Validation methods for population models of gene expression dynamics
Artículo de revista
2016
IFAC. International Federation of Automatic Control
Statistical methods
System biology
Stochastic modelling
Mixed-effects modelling
Gene expression
Métodos de estadística
Biología de sistemas
Modelización estocástica
Modelización de efectos mixtos
Expresión génica
Fisiología
Metabolismo
Expresión génica
Expresión genética
Physiology
Metabolism
Gene expression
Expresión del gen
Modelos biológicos
Biological models
Gene expression

System biology

Stochastic modelling

Mixed-effects modelling

Gene expression

Métodos de estadística

Biología de sistemas

Modelización estocástica

Modelización de efectos mixtos

Expresión génica

Fisiología

Metabolismo

Expresión génica

Expresión genética

Physiology

Metabolism

Gene expression

Expresión del gen

Modelos biológicos

Biological models

Gene expression

The advent of experimental techniques for the time-course monitoring of gene expression at the single-cell level has paved the way to the model-based study of gene expression variability within- an across-cells. A number of approaches to the inference of models accounting for variability of gene expression over isogenic cell populations have been developed and applied to real-world scenarios. The development of a systematic approach for the validation of population models is however lagging behind, and accuracy of the models obtained is often assessed on a semi-empirical basis. In this paper we study the problem of validating models of gene network dynamics for cell populations, providing statistical tools for qualitative and quantitative model validation and comparison, and guidelines for their application and interpretation based on a real biological case study
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