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What Can We Learn from Global Sensitivity Analysis of Biochemical Systems?

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Kent, Edward, Neumann, Stefan, Kummer, Ursula, Mendes, Pedro·2013·PLoS ONE
DOI 10.1371/journal.pone.0079244PMC PMC3828278
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Abstract

Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique.

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  • Sensitivity analysis has become instrumental in the systems pharmacology arena to guide the understanding and development of a complex model.

    “it has become instrumental in the systems pharmacology arena to guide the understanding and development of a complex model.25, 26, 27”

    From Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models (2015) · cites this paper as [25]

    The Introduction (page unavailable) says sensitivity analysis assesses model robustness and can provide insights into biochemical models; the Discussion (page unavailable) says global analysis reveals model behaviour. These passages support its use for understanding complex models, but do not establish that it has become instrumental specifically in systems pharmacology or that it guides model development. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
Kent, E., Neumann, S., Kummer, U., & Mendes, P.
Affiliations
  • University of Manchester (GB)
  • Heidelberg University (DE)
Corresponding author
Mendes, Pedro
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2013-11-14
Volume
8
Issue
11
Pages
e79244
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0079244
PMC
PMC3828278
OpenAlex
W2013591251
Cited by
44
References
2
Citations by year
2026: 3 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 4 · 2021: 3 · 2020: 5 · 2019: 5 · 2018: 1 · 2017: 5 · 2016: 5 · 2015: 6 · 2014: 1
FWCI
1.79
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Explorer (The University of Manchester) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • VTechWorks (Virginia Tech) (submittedVersion, cc-by)
Funders
  • Pfizer
  • University of Manchester
  • European Commission (289434)
  • Klaus Tschira Stiftung
  • Directorate for Biological Sciences
  • Biotechnology and Biological Sciences Research Council (BB/G529859, BB/J019259, BB/J019259/1)
  • National Institute of General Medical Sciences (R01GM080219, GM080219)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Probabilistic and Robust Engineering Design
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords
global sensitivity analysis, biochemical systems, parameter uncertainty, model robustness, sensitivity coefficients, random sampling, model reliability, signalling model, metabolic modeling, parameter identifiability, multimodal distributions
MeSH terms
Models, Biological, Metabolome

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