Biocharts: a visual formalism for complex biological systems
Hillel Kugler1,*†, Antti Larjo2,† and David Harel3
Author Affiliations
1Computational Biology Group, Microsoft Research, Cambridge, UK
2Department of Signal Processing, Tampere University of Technology, Finland
3Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel
*Author for correspondence (hkugler@microsoft.com).
Abstract
We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis.
biological modelling, multi-scale modelling, statecharts, bacterial chemotaxis, metabolism
Footnotes
† These authors contributed equally to the study.
Received October 19, 2009.
Accepted November 24, 2009.
© 2009 The Royal Society
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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