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Systems Biology Approaches to the Study of Apoptosis

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Essentials of Apoptosis

Abstract

Today, we can avail of comprehensive information on the molecular mechanisms of apoptosis signaling that was gathered during decades of intense research. This chapter presents how mathematical approaches in the field of cellular signaling are used to integrate this complex and heterogeneous information into computational models with the aim to elucidate the functional properties of apoptotic signaling networks. Mathematical modeling allows one to describe properties of signaling systems that emanate from the interplay of the system’s individual components and has a longstanding and successful history in the fields of physics, chemistry, and their applied engineering sciences. Systems analyses can serve to describe and identify signaling dynamics, molecular switches, thresholds, and feedback regulatory mechanisms and allow systems properties such as stability and robustness toward external perturbations to be identified. Crucially, systems analyses can also serve to generate novel qualitative and quantitative research hypotheses, which in turn allow for more focused experimental research approaches. This chapter provides a concise and critical overview on the current state of systems biology in the field of apoptotic signaling and the methodology employed.

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Acknowledgments

The authors wish to acknowledge Lorna Flanagan for critical reading of the manuscript and for helpful discussions. This work was supported by grants from Health Research Board Ireland (RP/2006/258) and Science Foundation Ireland (07/RFP/BICF601) awarded to MR, as well as by Siemens Ireland and the Image to Mathematical Modelling Transition Core of the Irish National Biophotonics and Imaging Platform (HEA PRTLI Cycle 4) and the EU Framework Programme 7 (APO-SYS).

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Authors and Affiliations

  1. Systems Biology Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, RCSI York House, York Street, Dublin 2, Ireland

    Heinrich Huber

  2. Industrial Control Centre, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, Scotland, UK

    Eric Bullinger

  3. Hamilton Institute, National University of Ireland, Maynooth, Ireland

    Markus Rehm

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  1. Heinrich Huber
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  2. Eric Bullinger
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  3. Markus Rehm
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Correspondence to Markus Rehm .

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    Huber, H., Bullinger, E., Rehm, M. (2009). Systems Biology Approaches to the Study of Apoptosis. In: Dong, Z., Yin, XM. (eds) Essentials of Apoptosis. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60327-381-7_12

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