In this paper, we propose an observed-based algorithm to estimate the time course of a set of not-directly measurable gene expressions for the network motif of the Multi-Output Feed-Forward Loop (MO-FFL), widespread in gene transcription networks of many organisms. The MO-FFL has been modeled according to a standard ordinary differential equations approach, providing a nonlinear model in the state space. Simulations show the effectiveness of the proposed approach in a very wide range of possible critical frameworks, such as only one target gene measurements or non-smooth input perturbations
Cacace, F., Germani, A., Palumbo, P. (2009). Observer-based identification of a Multi-Output Feedforward Loop from gene expression data. In PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009) (pp.6189-6194) [10.1109/CDC.2009.5400807].
Observer-based identification of a Multi-Output Feedforward Loop from gene expression data
Palumbo, P
2009
Abstract
In this paper, we propose an observed-based algorithm to estimate the time course of a set of not-directly measurable gene expressions for the network motif of the Multi-Output Feed-Forward Loop (MO-FFL), widespread in gene transcription networks of many organisms. The MO-FFL has been modeled according to a standard ordinary differential equations approach, providing a nonlinear model in the state space. Simulations show the effectiveness of the proposed approach in a very wide range of possible critical frameworks, such as only one target gene measurements or non-smooth input perturbations
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