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Open AccessArticle

TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation

Computational Systems Biology, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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These authors are equally contributed.
Entropy 2020, 22(9), 1030; https://doi.org/10.3390/e22091030
Received: 16 August 2020 / Revised: 7 September 2020 / Accepted: 11 September 2020 / Published: 15 September 2020
(This article belongs to the Special Issue Thermodynamics and Information Theory of Living Systems)
The objective of gene set enrichment analysis (GSEA) in modern biological studies is to identify functional profiles in huge sets of biomolecules generated by high-throughput measurements of genes, transcripts, metabolites, and proteins. GSEA is based on a two-stage process using classical statistical analysis to score the input data and subsequent testing for overrepresentation of the enrichment score within a given functional coherent set. However, enrichment scores computed by different methods are merely statistically motivated and often elusive to direct biological interpretation. Here, we propose a novel approach, called Thermodynamically Motivated Enrichment Analysis (TMEA), to account for the energy investment in biological relevant processes. Therefore, TMEA is based on surprisal analysis, which offers a thermodynamic-free energy-based representation of the biological steady state and of the biological change. The contribution of each biomolecule underlying the changes in free energy is used in a Monte Carlo resampling procedure resulting in a functional characterization directly coupled to the thermodynamic characterization of biological responses to system perturbations. To illustrate the utility of our method on real experimental data, we benchmark our approach on plant acclimation to high light and compare the performance of TMEA with the most frequently used method for GSEA. View Full-Text
Keywords: GSEA; gene set enrichment analysis; pathway analysis; surprisal analysis; information theory; thermodynamics; free energy; acclimation response; transcription levels GSEA; gene set enrichment analysis; pathway analysis; surprisal analysis; information theory; thermodynamics; free energy; acclimation response; transcription levels
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MDPI and ACS Style

Schneider, K.; Venn, B.; Mühlhaus, T. TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation. Entropy 2020, 22, 1030.

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