Sensitivity analysis of signaling pathway models based on discrete-time measurements

• Autorzy: Kardynska M. , Smieja J.

Identyfikatory

DOI

10.1515/acsc-2017-0015

• Języki publikacji: EN

Abstrakty

EN

The paper is focused on sensitivity analysis of large-scale models of biological systems that describe dynamics of the so called signaling pathways. These systems are continuous in time but their models are based on discrete-time measurements. Therefore, if sensitivity analysis is used as a tool supporting model development and evaluation of its quality, it should take this fact into account. Such models are usually very complex and include many parameters difficult to estimate in an experimental way. Changes of many of those parameters have little effect on model dynamics, and therefore they are called sloppy. In contrast, other parameters, when changed, lead to substantial changes in model responses and these are called stiff parameters. While this is a well-known fact, and there are methods to discern sloppy parameters from the stiff ones, they have not been utilized, so far, to create parameter rankings and quantify the influence of single parameter changes on system time responses. These single parameter changes are particularly important in analysis of signalling pathways, because they may pinpoint parameters, associated with the processes to be targeted at the molecular level in laboratory experiments. In the paper we present a new, original method of creating parameter rankings, based on an Hessian of a cost function which describes the fit of the model to a discrete experimental data. Its application is explained with simple dynamical systems, representing two typical dynamics exhibited by the signaling pathways.

Słowa kluczowe

EN

sensitivity analysis; signaling pathways; measurement uncertainty; discretetime measurements

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2017
• Tom: Vol. 27, no. 2
• Strony: 239--250
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr., wzory

Twórcy

autor

Kardynska M.

• Institute of Automatic Control, Silesian University of Technology, Akademicka Str. 16, 44-100 Gliwice, Poland

autor

Smieja J.

• Institute of Automatic Control, Silesian University of Technology, Akademicka Str. 16, 44-100 Gliwice, Poland

Bibliografia

• [1] R. Williams, J. Timmis and E. E. Qwarnstrom: Computational models of the NF-kB signalling pathway. Computation, 2(4), (2014), 131-158.
• [2] S. J. Maerkl and S. R. Quake: A systems approach to measuring the binding energy landscapes of transcription factors. Science, 315 (2007), 233-237.
• [3] D. A. Rand: Mapping the global sensitivity of cellular network dynamics. J. Royal Society Interface, 5 (2008), 59-69.
• [4] R. W. Newcomb: Linear Multiport Synthesis. McGraw-Hill, NY, 1966.
• [5] J. J. Cruz: Feedback Systems. McGraw-Hill, New York, 1972.
• [6] R. K. Brayton and R. Spence: Sensitivity and Optimization. Elsevier, Amsterdam, 1980.
• [7] M. Bentele, I. Lavrik, M. Ulrich, S. Stosser, D. W. Heermann, H. Kaltho, P. H. Krammer and R. Eils: Mathematical modeling reveals threshold mechanism in cd95-induced apoptosis. The Journal of Cell Biology, 166(6), (2004), 839-851.
• [8] H. Yue, M. Brown, J. Knowles, H. Wang, D. S. Broomhead and D. B. Kell: Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-κ B signalling pathway. Molecular BioSystems, 2(12), (2006), 640-649.
• [9] E. Balsa-Canto, A. A. Alonso and J. R. Banga: An iterative identification procedure for dynamic modeling of biochemical networks. BMC Systems Biology, 4(11), (2010).
• [10] V. Raia, M. Schilling and M. Boehm: Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets. Cancer Research, 71 (2011), 693-704.
• [11] K. Rateitschak, F. Winter, F. Lange, R. Jaster and O. Wolkenhauer: Parameter identifiability and sensitivity analysis predict targets for enhancement of STAT1 activity in pancreatic cancer and stellate cells. PLoS Computational Biology, 8(12), (2012), e1002815.
• [12] J. Smieja, M. Kardynska and A. Jamroz: The meaning of sensitivity functions in signaling pathways analysis. Discrete and Continuous Dynamical Systems – series B, 10 (2014), 2697-2707.
• [13] B. C. Daniels, Y. J. Chen, J. P. Sethna, R. N. Gutenkunst and C. R. Myers: Sloppiness, robustness, and evolvability in systems biology. Current Opinion in Biotechnology, 19 (2008), 389-395.
• [14] R. N. Gutenkunst, J. J. Watefall, F. P. Casey, K. S. Brown, C. R. Myers and J. P. Sethna: Universally sloppy parameter sensitivities in systems biology models. PLoS Computational Biology, 3(10), (2007), 1871-1878.
• [15] A. Marin-Sanguino, S. K. Gupta, E. O. Voit and J. Vera: Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases. Methods in Enzymology, 487 (2011), 319-369.
• [16] J. Leis and M. Kramer: Sensitivity analysis of systems of differential and algebraic equations. Computers & Chemical Engineering, 9 (1985), 93-96.
• [17] P. Iglesias and B. Ingalls (Ed): Control theory and systems biology. MIT Press, 2010.
• [18] J. J. Waterfall, F. P. Casey, R. N. Gutenkunst, K. S. Brown, C. R. Myers, P. W. Brouwer, V. Elser and J. P. Sethna: Sloppy-model universality class and the Vandermonde matrix. Physical Review Letters, 97(15), (2006), 150601.
• [19] R. A. Horn and C. R. Johnson: Matrix Analysis. Cambridge University Press, 1990.
• [20] K. S. Brown and J. P. Sethna: Statistical mechanical approaches to models with many poorly known parameters. Physical Review E, 68 (2003), 021904.
• [21] M. Kardynska and J. Smieja: L-1 and L-2 norms in sensitivity analysis of signaling pathway models. 21st Int. Conf. on Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, (2016), 589-594.
• [22] K. Puszynski, P. Lachor, M. Kardynska and J. Smieja: Sensitivity analysis of deterministic signaling pathways models. Bulletin of the Polish Academy of Sciences Technical Sciences, 60(3), (2012), 471-479.
• [23] K. A. Kim, S. L. Spencer, J. G. Albeck, J. M. Burke, P. K. Sorger, S. Gaudet and H. Kim do: Systematic calibration of a cell signaling network model. BMC Bioinformatics, 11 (2010), 202.
• [24] M. Kardynska and J. Smieja: Sensitivity analysis of signaling pathways in the frequency domain. Information Technologies in Medicine, 2 (2016), 275-285.
• [25] B. Hat, K. Puszynski and T. Lipniacki: Exploring mechanisms of oscillations in p53 and nuclear factor-κB systems. IET Systems Biology, 3 (2009), 342-355.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).