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BackgroundA gene-regulatory network (GRN) refers to DNA segments that interact through their RNA and protein products and thereby govern the rates at which genes are transcribed. Creating accurate dynamic models of GRNs is gaining importance in biomedical research and development. To improve our understanding of continuous deterministic modeling methods employed to construct dynamic GRN models, we have carried out a comprehensive comparative study of three commonly used systems of ordinary differential equations: The S-system (SS), artificial neural networks (ANNs), and the general rate law of transcription (GRLOT) method. These were thoroughly evaluated in terms of their ability to replicate the reference models' regulatory structure and dynamic gene expression behavior under varying conditions.ResultsWhile the ANN and GRLOT methods appeared to produce robust models even when the model parameters deviated considerably from those of the reference models, SS-based models exhibited a notable loss of performance even when the parameters of the reverse-engineered models corresponded closely to those of the reference models: this is due to the high number of power terms in the SS-method, and the manner in which they are combined. In cross-method reverse-engineering experiments the different characteristics, biases and idiosynchracies of the methods were revealed. Based on limited training data, with only one experimental condition, all methods produced dynamic models that were able to reproduce the training data accurately. However, an accurate reproduction of regulatory network features was only possible with training data originating from multiple experiments under varying conditions.ConclusionsThe studied GRN modeling methods produced dynamic GRN models exhibiting marked differences in their ability to replicate the reference models' structure and behavior. Our results suggest that care should be taking when a method is chosen for a particular application. In particular, reliance on only a single method might unduly bias the results.

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.