A Calculus of Chemical Systems

In recent years various calculi have been proposed for modelling biological systems, typically intracellular pathways. These calculi generally fall into one of two camps: ones based on process calculi, such as Milner’s pi-calculus [24], and rule-based ones. Examples of the former include [31, 32, 30]; examples of the latter include BIOCHAM, κ, BioNet

Programming with models: modularity and abstraction provide powerful capabilities for systems biology

Mathematical models are increasingly used to understand how phenotypes emerge from systems of molecular interactions. However, their current construction as monolithic sets of equations presents a fundamental barrier to progress. Overcoming this requires modularity, enabling sub-systems to be specified independently and combined incrementally, and abstraction, enabling generic properties of biological processes to be specified independently of specific instances. These, in turn, require models to be represented as programs rather than as datatypes. Programmable modularity and abstraction enables libraries of modules to be created, which can be instantiated and reused repeatedly in different contexts with different components. We have developed a computational infrastructure that accomplishes this. We show here why such capabilities are needed, what is required to implement them and what can be accomplished with them that could not be done previously

Reconstructing metabolic networks using interval analysis

Recently, there has been growing interest in the modelling and simulation of biological systems. Such systems are often modelled in terms of coupled ordinary differential equations that involve parameters whose (often unknown) values correspond to certain fundamental properties of the system. For example, in metabolic modelling, concentrations of metabolites can be described by such equations, where parameters correspond to the kinetic rates of the underlying chemical reactions. Within this framework, the increasing availability of time series data opens up the attractive possibility of reconstructing approximate parameter values, thus enabling the in silico exploration of the behaviour of complex dynamical systems. The parameter reconstruction problem, however, is very challenging – a fact that has resulted in a plethora of heuristics methods designed to fit parameters to the given data. In this paper we propose a completely deterministic method for parameter reconstruction that is based on interval analysis. We illustrate its utility by applying it to reconstruct metabolic networks using S-systems. Our method not only estimates the parameters very precisely, it also determines the appropriate network topologies. A major strength of the proposed method is that it proves that large portions of parameter space can be disregarded, thereby avoiding spurious solutions