Foreword

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Performance evaluation of component-based software systems: A survey

We examine product form equilibrium distributions for stochastic process algebra models, finding conditions that guarantee the existence of a solution for the traffic equations. These equations are a set of linear equations, which are the basis of exact analysis of product form models like queueing networks, stochastic Petri nets, as well as stochastic process algebras. This paper illustrates how the equilibrium distribution can be expressed as a product for a certain class of stochastic process algebra models that satisfy certain conditions. Although the product from criterion derived in this paper is developed in the context of Performance Evaluation Process Algebra (PEPA), the results can be easily generalised to any of the other stochastic process algebra.

Models and computational predictions are useful in identifying certain key parameters that play a central role in defining the overall behavior of the system, and thus lead to new and more informative experiments. In this thesis, in-silico models are developed over a range of individual biological scales (macroscopic, mesoscopic and microscopic) for a range of cellular phenomena (cellular interactions, migration and signalling pathways) in order to highlight the importance of combined in-vitro – in-silico investigations. It is widely accepted that Systems Biology aims to provide a simpler and more abstract framework to explain complex biological phenomena. However, integration of these models with experimental data is often underutilised. Incorporation of experimentally derived data sets into the mathematical framework of in-silico modelling results in reliable, well parameterised systems capable of replicating dynamical properties of the biological systems. Work in this thesis includes the development of a continuous macroscopic in-silico model to identify the key mechanisms of interaction between cells present within the gastric tumour microenvironment. This model of discovery is used in a predictive capacity to accept or reject hypotheses. Next, the construction of a discrete cell based model of fibroblast migration is used to determine the degree of bias fibroblast cells experience when migrating over different surface topologies. The key results from this model show that particular surface topographies can have an effect on migratory cell behaviour. Then, the parameterisation of a differential equation model is used to quantify the key mechanisms of Nrf2 regulation in the cytoplasm and nucleus. Validation with experimentally derived datasets results in the quantification of rate ratios important to the dynamics of this signalling pathway. Finally, a stochastic Petri-net model capable of simulating the dynamical behaviour of functional cross-talk between the Nrf2 and NF-κB pathways is developed. This approach allows for the evaluation of a wide array of network responses, without the need for computationally expensive parameterisation. Together, these models exhibit how integration of in-silico models with in-vitro datasets can be used to generate new knowledge, or testable hypotheses.

Stochastic Petri Nets and Stochastic Process Algebras can both be used for the qualitative and quantitative analysis of parallel and distributed systems. However, sometimes the complexity of the system can make such analysis infeasible, due to the large size of the state space underlying the model. To overcome this problem different aggregation techniques have been proposed in both formalisms. Two of them are compared in this paper.

In his famous essay, Lazebnik [1] discussed whether a biologist can fix a broken radio and suggested that applying the ‘biologist approach’ to a broken radio will likely not lead to a successful end, while any engineer or even a trained repairman evidently could fix the radio. In biology, the study of molecules one by one is fine for gathering information on how molecules work, but deciphering biological mechanisms, or the function of biological systems, is a different story. One difference between an engineer and a biologist, according to Lazebnik, lies in the language: in biology, language is oftentimes vague and nonquantitative which limits the possibility of making predictions [1]. In systems biology, a quantitative and predictive language is adopted to describe biological knowledge in order to understand how molecules act together within the network of interactions that makes up a living system. Systems biology, in other words, is the holistic analysis of complex systems. Indeed, systems biology can be defined as the computational and mathematical modeling of complex biological systems, in contrast to the traditional, reductionist approach to biology. Common technology platforms that are deployed to obtain complex datasets are transcriptomics, proteomics, metabolomics and epigenomics. However, systems biology should not be seen merely as the generation of lists of genes, proteins or metabolites using such omics platforms; the objective is to exploit these data and to develop quantitative models that describe the biological system and its response to individual perturbations (see [2] for an excellent introduction to this subject). Furthermore, systems biology can be seen as an iterative interplay between discoveryand hypothesis-driven science: “global observations (discoveries) are matched against model predictions (hypotheses) in an iterative manner, leading to the formation of new models, new predictions, and new experiments to test them” [2]. Systems toxicology, in turn, has been described as “the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization”, and it has been suggested that its application could be part of a new and improved risk assessment [3]. This is nothing less than a paradigm shift in the way that toxicology is conducted. Importantly, systems toxicology – and systems biology in general – relies heavily on computational approaches to manage, analyze and interpret the data generated by large-scale experiments, and computer databases are thus an integral feature of this approach [2]. To this end, commonly accepted repositories and software environments are crucially important [4]. This does not mean that the introduction of systems toxicology approaches is breaking the rice bowl of the classically trained toxicologist. Instead, the ‘new’ toxicology is Systems biology in nanosafety research

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.

Editorial overview: Systems biology for biotechnology

Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.

Paving the way for synthetic biology‐based bioremediation in Europe

Computer simulation is becoming increasingly popular among computer network researchers for performance modeling and evaluation of computer and telecommunication networks. This popularity is due to the availability of various sophisticated and powerful simulators, and also because of the flexibility in model construction and validation offered by simulation. While various network simulators (both open source and commercial) exist for modeling and performance evaluation of communication networks, OPNET is becoming popular network simulator as the package is available to academic institutions at no cost, especially OPNET IT Guru. This chapter aims to provide a tutorial on OPNET focusing on the simulation and performance modeling of IEEE 802.11 wireless local area networks (WLANs). Results obtained show that OPNET provides credible simulation results close to a real system.

We define a stochastic Petri net (SPN) semantics for stochastic process algebras (SPAs), a recently introduced formalism that offers a novel approach for performance modeling. The proposed semantics is evaluated in terms of three criteria: concurrency and retrievability, as defined by Olderog (1987, 1991) for untimed net semantics, and Markov equivalence for the stochastic aspects.

A software toolset which allows unified modelling language (UML) modellers to annotate models with performance information is described. An equivalent performance model is extracted from the UML, solved, and the results reflected back to the UML level. Used in this way, the toolset gives a high-level approach to software performance modelling where the benefits of the performance modelling process are achieved without significant additional notational burden.

Weather forecasting provides an important information for general public. Analysis of weather forecasting models, through traditional simulation techniques, are not exhaustive and thus not accurate. Probabilistic model checking, a formal methods technique, as a complementary approach provides an accurate analysis for probabilistic models. In this paper, we have analyzed a simple probabilistic weather forecasting model for Islamabad weather using PRISM model checker. The reach ability and prediction capability of model has been analyzed using quantitative and qualitative properties in PRISM. Analysis shows that all the states of weather forecasting model are reachable and model is biased. In addition, we have also implemented this model in MATLAB and compared the results of both implementation, quantitatively. We have also presented a comparison between PRISM and MATLAB implementation of weather forecasting model, qualitatively.

The promise of the current era of biological research is to understand the integrated function of biological systems in order to predict and rationally manipulate their behavior, with the ultimate aim of improving human health. In a nutshell, “systems biology” attempts to realize this promise by