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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
Published on: August 21, 2019
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A method for the inference of cytokine interaction networks
Joanneke E Jansen1,2,3, Dominik Aschenbrenner2,4,5, Holm H Uhlig2,4,6
1Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom.
Plos Computational Biology
|June 22, 2022
Summary
This study introduces a new method to map cytokine interactions, crucial for understanding inflammatory diseases. The approach identifies key cytokine drivers for targeted therapies in immune-mediated inflammatory diseases.
Area of Science:
- Immunology and Systems Biology
- Computational Biology and Bioinformatics
Background:
- Cell-cell communication involves numerous cytokines, which are key therapeutic targets for immune-mediated inflammatory diseases (IMIDs).
- Identifying causative inflammatory drivers, not just associated factors, is vital for selecting effective clinical trial targets.
- In vitro models are essential for simplifying and experimentally probing complex in vivo cytokine interactions.
Purpose of the Study:
- To present a novel method for inferring minimal, weighted cytokine interaction networks from experimental perturbation data.
- To overcome limitations of existing methods that struggle with non-linear and highly connected biological data.
- To enable the study of indirect interactions, quantify inhibition effects, and predict combined cytokine inhibition outcomes.
Main Methods:
- Utilized ordinary differential equation (ODE) systems to model cytokine interactions.
- Employed an efficient computation of the lowest Akaike information criterion (AIC) across all network configurations.
- Applied the method to in vitro data measuring cytokine secretion changes after single cytokine receptor blockade.
Main Results:
- Successfully inferred minimal, weighted cytokine interaction networks.
- Enabled the analysis of indirect cytokine interactions and quantified inhibition effects.
- Demonstrated the ability to predict combined cytokine inhibition effects, validated with synthetic and experimental data for IL23.
Conclusions:
- The developed method efficiently infers cytokine interaction networks from perturbation data, specifically for IMIDs.
- This approach aids in identifying crucial cytokine targets for therapeutic intervention in inflammatory diseases.
- The model is adaptable for more complex dynamics and temporal data, offering a versatile tool for immunological research.
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