Realistic simulation of time-course measurements in systems biology

Janine Egert; Clemens Kreutz; Janine Egert; Clemens Kreutz

doi:10.3934/mbe.2023467

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10570-10589. doi: 10.3934/mbe.2023467

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Realistic simulation of time-course measurements in systems biology

Janine Egert ^1,2,
Clemens Kreutz ^{1,2,3
,
,}

1.
Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center – University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany
2.
Centre for Integrative Biological Signalling Studies CIBSS, University of Freiburg, 79104 Freiburg, Germany
3.
Center for Data Analysis and Modelling (FDM), University of Freiburg, 79104 Freiburg, Germany

Academic Editor: Zhuolin Qu

Received: 28 December 2022 Revised: 29 March 2023 Accepted: 30 March 2023 Published: 11 April 2023

In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. For the evaluation and comparison of the performances of novel and competing computational methods, one major bottleneck is the availability of realistic test problems. We present an approach for performing realistic simulation studies for analyses of time course data as they are typically measured in systems biology. Since the design of experiments in practice depends on the process of interest, our approach considers the size and the dynamics of the mathematical model which is intended to be used for the simulation study. To this end, we used 19 published systems biology models with experimental data and evaluated the relationship between model features (e.g., the size and the dynamics) and features of the measurements such as the number and type of observed quantities, the number and the selection of measurement times, and the magnitude of measurement errors. Based on these typical relationships, our novel approach enables suggestions of realistic simulation study designs in the systems biology context and the realistic generation of simulated data for any dynamic model. The approach is demonstrated on three models in detail and its performance is validated on nine models by comparing ODE integration, parameter optimization, and parameter identifiability. The presented approach enables more realistic and less biased benchmark studies and thereby constitutes an important tool for the development of novel methods for dynamic modeling.
- systems biology,
- simulation,
- method evaluation,
- benchmark,
- model dynamics
Citation: Janine Egert, Clemens Kreutz. Realistic simulation of time-course measurements in systems biology[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10570-10589. doi: 10.3934/mbe.2023467

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Abstract

In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. For the evaluation and comparison of the performances of novel and competing computational methods, one major bottleneck is the availability of realistic test problems. We present an approach for performing realistic simulation studies for analyses of time course data as they are typically measured in systems biology. Since the design of experiments in practice depends on the process of interest, our approach considers the size and the dynamics of the mathematical model which is intended to be used for the simulation study. To this end, we used 19 published systems biology models with experimental data and evaluated the relationship between model features (e.g., the size and the dynamics) and features of the measurements such as the number and type of observed quantities, the number and the selection of measurement times, and the magnitude of measurement errors. Based on these typical relationships, our novel approach enables suggestions of realistic simulation study designs in the systems biology context and the realistic generation of simulated data for any dynamic model. The approach is demonstrated on three models in detail and its performance is validated on nine models by comparing ODE integration, parameter optimization, and parameter identifiability. The presented approach enables more realistic and less biased benchmark studies and thereby constitutes an important tool for the development of novel methods for dynamic modeling.

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