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Computational Models of Transcription Control: A Systems-Theoretic Perspective

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In a gene network, genes may be expressed constantly, or expressed based on molecular signals. Transcription is a key process in gene expression. Through evolution, biological organisms have developed internal regulatory mechanisms for transcription control. Such mechanisms dictate how the network will function under certain environmental conditions and respond to changes in the environment. To develop formal approaches that enable the design and synthesis of such logical controls in artificial gene networks represents a major challenge. A first step in meeting this challenge would be to build analytical models of transcription control. This paper reviews computational approaches for modeling transcription control in gene networks from a systems-theoretic perspective, with emphasis on the logical representational capability of the models and their potential use in synthesis of external control.





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Keywords: Gene networks; computational models; formal methods; transcription control

Document Type: Research Article

Affiliations: Bachelor of Technology Programme, Faculty of Engineering, National University of Singapore, Singapore.

Publication date: August 1, 2006

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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