Potential integration of multi-fitting, inverse problem and mechanistic modelling approaches to applied research in animal science: a review
L. M. Vargas-Villamil A B C and L. O. Tedeschi A
+ Author Affiliations
- Author Affiliations
A Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA.
B Present address: Colegio de Postgraduados, Apartado postal 24, 86500, Cárdenas, Tabasco, México.
C Corresponding author. Email: luis@avanzavet.com
Animal Production Science 54(12) 1905-1913 https://doi.org/10.1071/AN14568
Submitted: 13 May 2014 Accepted: 30 July 2014 Published: 20 October 2014
Abstract
Modern researchers working in applied animal science systems have faced issues with modelling huge quantities of data. Modelling approaches that have previously been used to model biological systems are having problems to adapt to increased number of publications and research. So as to develop new approaches that have the potential to deal with these fast-changing complex conditions, it is relevant to review modern modelling approaches that have been used successfully in other fields. Therefore, this paper reviews the potential capacity of new integrated applied animal-science approaches to discriminate parameters, interpret data and understand biological processes. The analysis shows that the principal challenge is handling ill-conditioned complex models, but an integrated approach can obtain meaningful information from complementary data that cannot be obtained from present applied animal-science approaches. Furthermore, it is shown that parameter sloppiness and data complementarity are key concepts during system behaviour restrictions and parameter discrimination. Additionally, model evaluation and implementation of the potential integrated approach are reviewed. Finally, the objective of an integral approach is discussed. Our conclusion is that these approaches have the potential to be used to deepen the understanding of applied animal systems, and that exist enough developed resources and methodologies to deal with the huge quantities of data associated with this science.
Additional keywords: animal models, estimation, research methods, simulation models, uncertainty.
References
Apgar JF, Witmer DK, White FM, Tidor B (2010) Sloppy models, parameter uncertainty, and the role of experimental design. Molecular BioSystems 6, 1890–1900.| Sloppy models, parameter uncertainty, and the role of experimental design.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtFamtr7M&md5=df8601c34f2e8dfb4296379e05f731b8CAS | 20556289PubMed |
Ashyraliyev M, Jaeger J, Blom JG (2008) Parameter estimation and determinability analysis applied to Drosophila gap gene circuits. BMC Systems Biology 2, 83
| Parameter estimation and determinability analysis applied to Drosophila gap gene circuits.Crossref | GoogleScholarGoogle Scholar | 18817540PubMed |
Beck JV, Woodbury KA (1998) Inverse problems and parameter estimation: integration of measurements and analysis. Measurement Science & Technology 9, 839–847.
| Inverse problems and parameter estimation: integration of measurements and analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1cXjvVGiu7g%3D&md5=6f9aa6e95707ac74160cfe3c33a88968CAS |
Boston RC, Wilkins P, Tedeschi LO (2007) Identifiability and accuracy: two critical problems associated with the application of models in nutrition and the health sciences. In ‘Mathematical modeling for nutrition and health sciences’. (Ed. M Hanigan) pp. 161–193. (University of Pennsylvania: Roanoke, NC)
Brown KS, Sethna JP (2003) Statistical mechanical approaches to models with many poorly known parameters. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics 68, 021904
| Statistical mechanical approaches to models with many poorly known parameters.Crossref | GoogleScholarGoogle Scholar |
Daniels BC, Chen YJ, Sethna JP, Gutenkunst RN, Myers CR (2008) Sloppiness, robustness, and evolvability in systems biology. Current Opinion in Biotechnology 19, 389–395.
| Sloppiness, robustness, and evolvability in systems biology.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtValsLzO&md5=1ae7d885e1cd775c69727d008615343fCAS | 18620054PubMed |
Dräger A, Schröder A, Zell A (2010) Automating mathematical modeling of biochemical reaction networks. In ‘Systems biology for signaling networks’. (Ed. S. Choi) pp. 159–205. (Springer: New York)
Engl HW, Flamm C, Kügler P, Lu J, Müller S, Schuster P (2009) Inverse problems in systems biology. Inverse Problems 25, 123 014
| Inverse problems in systems biology.Crossref | GoogleScholarGoogle Scholar |
Erguler K, Stumpf MPH (2011) Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models. Molecular BioSystems 7, 1593–1602.
| Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXksFSisr4%3D&md5=b137aa699e9c59be8ebbf630470ab8ecCAS | 21380410PubMed |
Groetsch CW (1993) Inverse Problems and Torricelli’s Law. The College Mathematics Journal 24, 210–217.
| Inverse Problems and Torricelli’s Law.Crossref | GoogleScholarGoogle Scholar |
Guanawardena J (2010) Models in systems biology: the parameter problem and the meanings of robustness. In ‘Elements of computational systems biology. Vol. 1’. (Eds HM Lodhi, SH Muggleton) pp. 1–28. (John Wiley & Sons: Hoboken, NJ)
Gutenkunst RN, Casey FP, Waterfall JJ, Myers CR, Sethna JP (2007a) Extracting falsifiable predictions from sloppy models. Annals of the New York Academy of Sciences 1115, 203–211.
| Extracting falsifiable predictions from sloppy models.Crossref | GoogleScholarGoogle Scholar | 17925353PubMed |
Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP (2007b) Universally sloppy parameter sensitivities in systems biology models. PLoS Computational Biology 3, e189
| Universally sloppy parameter sensitivities in systems biology models.Crossref | GoogleScholarGoogle Scholar |
Jacquez JA, Greif P (1985) Numerical parameter identifiability and estimability: integrating identifiability, estimability, and optimal sampling design. Mathematical Biosciences 77, 201–227.
| Numerical parameter identifiability and estimability: integrating identifiability, estimability, and optimal sampling design.Crossref | GoogleScholarGoogle Scholar |
Little MP, Heidenreich WF, Li G (2010) Parameter identifiability and redundancy: theoretical considerations. PLoS ONE 5, e8915
| Parameter identifiability and redundancy: theoretical considerations.Crossref | GoogleScholarGoogle Scholar | 20111720PubMed |
Maiwald T, Timmer J (2008) Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics 24, 2037–2043.
| Dynamical modeling and multi-experiment fitting with PottersWheel.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtFWhurjP&md5=d002f0bb12fe616240a001b4916be347CAS | 18614583PubMed |
Maiwald T, Kreutz C, Pfeirfer AC, Bohl S, Klingmuller U, Timmer J (2007) Dynamic pathway modeling. Annals of the New York Academy of Sciences 1115, 212–220.
| Dynamic pathway modeling.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhsVyhu7fI&md5=dcabd191e13cc80eb46969cb91baed54CAS | 18033750PubMed |
Petitti DB (2000) ‘Meta-analysis, decision analysis, and cost-effectiveness analysis: methods for quantitative synthesis in medicine.’ (Oxford University Press: New York)
Pia Saccomani M, Audoly S, D’Angiò L (2003) Parameter identifiability of nonlinear systems: the role of initial conditions. Automatica 39, 619–632.
| Parameter identifiability of nonlinear systems: the role of initial conditions.Crossref | GoogleScholarGoogle Scholar |
Poeter EP, Hill MC (1997) Inverse models: a necessary next step in ground-water modeling. Ground Water 35, 250–260.
| Inverse models: a necessary next step in ground-water modeling.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2sXhslartL4%3D&md5=18e804062ec3ce4b28525c474df299e6CAS |
Sauvant D, Martin O (2006) Empirical modelling through meta-analysis vs mechanistic modelling. In ‘Nutrient digestion and utilization in farm animals: modelling approaches’. (Eds J France, J Dijkstra, A Bannink, WJJ Gerrits) pp. 242–250. (CABI International: Cambridge, UK)
Tarantola A (2006) Popper, Bayes and the inverse problem. Nature Physics 2, 492–494.
| Popper, Bayes and the inverse problem.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xos1Kms78%3D&md5=704a18f8fdb77df1a40c03e4d1c4f2d4CAS |
Tedeschi LO (2006) Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225–247.
| Assessment of the adequacy of mathematical models.Crossref | GoogleScholarGoogle Scholar |
Tedeschi LO, Boston RC (2010) Identifiability and accuracy: a closer look at contemporary contributions and changes in these vital areas of mathematical modelling. In ‘Modelling nutrient digestion and utilization in farm animals’. (Eds D Sauvant, J Van Milgen, P Faverdin, N Friggens) pp. 91–99. (University of Wageningen Press: Wageningen, The Netherlands)
Tedeschi LO, Fox DG, Sainz RD, Barioni LG, de Medeiros SR, Boin C (2005) Mathematical models in ruminant nutrition. Scientia Agricola 62, 76–91.
| Mathematical models in ruminant nutrition.Crossref | GoogleScholarGoogle Scholar |
Toni T, Stumpf MP (2010) Parameter inference and model selection in signaling pathway models. In ‘Computational biology. Vol. 673’. (Ed. D Fenyö) pp. 283–295. (Human Press: New York)
Vargas-Villamil LM, Ku-Vera JC, Vargas-Villamil F, Medina-Peralta S, Avila-Vales EJ, Aranda-Ibañez EM, Avendaño-Reyes L (2013) Turix, a dynamic mechanistic model for feed evaluation. Revista Brasileira De Zootecnia – Brazilian Journal of Animal Science 42, 291–300.
| Turix, a dynamic mechanistic model for feed evaluation.Crossref | GoogleScholarGoogle Scholar |
Waterfall JJ, Casey FP, Gutenkunst RN, Brown KS, Myers CR, Brouwer PW, Elser V, Sethna JP (2006) Sloppy-model universality class and the Vandermonde matrix. Physical Review Letters 97, 150601-1–150601-2.
| Sloppy-model universality class and the Vandermonde matrix.Crossref | GoogleScholarGoogle Scholar |
Woelders H, Te Pas M, Bannink A, Veerkamp R, Smits M (2011) Systems biology in animal sciences. Animal 5, 1036–1047.
| Systems biology in animal sciences.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC38vovV2isw%3D%3D&md5=b13cc6f22fa57e9609a092e9f63ea456CAS | 22440099PubMed |
Young PC (2006) The data-based mechanistic approach to the modelling, forecasting and control of environmental systems. Annual Reviews in Control 30, 169–182.
| The data-based mechanistic approach to the modelling, forecasting and control of environmental systems.Crossref | GoogleScholarGoogle Scholar |
