The Essential Role of Sensitivity Analysis in System Biology Models

The Essential Role of Sensitivity Analysis in System Biology Models

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Special Issue
Year of Publication : 2020
Authors : Nur Afifah Ahmad Ahyad, Afnizanfaizal Abdullah
DOI :  10.14445/22315381/IJETT-AIIC106

Citation 

MLA Style: Nur Afifah Ahmad Ahyad, Afnizanfaizal Abdullah  "The Essential Role of Sensitivity Analysis in System Biology Models" International Journal of Engineering Trends and Technology (2020):26-32. 

APA Style:Nur Afifah Ahmad Ahyad, Afnizanfaizal Abdullah. The Essential Role of Sensitivity Analysis in System Biology Models  International Journal of Engineering Trends and Technology, 26-32.

Abstract
Biological network behaviour is triggered by several conditions inside and outside of the cell. Just like human tends to secrete more urine during cold weather, components inside a biological network also work the same way. Component behaviour cannot be determined as individual only but also by including the interaction with other components in a system. Along with the numerous research conducted, several mathematical model approach has been used for modelling biological network. These models require knowledge on initial condition and range of parameters needed for model functionality. However, these parameters are difficult and sometimes impossible to be captured in a biological lab experiment thus create uncertainties in model parameters. To address this problem, sensitivity analysis is used in modelling of system biology models. Sensitivity analysis helps in reducing model complexity, capture precision parameters and guide biological experimental. This paper provides overview of several sensitivity analysis approaches and guidelines in choosing sensitivity analysis approach.

Reference

[1] N. Vijesh, S. K. Chakrabarti, and J. Sreekumar, “Modeling of Gene Regulatory Networks: A Review”, Journal of Biomedical Science and Engineering, Vol. 6, No. 2, 2013.
[2] L. Glass, and S. A. Kauffman, “The Logical Analysis of Continuous, Non-Linear Biochemical Control Networks”, Journal of Theoretical Biology, Vol. 39, No. 1, 1973.
[3] R. Thomas, “Boolean Formalization of Genetic Control Circuits”, Journal of Theoretical Biology, Vol. 42, No. 3, 1973.
[4] G. Minetti, E. Alba, and G. Luque, “Seeding Strategies and Recombination Operators for Solving The DNA Fragment Assembly Problem”, Information Processing Letters, Vol. 108, No. 3, 2008.
[5] A. Saltelli, et al, “Global Sensitivity Analysis: The Primer”, John Wiley & Sons, 2008.
[6] M. Alam, et al, “Agent-Based Modeling and High Performance Computing”, Computational Immunology. Academic Press, 2016.
[7] B. Ingalls, “Sensitivity Analysis: from Model Parameters to System Behaviour”, Essays in Biochemistry, Vol. 45, 2008.
[8] Z. Zi, “Sensitivity Analysis Approaches Applied to Systems Biology Models”, IET Systems Biology, Vol. 5, No. 6, 2011.
[9] A. Raue, C. Kreutz, T. Malwald, U. Klingmuller, and J. Timmer, “Addressing Parameter Identifiability by Model-Based Experimentation”, IET Systems Biology, Vol. 5, No. 2, 2011.
[10] Z. Zi, Y. Zheng, A. E. Rundell, and E. Klipp, “SBML-SAT: A Systems Biology Markup Language (SBML) Based Sensitivity Analysis Tool”, BMC bioinformatics, Vol. 9, No. 1, 2008.
[11] G. M. Hornberger, and R. C. Spear, “Approach to The Preliminary Analysis of Environmental Systems”, J. Environ. Mgmt., Vol. 12, No. 1, 1981.
[12] D. J. Pannell, “Sensitivity Analysis of normative Economic Models: Theoretical Framework and Practical Strategies”, Agricultural Economics, Vol. 16, No. 2, 1997.
[13] H. Rabitz, “Sensitivity Analysis in Chemical Kinetics”, Annual Review of Physical Chemistry, Vol. 34, 1983.
[14] H. Rabitz, “Systems Analysis at The Molecular Scale”, Science, Vol. 246, No. 4927, 1989.
[15] A. Saltelli, M. Ratto, S. Tarantola, and F. Campolongo, “Sensitivity Analysis for Chemical Models”, Chemical Reviews, Vol. 105, No. 7, 2005.
[16] Y. Zheng, and A. Rundell, “Comparative Study of Parameter Sensitivity Analyses of The TCR-Activated Erk-MAPK Signalling Pathway”, IEE Proceedings- Systems Biology, Vol. 153, No. 4, 2006.
[17] F. Fröhlich, B. Kaltenbacher, F. J. Theis, and J. Hasenauer, “Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks”, PLoS Computational Biology, Vol. 13, No. 1, 2017.
[18] T. O’Connor, X. Yang, G. Tian, S. Chatterjee, and S. Lee, “Quality Risk Management for Pharmaceutical Manufacturing: The Role of process modeling and Simulations”, Predictive Modeling of Pharmaceutical Unit Operations. Woodhead Publishing, 2017.
[19] S. Hoops, et. al., “Ordinary Differential Equations (ODEs) Based Modeling”, Computational Immunology. Academic Press, 2016.
[20] A. Raue, et al., “Data2Dynamics: A Modeling Environment Tailored to Parameter Estimation In Dynamical Systems”, Bioinformatics, Vol. 31, No. 21, 2015.
[21] A. C. Hindmarsh, et al., “SUNDIALS: Suite of Nonlinear and Differential/Algebraic Equation Solvers”, ACM Transactions on Mathematical Software (TOMS), Vol. 31, No. 3, 2005.
[22] T. O’Connor, X. Yang, G. Tian, S. Chatterjee, and S. Lee, “Quality Risk Management for Pharmaceutical Manufacturing: The Role of process modeling and Simulations”, Predictive Modeling of Pharmaceutical Unit Operations. Woodhead Publishing, 2017.
[23] S. M. Blower, and H. Dowlatabadi, “Sensitivity and Uncertainty Analysis of Complex Models of Disease Transmission: An HIV Model, as An Example”, International Statistical Review/Revue Internationale de Statistique, 1994.
[24] S. M. Blower, H. B. Gershengorn, and R. M. Grant, “A Tale of Two Futures: HIV and Antiretroviral Therapy in San Francisco”, Science, Vol. 287, No. 5453, 2000.
[25] J. M. Dresch, X. Liu, D. N. Arnosti, and A. Ay, “Thermodynamic Modeling of Transcription: Sensitivity Analysis Differentiates Biological Mechanism from Mathematical Model-Induced Effects”, BMC systems biology, Vol. 4, No. 1,2010.
[26] Y. Jin, X. Peng, Y. Liang, and J. Ma, “Uniform Design-Based Sensitivity Analysis of Circadian Rhythm Model in Neurospora”, Computers & Chemical Engineering, Vol. 32, No. 8, 2008.
[27] X. J. Feng, et al., “Optimizing Genetic Circuits by Global Sensitivity Analysis”, Biophysical Journal, Vol. 87, No. 4, 2004.
[28] A. Rhemimet, S. Raghay, and O. Bencharef, “Long Short-Term Memory Recurrent Neural Network Architectures to Predict HIV-1 Protease Cleavage Sites”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 9, No. 1, 2020.
[29] H.D. Gadade, and D.K. Kirange, “Machine Learning Approach towards Tomato Leaf Disease Classification”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 9, No. 1, 2020.

Keywords
Computational Biology, ODE Biological Model, Parameter Uncertainties, Sensitivity Analysis.