Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Signaling Pathways, Modeling of

  • Jeanette Hellgren Kotaleski
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_195-1

Synonyms

Definition

The use of models to study the dynamical behavior of a biological (sub)system described as a set of biochemical reactions and diffusion. Here, the evolution in time for quantities such as protein concentrations or amount of enzyme activation is typically described using ordinary differential equations. The evolution in time of second messengers is typically described using partial differential equations. Alternatively, stochastic methods can be used to determine evolution in time of all molecules in a simulation.

Detailed Description

The development of quantitative models at multiple spatial and temporal scales is necessary for integrating the knowledge obtained from diverse experimental approaches into a coherent picture. Such models represent current knowledge in a compact and standardized way and constitute a tool for guiding experiments and generating predictions.

Modeling of intracellular signaling within the field of computational neuroscience...

Keywords

Enzymatic Reaction Dendritic Spine Biochemical Reaction Deterministic Approach Reaction Cascade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. Bhalla US, Iyengar R (1999) Emergent properties of networks of biological signaling pathways. Science 283(5400):381–387PubMedCrossRefGoogle Scholar
  2. Briggs GE, Haldane JB (1925) A note on the kinetics of enzyme action. Biochem J 19:338–339PubMedCentralPubMedGoogle Scholar
  3. Kotaleski JH, Blackwell KT (2010) Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches. Nat Rev Neurosci 11:239–251PubMedCrossRefGoogle Scholar
  4. Lund EW (1965) Guldberg and Waage and the law of mass action. J Chem Ed 42:548–550CrossRefGoogle Scholar
  5. Manninen T, Hituri K, Kotaleski JH, Blackwell KT, Linne ML (2010) Postsynaptic signal transduction models for long-term potentiation and depression. Front Comput Neurosci 4:152PubMedCentralPubMedCrossRefGoogle Scholar
  6. Michaelis L, Menten ML (1913) Die Kinetik der Invertinwirkung. Biochem Z 49:333–369Google Scholar
  7. Ramakrishnan N, Bhalla US (2008) Memory switches in chemical reaction space. PLoS Comput Biol 4(7):e1000122PubMedCentralPubMedCrossRefGoogle Scholar

Further Reading

  1. Cornish-Bowden A (2012) Fundamentals of enzyme kinetics, 4th edn. Wiley-Blackwell, Weinheim, ISBN 978-3-527-33074-4Google Scholar
  2. Johnson KA, Goody RS (2011) The original Michaelis constant: translation of the 1913 Michaelis-Menten paper. Biochemistry 50:8264–8269PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Computer Science and CommunicationKTH Royal Institute of TechnologyStockholmSweden