Abstract
Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems.
Highlights
Computational models are needed to describe the relationship between input and output data of a system as well as to estimate future outputs based on inputs
A more practical approach is to construct a grey-box system identification (SI) model, which depends on some prior knowledge about the system, or a black-box SI model, which does not require any prior knowledge about the system
Since many of the processes underlying the uncertainties of gene networks are likely to be inherently timevariant, we hypothesize that time-variant models can potentially match and estimate experimental measurements better than timeinvariant models
Summary
Computational models are needed to describe the relationship between input and output data of a system as well as to estimate future outputs based on inputs. The observed data are subject to measurement errors (e3) All these sources of uncertainty contribute to the perceived stochasticity of gene networks preventing the model estimates from better matching the data. Since many of the processes underlying the uncertainties of gene networks are likely to be inherently timevariant, we hypothesize that time-variant models can potentially match and estimate experimental measurements better than timeinvariant models. Used in engineering disciplines such as communications, signal processing, and control, an adaptive filter iteratively and continuously adjusts the model parameters based on the error between the measured and estimated data (Fig. 1B). Using recently available time-series data for the p53-MDM2 network as an example [5], we demonstrate that adaptive filters can be used to ‘‘track’’ the changing parameters of gene network models and to enhance model estimation. The levels of p53 and MDM2 in individual MCF7 cells have been tracked by time-lapse microscopy using the p53-CFP and MDM2-YFP fluorescent reporters [5]
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