Practical Inference Methods for Mechanistic Modelling of Biological Systems

Parameter estimation of ODE's using Support Vector Regression and Qualitative Constraints

author: Paola Bouchet, Université Evry Val d'Essonne

Description

Dynamical systems used for the modeling of biological networks (such as gene regulatory networks or metabolic networks) are generally based on Ordinary Differential Equations (ODE`s). Although nonlinear ODE`s are commonly used in Systems Biology, their identification and estimation from real data remain a difficult task because of the high number of parameters to estimate compared with the relatively few number of observations.

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*Slides*
0:00	Parameter Estimation of ODEs Using Support Vector Regression and Qualitative Constraints
0:32	Ordinary Differential Equations in Systems Biology - 1
1:04	Ordinary Differential Equations in Systems Biology - 2
1:36	Outline
1:55	Model and Assumptions
2:38	General Principle of the Two-Step Estimator
3:45	Support Vector Regression
4:44	The ε–SVR (Vapnik, 1995)
5:14	Difference between SVR and LS Splines Regression
5:16	The ε–SVR (Vapnik, 1995)
5:30	Difference between SVR and LS Splines Regression
6:13	- Questions
7:17	Incorporation of Qualitative Knowledge on the Nature of Dynamics
7:29	Incorporation of Qualitative Knowledge on the Dynamics - 1
7:50	Incorporation of Qualitative Knowledge on the Dynamics - 2
8:12	Incorporation of Qualitative Knowledge on the Dynamics - 3
8:30	Incorporation of Qualitative Knowledge - 1
9:09	Incorporation of Qualitative Knowledge - 2
9:50	Example of Qualitative Knowledge: Oscillating Behavior - 1
10:15	Example of Qualitative Knowledge: Oscillating Behavior - 2
10:57	Example of Qualitative Knowledge: Oscillating Behavior - 3
11:13	Example: First Results on the Repressilator
11:23	The Repressilator (Elowitz and Leibler, 2000) - 1
11:50	The Repressilator (Elowitz and Leibler, 2000) - 2
12:16	Theoretical Curve
13:22	Influence of Observations Position
14:07	Decomposition of P3 (Semiparametric SVR)
14:29	Nonparametric SVR vs Semiparametric SVR
15:08	Conclusion and Perspectives - 1
15:36	Conclusion and Perspectives - 2

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