Related Experiment Video
Updated: Jun 6, 2026
10:41
Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools
Published on: December 16, 2015
Modeling of cellular processes: methods, data, and requirements.
Thomas Millat1, Olaf Wolkenhauer, Ralf-Jörg Fischer
1Systems Biology & Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany. thomas.millat@uni-rostock.de
Methods in Molecular Biology (Clifton, N.J.)
|November 11, 2010
Summary
Systems biology uses interdisciplinary teams and system-theoretic methods for quantitative analysis of biological systems. This workflow highlights the critical role of experimental data quality and completeness in biological modeling.
Area of Science:
- Systems biology
- Computational biology
- Bioinformatics
Background:
- Systems biology offers a comprehensive, quantitative approach to understanding biological system interactions over time.
- It necessitates an interdisciplinary team of investigators to analyze complex biological systems.
- System-theoretic methods are integral to investigating and understanding system behavior.
Purpose of the Study:
- To define the relationship between models and data within the systems biology workflow.
- To focus on the specific requirements for experimental data from a systems biology perspective.
- To underscore the impact of data quality, quantity, and completeness on cellular process modeling.
Main Methods:
- Development of conceptual models based on existing system knowledge.
- Translation of conceptual models into mathematical models for simulation and analysis.
- Application of system-theoretic tools for model simulation (analytical or numerical) and analysis.
- Comparison of simulation outcomes with experimental data.
Main Results:
- Mathematical models are derived from conceptual models for simulation and analysis.
- Assumptions, approximations, and data availability are critical factors in the systems biology workflow.
- The modeling of cellular processes imposes specific demands on experimental design.
Conclusions:
- The quality, amount, and completeness of experimental data are crucial for successful systems biology projects.
- Effective systems biology modeling relies on a strong interplay between computational models and empirical data.
- Understanding data requirements is essential for advancing the field of systems biology and cellular process modeling.
