This paper reviews a variety of different graphical notations currently in active use for modelling dynamic processes in bioinformatics and biotechnology, and crystallises from these notations a set of properties essential to any proposal for a modelling language seeking to provide an adequate systemic description of biological processes.

A central aim of systems biology is to elucidate the complex dynamic structure of biological systems within which functioning and control occur. The success of this endeavour requires a dialogue between the two quite distinct disciplines of life science and systems theory, and so drives the need for graphical notations which facilitate this dialogue. Several methods have been developed for modelling and simulating biochemical networks, some of which provide notations for graphicall4y constructing a model. Such notations must support the full panoply of mechanisms of systems biology, including metabolic, regulatory, signalling and transport processes. Notations in systems biology tend to fall into two groups. The first group derives its orientation from conventional biochemical pathway diagrams, and so tends to ignore the role of information processing. The second group focuses on the processing of information, incorporating information-processing ideas from other systems-oriented disciplines, such as engineering and business. This, however, can lead to the two crucial and related difficulties of impedance mismatch and conceptual baggage. Impedance mismatch concerns the rift between non-biological notations and biological reality, which forces the researcher to employ awkward workarounds when modelling uniquely biological mechanisms. Conceptual baggage can arise when, for instance, an engineering notation is adapted to cater for these distinctively biological needs, since these adaptations will, typically, never completely free the notation of the conceptual structure of its original engineering motivation. A novel formalism, codependence modelling, which seeks to combine the needs of the biologist with the mathematical rigour required to support computer simulation of dynamics is proposed here. The notion of codependence encompasses the transformation of both chemical substance and information, thus integrating both metabolic and gene regulatory processes within a single conceptual schema.

Living organisms regulate the expression of genes using complex interactions of transcription factors, messenger RNA and active protein products. Due to their complexity, gene-regulatory networks are not fully understood, however, various modeling approaches can be used to gain insight into their function and operation. This paper describes an ongoing study to use evolutionary algorithms to create computational models of gene-regulatory networks based on observed microarray data. Because of the computational requirements of this approach (which requires the discovery ofgene network topologies), it is critical that it is implemented on a computing platform capable of delivering significant compute power. We discuss how this can be achieved using distributed and grid computing technology. In particular we investigate how Condor and JavaSpaces technology is suited to the requirements of our modeling approach.

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.

Determining network models of gene-regulatory networks using evolutionary algorithms not only requires considerable computational power, but also a modeling formalism that can explain the underlying dynamics.