Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference.
BACKGROUND
Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given.


RESULTS
Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach.


CONCLUSIONS
This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models.


BACKGROUND
Matlab code available on demand.
IBISC FRE CNRS 2873, University of Evry and Genopole 523, place des terrasses 91025 Evry, France.
Minh
M
Quach
Nicolas
N
Brunel
Florence
F
d'Alché-Buc
D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D013269:Stochastic Processes; D049490:Systems Biology / Q000379:methods
D020543:Proteome