Modeling and Analyzing Cyber-Physical Systems Using Hybrid Predicate Transition Nets

Cyber-Physical Systems (CPSs) are software controlled physical devices that are being used everywhere from utility features in household devices to safety-critical features in cars, trains, aircraft, robots, smart healthcare devices. CPSs have complex hybrid behaviors combining discrete states and continuous states capturing physical laws. Developing reliable CPSs are extremely difficult. Formal modeling methods are especially useful for abstracting and understanding complex systems and detecting and preventing early system design problems. To ensure the dependability of formal models, various analysis techniques, including simulation and reachability analysis, have been proposed in recent decades. This thesis aims to provide a unified formal modeling and analysis methodology for studying CPSs. Firstly, this thesis contributes to the modeling and analysis of discrete, continuous, and hybrid systems. This work enhances modeling of discrete systems using predicate transition nets (PrTNs) by fully realizing the underlying specification through incorporating the first-order logic with set theory, improving the type system, and providing incremental model composition. This work enhances the technique of analyzing discrete systems using PrTN by improving the simulation algorithm and its efficient implementation. This work also improves the analysis of discrete systems using SPIN by providing a more accurate and complete translation method. Secondly, this work contributes to the modeling and analysis of hybrid systems by proposing an extension of PrTNs, hybrid predicate transition nets (HPrTNs). The proposed method incorporates a novel concept of token evolution, which nicely addresses the continuous state evolution and the conflicts present in other related works. This work presents a powerful simulation capability that can handle linear, non-linear dynamics, transcendental functions through differential equations. This work also provides a complementary technique for reachability analysis through the translation of HPrTN models for analysis using SpaceEx

Analysis of signalling pathways using continuous time Markov chains

We describe a quantitative modelling and analysis approach for signal transduction networks. We illustrate the approach with an example, the RKIP inhibited ERK pathway [CSK+03]. Our models are high level descriptions of continuous time Markov chains: proteins are modelled by synchronous processes and reactions by transitions. Concentrations are modelled by discrete, abstract quantities. The main advantage of our approach is that using a (continuous time) stochastic logic and the PRISM model checker, we can perform quantitative analysis such as what is the probability that if a concentration reaches a certain level, it will remain at that level thereafter? or how does varying a given reaction rate affect that probability? We also perform standard simulations and compare our results with a traditional ordinary differential equation model. An interesting result is that for the example pathway, only a small number of discrete data values is required to render the simulations practically indistinguishable

Computational Modeling, Formal Analysis, and Tools for Systems Biology.

As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science

Process Calculi Abstractions for Biology

Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri Nets theory, and some other by stochastic processes. A most recent approach consists in interpreting the living entities as terms of process calculi where the behavior of the represented systems can be inferred by applying syntax-driven rules. A comprehensive picture of the state of the art of the process calculi approach to biological modeling is still missing. This paper goes in the direction of providing such a picture by presenting a comparative survey of the process calculi that have been used and proposed to describe the behavior of living entities. This is the preliminary version of a paper that was published in Algorithmic Bioprocesses. The original publication is available at http://www.springer.com/computer/foundations/book/978-3-540-88868-

Modeling biochemical transformation processes and information processing with Narrator

BACKGROUND: Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. RESULTS: Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. CONCLUSION: Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from

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

Incremental and unifying modelling formalism for biological interaction networks

International audienc

Systems Biology of Cancer: A Challenging Expedition for Clinical and Quantitative Biologists

A systems-biology approach to complex disease (such as cancer) is now complementing traditional experience-based approaches, which have typically been invasive and expensive. The rapid progress in biomedical knowledge is enabling the targeting of disease with therapies that are precise, proactive, preventive, and personalized. In this paper, we summarize and classify models of systems biology and model checking tools, which have been used to great success in computational biology and related fields. We demonstrate how these models and tools have been used to study some of the twelve biochemical pathways implicated in but not unique to pancreatic cancer, and conclude that the resulting mechanistic models will need to be further enhanced by various abstraction techniques to interpret phenomenological models of cancer progression

Biological knowledge management and gene network analysis: a heuristic road to System Biology

In order to understand the molecular basis of living cells and organisms, biologists over the past decades have been studying life's core molecular players: the genes. Most genes have a specific function, a role they play in the collective task of developing a cell and supporting all the aspects of keeping it alive. These genes do not perform their function randomly. Instead, after billions of years of evolution, nature's trial-and-error process, they have become parts of an utterly complex and intricate network, an interconnected mesh of genes that comprises signal detection cascades, enzymatic reactions, control mechanisms, etc. Over several past decades, experimental molecular biologists have sought mainly to study these genes via a one-by-one approach. However, with the advent of high-throughput experimental techniques, the number-crunching power of computers, and the realisation that many biological functions are the result of interactions between genes or their proteins, Biology's related field of Systems Biology has emerged. Here, one tries to combine the dispersed information produced by many researchers, in integrated assemblies called gene networks. Our research comprises the development of two new methods for improved information integration in the field of molecular Systems Biology. The first one aims to support an approach to acquire insights in the dynamics of gene networks (the behaviour of gene activities over time), called 'modelling and simulation' of genetic regulatory networks. Our second new method approaches the problem of how to collect and manage the information necessary to compose such genetic networks in the first place, based on scattered information in a dispersed and increasingly fast growing body of publications. These two methods form two separate parts in this thesis (chapters 2-4, and chapters 5-7). Chapter 1, section 1.3 provides an introductory, complete overview of this thesis. It is intended as a light introduction to my doctoral research, presented in an informal and entertaining way, and mainly addressed to my friends and family. It forms an introduction for the laymen to our work and the concepts that are important for this thesis. Chapters 2, 3 and 4 constitute Part 1 of this thesis. Chapter 2 gives a review of the various formalisms for modelling and simulation of gene networks, as a thorough background for our work presented in the following chapter. Chapter 3 describes SIM-plex, our new software tool that forms a bridge between a mathematical gene network modelling formalism, and the biologist, who usually is more an expert in the biology behind the gene network than a mathematician can ever be. It shields off the mathematics in a new way so as to enable biologists to experiment with modelling and simulation themselves. Chapter 4 describes the various applications that SIM-plex was used for. The research described in Part 2 of this thesis, chapters 5, 6 and 7, emerged from our own need for a better management of biological information. We experienced this necessity while we were building a larger genetic network for the Arabidopsis cell cycle, and it forms a general problem in biology. Chapter 5 gives a background of the currently existing methods for harvesting literature information, but comes to the conclusion that no existing automated or manual method displays sufficient potential to capture the largest part of information from literature in a structured way. In chapter 6, we describe our bold proposal of a new method to tackle this problem: MineMap, a community-based manual text-curation initiative. We describe the various aspects required to make such a project possible, based on our own experiences with our prototype application MineMap. This research is organised in a 'heuristic' way, in the sense that we built a first sketch and a working solution that also generated experiences for improvements in a next design. While chapter 6 describes our new ideas and concrete implementations in considerable detail, chapter 7 then illustrates the core concept behind MineMap