Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modelling Article Swipe
Tongli Zhang
,
John J. Tyson
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s10928-021-09798-1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s10928-021-09798-1
Individual biological organisms are characterized by daunting heterogeneity, which precludes describing or understanding populations of ‘patients’ with a single mathematical model. Recently, the field of quantitative systems pharmacology (QSP) has adopted the notion of virtual patients (VPs) to cope with this challenge. A typical population of VPs represents the behavior of a heterogeneous patient population with a distribution of parameter values over a mathematical model of fixed structure. Though this notion of VPs is a powerful tool to describe patients’ heterogeneity, the analysis and understanding of these VPs present new challenges to systems pharmacologists. Here, using a model of the hypothalamic–pituitary–adrenal axis, we show that an integrated pipeline that combines machine learning (ML) and bifurcation analysis can be used to effectively and efficiently analyse the behaviors observed in populations of VPs. Compared with local sensitivity analyses, ML allows us to capture and analyse the contributions of simultaneous changes of multiple model parameters. Following up with bifurcation analysis, we are able to provide rigorous mechanistic insight regarding the influences of ML-identified parameters on the dynamical system’s behaviors. In this work, we illustrate the utility of this pipeline and suggest that its wider adoption will facilitate the use of VPs in the practice of systems pharmacology.
Related Topics
Concepts
Computer science
Pipeline (software)
Dynamical systems theory
Bifurcation
Population
Machine learning
Artificial intelligence
Sensitivity (control systems)
Virtual patient
Nonlinear system
Medicine
Engineering
Programming language
Physics
Electronic engineering
Quantum mechanics
Medical education
Environmental health
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10928-021-09798-1
- https://link.springer.com/content/pdf/10.1007/s10928-021-09798-1.pdf
- OA Status
- hybrid
- Cited By
- 10
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4206429507
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4206429507Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10928-021-09798-1Digital Object Identifier
- Title
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Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modellingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-05Full publication date if available
- Authors
-
Tongli Zhang, John J. TysonList of authors in order
- Landing page
-
https://doi.org/10.1007/s10928-021-09798-1Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10928-021-09798-1.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10928-021-09798-1.pdfDirect OA link when available
- Concepts
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Computer science, Pipeline (software), Dynamical systems theory, Bifurcation, Population, Machine learning, Artificial intelligence, Sensitivity (control systems), Virtual patient, Nonlinear system, Medicine, Engineering, Programming language, Physics, Electronic engineering, Quantum mechanics, Medical education, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 4, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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