'BioNessie(G) - a grid enabled biochemical networks simulation environment

The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations

BioNessie - a grid enabled biochemical networks simulation environment

The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations

Speeding up systems biology simulations of biochemical pathways using condor

This is the accepted version of the following article: Speeding up Systems Biology Simulations of Biochemical Pathways using Condor". Concurrency and Computation: Practice and Experience Volume 26, Issue 17, pages 2727–2742, 10 December 2014 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/cpe.3161/abstractSystems biology is a scientific field that uses computational modelling to study biological and biochemical systems. The simulation and analysis of models of these systems typically explore behaviour over a wide range of parameter values; as such, they are usually characterised by the need for nontrivial amounts of computing power. Grid computing provides access to such computational resources. In previous research, we created the grid-enabled biochemical networks simulation environment to attempt to speed up system biology simulations over a grid (the UK National Grid Service and ScotGrid). Following on from this work, we have created the simulation modelling of the epidermal growth factor receptor microtubule-associated protein kinase pathway utility, a standalone simulation tool dedicated to the modelling and analysis of the epidermal growth factor receptor microtubule-associated protein kinase pathway. This builds on experiences from biochemical networks simulation environment by decoupling the simulation modelling elements from the Grid middleware. This new utility enables us to interface with different grid technologies. This paper therefore describes the new SIMAP utility and an empirical investigation of its performance when deployed over a desktop grid based on the high throughput computing middleware Condor. We present our results based on a case study with a model of the mammalian ErbB signalling pathway, a pathway strongly linked to cance

Grid-enabled SIMAP utility: Motivation, integration technology and performance results

A biological system comprises large numbers of functionally diverse and frequently multifunctional sets of elements that interact selectively and nonlinearly to produce coherent behaviours. Such a system can be anything from an intracellular biological process (such as a biochemical reaction cycle, gene regulatory network or signal transduction pathway) to a cell, tissue, entire organism, or even an ecological web. Biochemical systems are responsible for processing environmental signals, inducing the appropriate cellular responses and sequence of internal events. However, such systems are not fully or even poorly understood. Systems biology is a scientific field that is concerned with the systematic study of biological and biochemical systems in terms of complex interactions rather than their individual molecular components. At the core of systems biology is computational modelling (also called mathematical modelling), which is the process of constructing and simulating an abstract model of a biological system for subsequent analysis. This methodology can be used to test hypotheses via insilico experiments, providing predictions that can be tested by in-vitro and in-vivo studies. For example, the ERbB1-4 receptor tyrosine kinases (RTKs) and the signalling pathways they activate, govern most core cellular processes such as cell division, motility and survival (Citri and Yarden, 2006) and are strongly linked to cancer when they malfunction due to mutations etc. An ODE (ordinary differential equation)-based mass action ErbB model has been constructed and analysed by Chen et al. (2009) in order to depict what roles of each protein plays and ascertain to how sets of proteins coordinate with each other to perform distinct physiological functions. The model comprises 499 species (molecules), 201 parameters and 828 reactions. These in silico experiments can often be computationally very expensive, e.g. when multiple biochemical factors are being considered or a variety of complex networks are being simulated simultaneously. Due to the size and complexity of the models and the requirement to perform comprehensive experiments it is often necessary to use high-performance computing (HPC) to keep the experimental time within tractable bounds. Based on this as part of an EC funded cancer research project, we have developed the SIMAP Utility that allows the SImulation modeling of the MAP kinase pathway (http://www.simap-project.org). In this paper we present experiences with Grid-enabling SIMAP using Condor

A systems biology approach to multi-scale modelling and analysis of planar cell polarity in drosophila melanogaster wing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Systems biology aims to describe and understand biology at a global scale where biological systems function as a result of complex mechanisms that happen at several scales. Modelling and simulation are computational tools that are invaluable for description, understanding and prediction these mechanisms in a quantitative and integrative way. Thus multi-scale methods that couple the design, simulation and analysis of models spanning several spatial and temporal scales is becoming a new emerging focus of systems biology. This thesis uses an exemplar – Planar cell polarity (PCP) signalling – to illustrate a generic approach to model biological systems at different spatial scales, using the new concept of Hierarchically Coloured Petri Nets (HCPN). PCP signalling refers to the coordinated polarisation of cells within the plane of various epithelial tissues to generate sub-cellular asymmetry along an axis orthogonal to their apical-basal axes. This polarisation is required for many developmental events in both vertebrates and non-vertebrates. Defects in PCP in vertebrates are responsible for developmental abnormalities in multiple tissues including the neural tube, the kidney and the inner ear. In Drosophila wing, PCP is seen in the parallel orientation of hairs that protrude from each of the approximately 30,000 epithelial cells to robustly point toward the wing tip. This work applies HCPN to model a tissue comprising multiple cells hexagonally packed in a honeycomb formation in order to describe the phenomenon of Planar Cell Polarity (PCP) in Drosophila wing. HCPN facilitate the construction of mathematically tractable, compact and parameterised large-scale models. Different levels of abstraction that can be used in order to simplify such a complex system are first illustrated. The PCP system is first represented at an abstract level without modelling details of the cell. Each cell is then sub-divided into seven virtual compartments with adjacent cells being coupled via the formation of intercellular complexes. A more detailed model is later developed, describing the intra- and inter-cellular signalling mechanisms involved in PCP signalling. The initial model is for a wild-type organism, and then a family of related models, permitting different hypotheses to be explored regarding the mechanisms underlying PCP, are constructed. Among them, the largest model consists of 800 cells which when unfolded yields 164,000 places (each of which is described by an ordinary differential equation). This thesis illustrates the power and validity of the approach by showing how the models can be easily adapted to describe well-documented genetic mutations in the Drosophila wing using the proposed approach including clustering and model checking over time series of primary and secondary data, which can be employed to analyse and check such multi-scale models similar to the case of PCP. The HCPN models support the interpretation of biological observations reported in literature and are able to make sensible predictions. As HCPN model multi-scale systems in a compact, parameterised and scalable way, this modelling approach can be applied to other large-scale or multi-scale systems.This study was funded by Brunel University

A generic approach to behaviour-driven biochemical model construction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Modelling of biochemical systems has received considerable attention over the last decade from bioengineering, biochemistry, computer science, and mathematics. This thesis investigates the applications of computational techniques to computational systems biology, for the construction of biochemical models in terms of topology and kinetic rates. Due to the complexity of biochemical systems, it is natural to construct models representing the biochemical systems incrementally in a piecewise manner. Syntax and semantics of two patterns are defined for the instantiation of components which are extendable, reusable and fundamental building blocks for models composition. We propose and implement a set of genetic operators and composition rules to tackle issues of piecewise composing models from scratch. Quantitative Petri nets are evolved by the genetic operators, and evolutionary process of modelling are guided by the composition rules. Metaheuristic algorithms are widely applied in BioModel Engineering to support intelligent and heuristic analysis of biochemical systems in terms of structure and kinetic rates. We illustrate parameters of biochemical models based on Biochemical Systems Theory, and then the topology and kinetic rates of the models are manipulated by employing evolution strategy and simulated annealing respectively. A new hybrid modelling framework is proposed and implemented for the models construction. Two heuristic algorithms are performed on two embedded layers in the hybrid framework: an outer layer for topology mutation and an inner layer for rates optimization. Moreover, variants of the hybrid piecewise modelling framework are investigated. Regarding flexibility of these variants, various combinations of evolutionary operators, evaluation criteria and design principles can be taken into account. We examine performance of five sets of the variants on specific aspects of modelling. The comparison of variants is not to explicitly show that one variant clearly outperforms the others, but it provides an indication of considering important features for various aspects of the modelling. Because of the very heavy computational demands, the process of modelling is paralleled by employing a grid environment, GridGain. Application of the GridGain and heuristic algorithms to analyze biological processes can support modelling of biochemical systems in a computational manner, which can also benefit mathematical modelling in computer science and bioengineering. We apply our proposed modelling framework to model biochemical systems in a hybrid piecewise manner. Modelling variants of the framework are comparatively studied on specific aims of modelling. Simulation results show that our modelling framework can compose synthetic models exhibiting similar species behaviour, generate models with alternative topologies and obtain general knowledge about key modelling features