing

Optimization Algorithms for Computational Systems Biology

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology

COMPUTATIONAL MODELING OF METABOLIC PATHWAYS TOWARD PREDICTING DYNAMIC PHENOTYPES

Metabolic systems are important to a wide variety of applications, including therapeutic development, agricultural crop production, and manufacturing of industrial chemicals. Developing metabolic models is one of the best approaches to study metabolism, as computational experiments are generally cheaper and faster to perform than experiments in a laboratory. While there are computational frameworks that can model large metabolic systems at steady state or the metabolite dynamics of a small number of key metabolic pathways, it is substantially more difficult to model the dynamics of metabolism at the genome scale. In this thesis dissertation, I present three computational platforms that address several of the challenges in developing dynamic genome-scale metabolic models. First, I devised a stepwise machine learning strategy for identifying the regulatory topology within metabolic systems, which can be used to construct more accurate metabolic models. I then developed a framework for inferring absolute concentrations from relative abundances in metabolomics data, which will allow metabolomics (the systems-scale study of metabolites) to be more easily used with metabolic modeling tools. Finally, I implemented new constraints within a linear programming dynamic modeling framework that increase its ability to model a wider variety of metabolic systems. Together, these three platforms create a cohesive workflow for modeling the dynamics of metabolism at any scale.Ph.D

A NEW FRAMEWORK FOR MATERIAL INFORMATICS AND ITS APPLICATION TOWARD ELECTRIDE-HALIDE MATERIAL SYSTEMS

Despite many years of material exploration, the vast majority of unique crystalline materials remain undiscovered, and these undiscovered materials may offer stronger steels, better catalysts, improved transistors, and many other solutions to urgent societal problems. We therefore need a fast and efficient way of identifying new materials so that society can harness their benefits. To aid in accelerated materials discovery, this dissertation describes a computational framework designed for high-throughput calculations and analyses: the Simulated Materials Ecosystem (Simmate). This software allows users to explore various crystal databases, predict new materials, quickly calculate properties, and share results across analyses. We illustrate Simmate’s functionality through the exploration of an exotic class of materials known as electrides, which have gained considerable attention in recent literature thanks to their applications as superconductors, co-catalysts, and solid-state dopants. This diverse set of applications derives from an electride’s defining property: bare electrons that exist at isolated lattice sites. “Electride electrons” effectively serve as anions, which led us to propose the direct substitution of electrides with other -1 species, namely, halides (F-, Cl-, Br-, I-). Herein, we use Simmate to explore electride-halide systems, understand transitions between such materials, and predict new systems with enhanced material properties. This work ultimately led to the identification of novel ionic conductors, metastable electrides, and new search algorithms for discovering more of the same. Our framework and high-throughput search strategies are highly generalizable and will accelerate the exploration of many different materials beyond our illustrative examples with electride-halide material systems.Doctor of Philosoph

Nutrient Cycling in Forest Ecosystems

The long-term productivity of forest ecosystems depends on the cycling of nutrients. The effect of carbon dioxide fertilization on forest productivity may ultimately be limited by the rate of nutrient cycling. Contemporary and future disturbances such as climatic warming, N-deposition, deforestation, short rotation sylviculture, fire (both wild and controlled), and the invasion of exotic species all place strains on the integrity of ecosystem nutrient cycling. Global differences in climate, soils, and species make it difficult to extrapolate even a single important study worldwide. Despite advances in the understanding of nutrient cycling and carbon production in forests, many questions remain. The chapters in this volume reflect many contemporary research priorities. The thirteen studies in this volume are arranged in the following subject groups: • N and P resorption from foliage worldwide, along chronosequences and along elevation gradients; • Litter production and decomposition; • N and P stoichiometry as affected by N deposition, geographic gradients, species changes, and ecosystem restoration; • Effects of N and P addition on understory biomass, litter, and soil; • Effects of burning on soil nutrients; • Effects of N addition on soil fauna