PROJECT TITLE :

Structural and practical identifiability analysis of S-system

ABSTRACT:

In the field of systems biology, biological reaction networks are typically modelled by standard differential equations. A sub-category, the S-systems illustration, is a widely used kind of modelling. Existing S-systems identification techniques assume that the system itself is often structurally identifiable. However, thanks to sensible limitations, biological reaction networks are usually solely partially measured. In addition, the captured knowledge solely covers a limited trajectory, therefore knowledge can solely be considered as a local snapshot of the system responses with respect to the entire set of state trajectories over the entire state area. Hence the estimated model can solely mirror partial system dynamics and might not be unique. To boost the identification quality, the structural and practical identifiablility of S-system are studied. The S-system is shown to be identifiable below a set of assumptions. Then, an application on yeast fermentation pathway was conducted. 2 case studies were chosen; where the primary case is predicated on a bigger state trajectories and therefore the second case is predicated on a smaller one. By expanding the dataset which span a comparatively larger state space, the uncertainty of the estimated system can be reduced. The results indicated that initial concentration is connected to the sensible identifiablity.