Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models.

ABSTRACT: In this paper, we address the system identification problem in the context of biological modelling. We present and demonstrate a methodology for (i) assessing the possibility of inferring the unknown quantities in a dynamic model and (ii) effectively estimating them from output data. We introduce the term Full Input-State-Parameter Observability (FISPO) analysis to refer to the simultaneous assessment of state, input and parameter observability (note that parameter observability is also known as identifiability). This type of analysis has often remained elusive in the presence of unmeasured inputs. The method proposed in this paper can be applied to a general class of nonlinear ordinary differential equations models. We apply this approach to three models from the recent literature. First,

SUBMITTER: Villaverde AF 

PROVIDER: S-EPMC6685009 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature