The Discovery of Initial Fluxes of Metabolic P Systems

A central issue in systems biology is the study of efficient methods to infer fluxes of biological reactions starting from experimental data. Among the different techniques proposed in the last years, in the theory of Metabolic P systems Log-Gain principles have been introduced, which prove to be helpful for deducing biological fluxes from temporal series of observed dynamics. However, crucial tasks remain to be performed for a complete suitable application of these principles. In particular the algebraic systems introduced by the Log-Gain principles require the knowledge of the initial fluxes associated with a set of biochemical reactions. In this paper we propose an algorithm for estimating initial fluxes, which is tested in two case studies

Metabolic Reprogramming and Reconstruction: Integration of Experimental and Computational Studies to Set the Path Forward in ADPKD

Metabolic reprogramming is a key feature of Autosomal Dominant Polycystic Kidney Disease (ADPKD) characterized by changes in cellular pathways occurring in response to the pathological cell conditions. In ADPKD, a broad range of dysregulated pathways have been found. The studies supporting alterations in cell metabolism have shown that the metabolic preference for abnormal cystic growth is to utilize aerobic glycolysis, increasing glutamine uptake and reducing oxidative phosphorylation, consequently resulting in ADPKD cells shifting their energy to alternative energetic pathways. The mechanism behind the role of the polycystin proteins and how it leads to disease remains unclear, despite the identification of numerous signaling pathways. The integration of computational data analysis that accompanies experimental findings was pivotal in the identification of metabolic reprogramming in ADPKD. Here, we summarize the important results and argue that their exploitation may give further insights into the regulative mechanisms driving metabolic reprogramming in ADPKD. The aim of this review is to provide a comprehensive overview on metabolic focused studies and potential targets for treatment, and to propose that computational approaches could be instrumental in advancing this field of research

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

Linking Bistable Dynamics to Metabolic P Systems

Bistability, or more generally multistability, is an important recurring theme in biological systems. In particular, the discovery of bistability in signal pathways of genetic networks, prompts strong interest in understanding both the design and function of these networks. Therefore, modelling these systems is crucial to understand their behaviors, and also to analyze and identify characteristics that would otherwise be di cult to realize. Although di erent classes of models have been used to study bistable dynamics, there is a lag in the development of models for bistable systems starting from experimental data. This is due to the lack of detailed knowledge of biochemical reactions and kinetic rates. In this work, we propose a procedure to develop, starting from observed dynamics, Metabolic P models for multistable processes. As a case study, a mathematical model of the Schl ogel's dynamics, which represents an example of a chemical reaction system that exhibits bistability, is inferred starting from observed stochastic bistable dynamics. Since, recent experiments indicate that noise plays an important role in the switching of bistable systems, the success of this work suggests that this approach is a very promising one for studying dynamics and role of noise in biological systems, such as, for example, genetic regulatory networks

An Algorithm for Initial Fluxes of Metabolic P Systems

A central issue in systems biology is the study of efficient methods inferring fluxes of biological reactions by starting from experimental data. Among the different techniques proposed in the last years, the theory of Metabolic P systems, which is based on the Log-Gain principle, proved to be helpful for deducing biologi- cal fluxes from temporal series of observed dynamics. According to this approach, the algebraic systems provided by the Log-Gain principle determine the reaction fluxes underlying a system dynamics when initial fluxes are known. Here we propose a heuristic algorithm for estimating the initial fluxes, that is tested in two case studies

Dissection of metabolic reprogramming in polycystic kidney disease reveals coordinated rewiring of bioenergetic pathways.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder caused by loss-of-function mutations in PKD1 or PKD2. Increased glycolysis is a prominent feature of the disease, but how it impacts on other metabolic pathways is unknown. Here, we present an analysis of mouse Pkd1 mutant cells and kidneys to investigate the metabolic reprogramming of this pathology. We show that loss of Pkd1 leads to profound metabolic changes that affect glycolysis, mitochondrial metabolism, and fatty acid synthesis (FAS). We find that Pkd1-mutant cells preferentially use glutamine to fuel the TCA cycle and to sustain FAS. Interfering with either glutamine uptake or FAS retards cell growth and survival. We also find that glutamine is diverted to asparagine via asparagine synthetase (ASNS). Transcriptional profiling of PKD1-mutant human kidneys confirmed these alterations. We find that silencing of Asns is lethal in Pkd1-mutant cells when combined with glucose deprivation, suggesting therapeutic approaches for ADPKD

Modelling and Reverse-Engineering of Biological Phenomena by means of Metabolic P Systems

Le reti biologiche hanno un ruolo cruciale in ogni processo vitale: meccanismi di regolazione genetica, differenziazione cellulare, metabolismo, ciclo cellulare, e trasduzione intracellulare del segnale. Progressi nei metodi sperimentali hanno permesso studi su larga scala di queste reti e possono rivelare la loro logica. Di conseguenza, i biologi devono integrare grandi quantit\ue0 di dati sperimentali a analizzare reti complesse. I modelli matematici sono strumenti essenziali per collegare i comportamenti di un sistema con le interazioni tra le sue componenti. Modelli di reti biochimiche possono portare benefici in diversi campi. In medicina, malattie legate a disfunzioni di meccanismi genetici possono essere delucidate. La farmaceutica, potrebbe essere avvantaggiata nella ricerca di nuovi trattamenti e medicinali. Progetti biotecnologici possono trarre benefici da modelli predittivi che sostituiranno noiosi e costosi esperimenti di laboratorio. E, analisi computazionali possono contribuire alla ricerca biologica di base. Perci\uf2, il successo della Biologia dei Sistemi certamente richieder\ue0 nuovi strumenti di simulazione e modellazione, nonch\ue9 nuovi approcci di reverse-engineering. Negli ultimi anni, molti tools e modelli computazionali sono stati sviluppati per l'analisi di sistemi biologici. Tuttavia, probabilmente la nostra corrente visione di come le regolazioni vengono svolte non sta prendendo in considerazioni alcuni elementi. Nuovi e ulteriori esperimenti sono necessari, mentre i risultati sperimentali devono essere incorporati in nuovi modelli. Questo \ue8 legato alla necessit\ue0 di migliorare gli approcci di reverse-engineering che usano serie temporali. Rimarchiamo inoltre che l'integrazione di diversi tipi di reti biologiche sar\ue0 un passo fondamentale verso l'obiettivo che si \ue8 posto la Biologia dei Sistemi. Questo trarr\ue0 beneficio dallo sviluppo di nuovi strumenti di modellazione che tengono in considerazione diverse entit\ue0 (geni, proteine, metaboliti, ecc...) e relazioni (reazioni metaboliche, interazioni, regolazione, ecc...). Questo rappresenta un settore dove nuovi formalismi di modellazione e strumenti di simulazione avranno un grande valore aggiunto. Diversi problemi e richieste emergono in questa direzione, come, per esempio, l'utilizzo di informazioni incomplete, la manipolazione, l'uso e l'analisi di grandi e complessi modelli, l'estrazione di conoscenze relative ai meccanismi regolativi, l'inferenza di utili modelli a partire da dati sperimentali. Tuttavia, i frameworks esistenti difficilmente possono soddisfare interamente queste richieste, e questo si riflette nella ricerca di nuovi modelli computazionali. Inoltre, una delle pi\uf9 importanti necessit\ue0 nell'affrontare la complessit\ue0 dei sistemi biologici sembra essere la possibilit\ue0 di osservare questi sistemi da adeguati livelli di astrazione. In questa direzione, i Metabolic P systems (MP systems) sono stati introdotti. Essi sono un nuovo modello computazionale che fornisce una macroscopica, globale, e tempo-discreto prospettiva nell'analisi di processi biologici e delle relative dinamiche. Gli MP systems offrono questi vantaggi: i) una naturale mappatura tra elementi reali ed elementi del modello, ii) la possibilit\ue0 di adattare la prospettiva del modello alla risoluzione temporale dei dati osservati, e iii) la teoria Log-Gain. Grazie a questa teoria, il processo di modelling mediante l'utilizzo degli MP systems pu\uf2 essere ricondotto ad un problema di reverse-engineering. Tuttavia, problemi aperti rimangono da risolvere. In questa Tesi, proponiamo soluzioni per questi problemi. Essa parte dal punto di vista dell'informazione biologica e di come viene processata negli organismi viventi. Quindi, dopo un a visione d'insieme delle tecniche di modellazione in biologica computazionale e dei tools che le supportano, la Tesi si focalizza sul tema principale: la modellazione e il reverse-engineering di fenomeni biologici mediate l'utilizzo degli MP systems. I primi risultati dimostrano l'utilit\ue0 degli MP systems per modellare diverse classi di fenomeni. Infatti, i) abbiamo modellato la glicolisi nel Saccharomyces cerevisiae e una rete genetica sintetica, ii) abbiamo sviluppato una pipeline per la stima di MP systems di sistemi bistabili/multistabili. Altri risultati riguardano MetaPlab, uno strumento software Java implementato per automatizzare la fase di modellazione, reverse-engineering, e analisi di fenomeni biochimici mediante l'utilizzo di MP systems. L'autore ha contribuito allo sviluppo di un plug-in per il computo dei flussi e nella realizzazione della guida di MetaPlab e di tutorials di diversi plu-ins. I rimanenti risultati rappresentano il cuore di questo lavoro e sono legati alla teoria Log-Gain, la quale rappresenta il primo passo per ottenere un MP system a partire da dati sperimentali. Abbiamo proposto soluzioni per i problemi aperti e i tasks cruciali, in maniera tale da sviluppare uno strumento che potesse essere applicato per l'inferenza di modelli MP che potessero spiegare dinamiche osservate. In particolare, i) abbiamo dimostrato che la teoria Log-Gain pu\uf2 essere applicata anche in caso di mancanza di informazioni circa i meccanismi regolativi, ii) abbiamo raggiunto risultati teorici riguardanti la computazione efficiente dei flussi reattivi, iii) abbiamo proposto un algoritmo euristico per calcolare i flussi iniziali, elementi indispensabili per l'applicazione della teoria Log-Gain, iv) abbiamo sviluppato una pipeline per l'analisi dei dati che affronta l'intero processo di sintesi delle funzioni di regolazione degli MP systems dalla preparazione dei dati alla validazione del modello. Inoltre, questa Tesi presenta il primo modello MP dedotto applicando la teoria Log-Gain applicata a dati sperimentali. Infatti, abbiamo definito il modello MP di un importante fenomeno fotosintetico chiamato Non Photochemical Quenching, il quale determina l'adattamento delle piante alle luce ambientale. Visto che nessun modello per questo fenomeno era stato precedentemente sviluppato, questo risultato mostra i vantaggi della teoria Log-Gain per la deduzione di modelli matematici di sistemi complessi. In questa maniera la teoria degli MP systems si dimostra essere un nuovo strumento per la costruzione di modelli, in particolare in quei casi dove la valutazione di parametri cinetici risulta difficoltosa. Questo perch\ue9 la teoria Log-Gain permette di evitare analisi a livello microscopico. Ricordiamo anche i modelli che abbiamo ottenuto per la mitosi cellulare negli embrioni anfibi e il signaling pathway legato al metabolismo dell'insulina. I risultati raggiunti per il primo modello dimostrano che il nostro framework e abile nel catturare le caratteristiche salienti di un sistema anche quando viene osservato da un punto di vista macroscopico. Diversamente, i risultati del secondo modello presentano investigazioni relative all'implementazione su Graphic Processing Units (GPU) degli algoritmi per la stima dei flussi sviluppati nel contesto della teoria Log-Gain. Questa piattaforma hardware permette computazione massivamente parallele. Il problema del calcolo dei flussi in sistemi biologici pu\uf2 chiaramente ottenere benefici dalla parallelizzazione. Studi su simulazioni e una comparazione con MatLab dimostra che un implementazione GPU supera un'implementazione puramente sequenziale. In conclusione, richiamiamo che in cerca di soluzioni per i problemi aperti relativi alla teoria Log-Gain, il legame degli MP systems con una variet\ue0 di metodi, che vanno dall'algebra lineare, all'ottimizzazione su spazi vettoriali e reti neurali, \ue8 stata evidenziata.Biological networks have a crucial role in each process of life, including gene regulatory mechanisms, cell differentiation, metabolism, the cell cycle, and signal transduction. Advances in experimental methods have enabled large-scale studies of these networks and can reveal the logic that underlies them. Consequently, biologists must integrate great quantities of experimental data and analyze complex networks. Mathematical models are essential tools to link the behaviours of a system to the interaction between its components. Models of biochemical networks are expected to benefit several fields. In medicine, mechanisms of diseases which are characterized by dysfunctions of regulatory processes can be elucidated. Pharmaceutics could take advantage in the search of new treatments and drugs. Biotechnological projects can benefit from predictive models that will replace some tedious and costly experiments in laboratory. And, computational analysis may contribute to basic biological research. Therefore, the success of Systems Biology will certainly require new modelling, simulation tools, and reverse-engineering approaches. In the last years, many tools and computational models have been developed for biological systems analysis. Nonetheless, our current picture of how regulations are carried out is probably still missing several significant pieces. More experimental work is needed, and these experimental results must be incorporated in improved models. This is linked with the necessity to improve reverse-engineering approaches which use time-series data. We emphasize that integration of different types of biological networks will be a fundamental step to the goal of Systems Biology. This would benefit from the creation of a common modelling framework which takes into account different entities, such as genes, proteins, metabolites, etc..., and relationships, like metabolic reactions, interactions, regulations, transports, etc... This represents a field where novel modelling formalisms and simulation tools will have great added value. Several problems and requirements arise toward this advance, such as how to deal with incomplete information, how to manipulate large models, how to extract valuable information about the regulative mechanisms, how to analyse these models, and how to infer suitable models from experimental data. However, the existing frameworks can hardly fulfill such demands, which reflects the need to search for suitable computational models. Moreover, one of the most important features for handling the high complexity of biological phenomena seems to be the possibility to observe these systems from an adequate abstraction level. Along this direction, the Metabolic P systems (MP systems) have been introduced. They are a new computational model which provides a macroscopic, global and time-discrete perspective on metabolic processes and related dynamics. Advantages of this approach are a i) natural mapping between real elements and model elements, ii) the possibility to adapt the model perspective to the temporal grain of observed data, and iii) the Log-Gain theory. According with this theory, the MP modelling process of a biochemical systems can be reduced to a reverse-engineering problem. However, crucial tasks and open problems remained to be performed for a complete discovery of the underlying MP system which explains an observed dynamics. In this Thesis, we propose solutions for these problems and tasks. It starts from the standpoint of biological information and its processing in living organisms. Then, after an overview about modelling and tools in computational biosystems, the Thesis is focused on the main theme: modelling and reverse-engineering of biological phenomena by means of MP systems. The first results prove the usefulness of MP systems to model several classes of phenomena. In fact, i) we modelled the upper part of the glycolysis in Saccharomyces cerevisiae and a synthetic oscillatory genetic network, ii) we developed a work-flow for the estimation of MP systems describing the dynamics of bistable/multistable phenomena. Others results concern MetaPlab, a Java software implemented to automatize modelling, reverse-engineering, and analysis of biochemical phenomena by means of MP systems. The author contributes to develop a flux discovery plugin and to realize a MetaPlab user guide and plugin tutorials. The remaining results represent the core of this work and are related to the Log-Gain theory, which represents the first step to obtain an MP system starting from experimental data. We performed the crucial tasks and solved the open problems of this theory, in order to have a framework useful for a complete discovery of the underlying MP system explaining an observed dynamics. In particular, i) we proved that the Log-Gain theory can be applied even with a lack of information about the regulative mechanisms; ii) we reported results regarding the efficient computations of reaction fluxes; iii) we proposed a heuristic algorithm to compute initial reaction fluxes, which are needed for the application of the Log-Gain theory; iv) we developed a complete pipeline for data analysis which addresses the entire process of flux regulation function synthesis and regulators discovery from data preparation to model validation. Moreover, this Thesis provides the first MP model deduced by means of the Log-Gain theory from experimental data. In fact, we defined an MP model of an important photosynthetic phenomenon called Non Photochemical Quenching, which determines the plant accommodation to the environmental light. Since no previous mathematical models of this phenomenon were available, this result shows the advantage of the Log-Gain theory for deducing mathematical models describing complex systems. In this manner the theory of MP systems can be seen as a new tool for constructing models, where the difficulty of kinetic rate constants evaluation is solved by the log-gain procedure, avoiding analysis at microscopic level. We also recall the models that we obtained for the mitotic oscillator in early amphibian embryos and the metabolic insulin signaling pathway. The results achieved for the first model prove that our framework is able to capture the salient characteristics of a system, also when it is observed from a macroscopic point of view. Differently, the results of the second model present investigations on the use of Graphic Processing Units (GPU) in the context of flux estimation by means of Log-Gain theory. These results are relevant in the framework of fluxes estimation since they highlight the potentialities of MP systems to infer biological fluxes when the size of a phenomenon increases. Simulation studies and a comparison with MatLab clearly shows that the (GPU) implementation outperforms pure sequential counterparts. Finally, we point out that in the search of solutions for the open problems of the Log-Gain theory, a variety of methods naturally occurred, going from vector algebra and vector optimization to artificial neural networks

Inference of Biological Pathways by Integrating Different Kinds of Correlation Indexes

Different kinds of causality are analyzed in the context of modeling of biological dynamics