Peter Saffrey

Computational challenges of systems biology

Progress in the study of biological systems such as the heart, brain, and liver will require computer scientists to work closely with life scientists and mathematicians. Computer science will play a key role in shaping the new discipline of systems biology and addressing the significant computational challenges it poses.

Addressing the challenges of multiscale model management in systems biology

Mathematical and computational modelling are emerging as important techniques for studying the behaviour of complex biological systems. We argue that two advances are necessary to properly leverage these techniques: firstly, the ability to integrate models developed and executed on separate tools, without the need for substantial translation and secondly, a comprehensive system for storing and man-ageing not only the models themselves but also the parameters and tools used to execute those models and the results they produce. A framework for modelling with these features is described here. We have developed of a suite of XML-based services used for the storing and analysis of models, model parameters and results, and tools for model integration. We present these here, and evaluate their effectiveness using a worked example based on part of the hepatocyte glycogenolysis system.

A composite computational model of liver glucose homeostasis. I. Building the composite model

A computational model of the glucagon/insulin-driven liver glucohomeostasis function, focusing on the buffering of glucose into glycogen, has been developed. The model exemplifies an ‘engineering’ approach to modelling in systems biology, and was produced by linking together seven component models of separate aspects of the physiology. The component models use a variety of modelling paradigms and degrees of simplification. Model parameters were determined by an iterative hybrid of fitting to high-scale physiological data, and determination from small-scale in vitro experiments or molecular biological techniques. The component models were not originally designed for inclusion within such a composite model, but were integrated, with modification, using our published modelling software and computational frameworks. This approach facilitates the development of large and complex composite models, although, inevitably, some compromises must be made when composing the individual models. Composite models of this form have not previously been demonstrated.

A composite computational model of liver glucose homeostasis. II. Exploring system behaviour

Using a composite model of the glucose homeostasis system, consisting of seven interconnected submodels, we enumerate the possible behaviours of the model in response to variation of liver insulin sensitivity and dietary glucose variability. The model can reproduce published experimental manipulations of the glucose homeostasis system and clearly illustrates several important properties of glucose homeostasis—boundedness in model parameters of the region of efficient homeostasis, existence of an insulin sensitivity that allows effective homeostatic control and the importance of transient and oscillatory behaviour in characterizing homeostatic failure. Bifurcation analysis shows that the appearance of a stable limit cycle can be identified.

End-to-end information management for systems biology

Mathematical and computational modelling are research areas with increasing importance in the study of behaviour in complex biological systems. With the increasing breadth and depth of models under consideration, a disciplined approach to managing the diverse data associated with these models is needed. Of particular importance is the issue of provenance, where a model result is linked to information about the generating model, the parameters used in that model and the papers and experiments that were used to derive those parameters. This paper presents an architecture to manage this information along with accompanying tool support and examples of the management system in use at various points in the development of a large model.

A specification language and a framework for the execution of composite models in systems biology

When modelling complex biological systems it is often desirable to combine a number of distinct sub-models to form a larger composite model. We describe an XML based language that can be used to specify composite models and a lightweight computational framework that executes these models. The language supports specification of structure and implementation details for composite models, along with the interfaces provided by each sub-model. The framework executes each sub-model in its native environment, allowing extensive reuse of existing models. It uses mathematical and computational connectors and translators to unify the models computationally. Unlike other suggested approaches for model integration, our approach does not impose one modeling scheme, composition algorithm or underlying middleware framework. We demonstrate our approach by constructing a composite model describing part of the glucose homeostasis system.

Optimising Communication Structure for Model Checking

Model checking is an effective tool in the verification of concurrent systems but can require skillful use. The choice of representation for a particular system can make a substantial difference to whether the verification will prove tractable. We present a method for improving the choice of representation by effective use of communication structure. The main contribution is a technique for selecting a communication structure which yields a reduced search space whilst preserving the essential behaviour of a representation. We illustrate our method with examples based on the model-checker Spin.