Modeling of the Glycolysis Pathway in Plasmodium falciparum using Petri Nets

Malaria is one of the deadly diseases, which affects a large number of the world’s population. The Plasmodium falciparum parasite during erythrocyte stages produces its energy mainly through anaerobic glycolysis, with pyruvate being converted into lactate. The glycolysis metabolism in P. falci-parum is one of the important metabolic pathways of the parasite because the parasite is entirely dependent on it for energy. Also, several glycolytic enzymes have been proposed as drug targets. Petri nets (PNs) have been recognized as one of the important models for representing biological pathways. In this work, we built a qualitative PN model for the glycolysis pathway in P. falciparum and analyzed the model for its structural and quantitative properties using PN theory. From PlasmoCyc files, a total of 11 reactions were extracted; 6 of these were reversible and 5 were irreversible. These reactions were catalyzed by a total number of 13 enzymes. We extracted some of the essential reactions in the pathway using PN model, which are the possible drug targets without which the pathway cannot function. This model also helps to improve the understanding of the biological processes within this pathway

Inferring cellular networks – a review

In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations

Modeling of the Glycolysis Pathway in Plasmodium falciparum using Petri Nets

Malaria is one of the deadly diseases, which affects a large number of the world’s population. The Plasmodium falciparum parasite during erythrocyte stages produces its energy mainly through anaerobic glycolysis, with pyruvate being converted into lactate. The glycolysis metabolism in P. falci-parum is one of the important metabolic pathways of the parasite because the parasite is entirely dependent on it for energy. Also, several glycolytic enzymes have been proposed as drug targets. Petri nets (PNs) have been recognized as one of the important models for representing biological pathways. In this work, we built a qualitative PN model for the glycolysis pathway in P. falciparum and analyzed the model for its structural and quantitative properties using PN theory. From PlasmoCyc files, a total of 11 reactions were extracted; 6 of these were reversible and 5 were irreversible. These reactions were catalyzed by a total number of 13 enzymes. We extracted some of the essential reactions in the pathway using PN model, which are the possible drug targets without which the pathway cannot function. This model also helps to improve the understanding of the biological processes within this pathway

Petri nets for modelling metabolic pathways: a survey

In the last 15 years, several research efforts have been directed towards the representation and the analysis of metabolic pathways by using Petri nets. The goal of this paper is twofold. First, we discuss how the knowledge about metabolic pathways can be represented with Petri nets. We point out the main problems that arise in the construction of a Petri net model of a metabolic pathway and we outline some solutions proposed in the literature. Second, we present a comprehensive review of recent research on this topic, in order to assess the maturity of the field and the availability of a methodology for modelling a metabolic pathway by a corresponding Petri net

Modeling of dynamic systems with Petri nets and fuzzy logic

Aktuelle Methoden zur dynamischen Modellierung von biologischen Systemen sind für Benutzer ohne mathematische Ausbildung oft wenig verständlich. Des Weiteren fehlen sehr oft genaue Daten und detailliertes Wissen über Konzentrationen, Reaktionskinetiken oder regulatorische Effekte. Daher erfordert eine computergestützte Modellierung eines biologischen Systems, mit Unsicherheiten und grober Information umzugehen, die durch qualitatives Wissen und natürlichsprachliche Beschreibungen zur Verfügung gestellt wird. Der Autor schlägt einen neuen Ansatz vor, mit dem solche Beschränkungen überwunden werden können. Dazu wird eine Petri-Netz-basierte graphische Darstellung von Systemen mit einer leistungsstarken und dennoch intuitiven Fuzzy-Logik-basierten Modellierung verknüpft. Der Petri Netz und Fuzzy Logik (PNFL) Ansatz erlaubt eine natürlichsprachlich-basierte Beschreibung von biologischen Entitäten sowie eine Wenn-Dann-Regel-basierte Definition von Reaktionen. Beides kann einfach und direkt aus qualitativem Wissen abgeleitet werden. PNFL verbindet damit qualitatives Wissen und quantitative Modellierung.Current approaches in dynamic modeling of biological systems often lack comprehensibility,n especially for users without mathematical background. Additionally, exact data or detailed knowledge about concentrations, reaction kinetics or regulatory effects is missing. Thus, computational modeling of a biological system requires dealing with uncertainty and rough information provided by qualitative knowledge and linguistic descriptions. The author proposes a new approach to overcome such limitations by combining the graphical representation provided by Petri nets with the modeling of dynamics by powerful yet intuitive fuzzy logic based systems. The Petri net and fuzzy logic (PNFL) approach allows natural language based descriptions of biological entities as well as if-then rule based definitions of reactions, both of which can be easily and directly derived from qualitative knowledge. PNFL bridges the gap between qualitative knowledge and quantitative modeling

Computational Modeling of IP3 Receptor Function and Intracellular Mechanisms in Synaptic Plasticity

Learning and memory in the brain have been shown to involve complex molecular interactions. In the field of computational neuroscience, mathematical modeling and computer simulations are combined with laboratory experiments to better understand the dynamics of these interactions. A vast number of computational models related to intracellular molecular mechanisms calls for means to compare them to each other. In this thesis, computational models and methods for understanding specific molecular mechanisms in synaptic plasticity, a phenomenon involved in learning, are studied and compared both quantitatively and qualitatively. The focus is set on the IP3 receptor kinetics and the intracellular molecular mechanisms including processing of calcium ions in the postsynaptic neuron. Calcium has been shown to play an important role in different types of synaptic plasticity, only the mechanisms and dynamics for elevation of cytosolic calcium concentration vary. The IP3 receptor, an intracellular calcium releasing channel, is one of the major factors responsible for the calcium elevation in neurons. Firstly, the applicability of deterministic and stochastic approaches in modeling the IP3 receptor kinetics, involving small number of molecules, is studied. In this case, the study shows that stochastic approach, especially Gillespie stochastic simulation algorithm, should be favored. Secondly, since a well-established model for IP3 receptor function in neurons is lacking, this thesis provides not only tools for model comparison but also an insight to which model of the tens of models to choose. Using stochastic simulations, four IP3 models are compared to experimental data to clarify how well they model the measured features in neurons. The results show that there are major differences in the statistical properties of the IP3 receptor models although the models have originally been developed to describe the same phenomenon. Thirdly, this study shows that the models for postsynaptic signaling in synaptic plasticity are becoming more sophisticated by involving stochastic properties, incorporating electrophysiolocial properties of the entire neuron, or having diffusion of signaling molecules. Computational comparison of these models reveals that when using the same input, models describing the phenomenon in the same neuron type produce different results. One of the future goals of computational neuroscience is to find predictive computational models for biochemical and biophysical mechanisms of synaptic plasticity in different brain areas and cells of mammals. When describing a system of molecular events, the selection of modeling and simulation approach should be done carefully by keeping the properties of the modeled biological system in mind. Not only do theoreticians and modelers need to consider experimental findings, but the experimental progress could also be enhanced by using simulations to select the most promising experiments. As discussed in this thesis, attention paid to these issues should improve the utility of modeling approaches for investigating molecular mechanisms of synaptic plasticity. Only then is it possible to use the models to learn something new about the mammalian brain function

Struktur, Funktion und Verhalten dynamischer Modelle der Systembiologie: formale Wissensrepräsentation als Grundlage für computergestützte Modellierung und Simulation

Das Verstehen komplexer biologischer Phänomene auf der Basis dynamischer Modelle ist ohne Computerunterstützung nicht möglich. Seit mehr als einem Jahrzehnt werden enorme Anstrengungen unternommen, eine Softwareinfrastruktur für die systembiologische Modellierung und Simulation zu entwickeln. Die vorliegende Arbeit bietet den dafür notwendigen Standards, formalen Sprachen, Ontologien und auf diesen operierenden Programmen ein wissenschaftstheoretisches Fundament in Gestalt der sogenannten Wissensfacetten von Bio-Modellen. Die Wissensfacetten von Bio-Modellen stellen ein konzeptuelles Schema zur systematischen Beschreibung von Bio-Modellen, ihrer Verwendung und ihres Verhaltens dar. Dabei muss eine vollständige Beschreibung eines Bio-Modells seine Struktur, seine Funktion und sein Verhalten umfassen und seine intrinsische formale Semantik mit der extrinsischen biologischen Wirklichkeit in Beziehung setzen. Die formale Repräsentation der Wissensfacetten von Bio-Modellen bietet eine Grundlage für eine umfassende Computerunterstützung der Modellierung und Simulation in der Systembiologie. Geleitet durch die Wissensfacetten werden existierende Ansätze für die formale Repräsentation relevanter Wissensfragmente eingeordnet und bewertet sowie Defizite an Beschreibungsmitteln identifiziert. Ein großes Defizit liegt im Fehlen formalsprachlicher Mittel zur qualitativen Beschreibung des Verhaltens von Bio-Modellen. Im Rahmen dieser Arbeit wurde die Ontologie TEDDY entwickelt, auf deren Grundlage sich das Verhalten von Bio-Modellen propositional formalisieren lässt. TEDDY stellt eine formale begriffliche Basis der Theorie dynamischer Systeme dar. Die drei Hauptresultate der vorliegenden Arbeit sind eingebettet in grundsätzliche Betrachtungen zur Modellierung und Simulation in der Systembiologie und basieren auf allgemeinen Begriffen und Verfahren der Wissensrepräsentation sowie speziellen Methoden der qualitativen Beschreibung von Dynamik