Skip to main content
Log in

Theoretical Analysis of Time-to-Peak Responses in Biological Reaction Networks

  • Original Article
  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

Processing of information by signaling networks is characterized by properties of the induced kinetics of the activated pathway components. The maximal extent of pathway activation (maximum amplitude) and the time-to-peak-response (position) are key determinants of biological responses that have been linked to specific outcomes. We investigate how the maximum amplitude of pathway activation and its position depend on the input and wiring of a signaling network. For this purpose, we consider a simple reaction AB that is regulated by a transient input and extended this to include back-reaction and additional partners. In particular, we show that a unique maximum of B(t) exists. Moreover, we prove that the position of the maximum is independent of the applied input but regulated by degradation reactions of B. Indeed, the time-to-peak-response decreases with increasing degradation rate, which we prove for small models and show in simulations for more complex ones. The identified dependencies provide insights into design principles that facilitate the realization dynamical characteristics like constant position of maximal pathway activation and thereby guide the characterization of unknown kinetics within larger protein networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Asthagiri, A. R., Reinhart, C. A., Horwitz, A. F., & Lauffenburger, D. A. (2000). The role of transient erk2 signals in fibronectin- and insulin-mediated dna synthesis. J. Cell Sci., 113(24), 4499–4510.

    Google Scholar 

  • Behar, M., Hao, N., Dohlman, H., & Elston, T. (2007). Mathematical and computational analysis of adaptation via feedback inhibition in signal transduction pathways. Biophys. J., 93(3), 806–821.

    Article  Google Scholar 

  • Chen, J. R., Plotkin, L. I., Aguirre, J. I., Han, L., Jilka, R. L., Kousteni, S., Bellido, T., & Manolagas, S. C. (2005). Transient versus sustained phosphorylation and nuclear accumulation of erks underlie anti-versus pro-apoptotic effects of estrogens. J. Biol. Chem., 280(6), 4632–4638.

    Article  Google Scholar 

  • Cornish-Bowden, A. (1995). Fundamentals of enzyme kinetics. London: Portland Press.

    Google Scholar 

  • Dayneka, N., Garg, V., & Jusko, W. (1993). Comparison of four basic models of indirect pharmacodynamic responses. J. Pharmacokin. Biopharm., 21, 457–478.

    Article  Google Scholar 

  • Dolmetsch, R. E., Lewis, R. S., Goodnow, C. C., & Healy, J. I. (1997). Differential activation of transcription factors induced by ca2+ response amplitude and duration. Nature, 386(6627), 855–858.

    Article  Google Scholar 

  • Gabrielsson, J., & Weiner, D. (1997). Pharmacokinetic/pharmacodynamic data analysis: concepts and applications, Stockholm: Swedish Pharmaceutical Press.

    Google Scholar 

  • Gurdon, J. B., Standley, H., Dyson, S., Butler, K., Langon, T., Ryan, K., Stennard, F., Shimizu, K., & Zorn, A. (1999). Single cells can sense their position in a morphogen gradient. Development, 126(23), 5309–5317.

    Google Scholar 

  • Heinrich, P. C., Behrmann, I., Haan, S., Hermanns, H. M., Müller-Newen, G., & Schaper, F. (2003). Principles of interleukin (il)-6-type cytokine signalling and its regulation. Biochem. J., 374(1), 1–20.

    Article  Google Scholar 

  • Heinrich, R., Neel, B. G., & Rapoport, T. A. (2002). Mathematical models of protein kinase signal transduction. Mol. Cell, 9(5), 957–970.

    Article  Google Scholar 

  • Kholodenko, B. (2006). Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol., 7(3), 165–176.

    Article  Google Scholar 

  • Kholodenko, B. N., Demin, O. V., Moehren, G., & Hoek, J. B. (1999). Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem., 274(42), 30169–30181.

    Article  Google Scholar 

  • Kitano, H. (2002). Computational systems biology. Nature, 420(6912), 206–210.

    Article  Google Scholar 

  • Krzyzanski, W., & Jusko, W. (1997). Mathematical formalism for the properties of four basic models of indirect pharmacodynamic responses. J. Pharmacokinet. Biopharm., 25, 107–123.

    Article  Google Scholar 

  • Lauffenburger, D., & Linderman, J. (1993). Receptors: models for binding, trafficking, and signaling. Oxford: Oxford University Press.

    Google Scholar 

  • Mager, D., Wyska, E., & Jusko, W. (2003). Diversity of mechanism-based pharmacodynamic models. Drug Metab. Dispos, 31, 510–519.

    Article  Google Scholar 

  • Marshall, C. J. (1995). Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation. Cell, 80(2), 179–185.

    Article  Google Scholar 

  • Nguyen, H., & Peletier, L. (2009). Monotonicity of time to peak response with respect to drug dose for turnover models. C. R. Acad. Sci. Paris, 347(9–10), 495–500.

    MATH  MathSciNet  Google Scholar 

  • Peletier, L., Gabrielsson, J., & Haag, K. (2005). A dynamical systems analysis of the indirect response model with special emphasis on time to peak response. J. Pharmacokinet. Pharmacodyn., 32(3–4), 607–654.

    Article  Google Scholar 

  • Schilling, M., Maiwald, T., Bohl, S., Kollmann, M., Kreutz, C., Timmer, J., & Klingmüller, U. (2005). Computational processing and error reduction strategies for standardized quantitative data in biological networks. FEBS J., 272(24), 6400–6411.

    Article  Google Scholar 

  • Weisstein, E. (2008). Exponential integral. Tech. rep., MathWorld. http://mathworld.wolfram.com/ExponentialIntegral.html. Accessed 23 April 2010.

  • Wolkenhauer, O., & Mesarovic, M. (2005). Feedback dynamics and cell function: why systems biology is called systems biology. Mol. Biosyst., 1(1), 14–16.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabian J. Theis.

Additional information

F.J. Theis and S. Bohl contributed equally.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Cite this article

Theis, F.J., Bohl, S. & Klingmüller, U. Theoretical Analysis of Time-to-Peak Responses in Biological Reaction Networks. Bull Math Biol 73, 978–1003 (2011). https://doi.org/10.1007/s11538-010-9548-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11538-010-9548-x

Keywords

Navigation