Data-Driven Flow-Map Models for Data-Efficient Discovery of Dynamics and Fast Uncertainty Quantification of Biological and Biochemical Systems Article Swipe
Georgios Makrygiorgos
,
Aaron J. Berliner
,
Fengzhe Shi
,
Douglas S. Clark
,
Adam P. Arkin
,
Ali Mesbah
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.02.19.481146
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.02.19.481146
Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-toevaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a co-culture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.
Related Topics
Concepts
Computer science
Dynamical systems theory
Uncertainty quantification
System dynamics
Polynomial chaos
Black box
Benchmark (surveying)
Forcing (mathematics)
Propagation of uncertainty
Data mining
Biological system
Machine learning
Artificial intelligence
Algorithm
Mathematics
Monte Carlo method
Statistics
Mathematical analysis
Geography
Biology
Geodesy
Physics
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.02.19.481146
- https://www.biorxiv.org/content/biorxiv/early/2022/02/22/2022.02.19.481146.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214595693
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214595693Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2022.02.19.481146Digital Object Identifier
- Title
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Data-Driven Flow-Map Models for Data-Efficient Discovery of Dynamics and Fast Uncertainty Quantification of Biological and Biochemical SystemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-22Full publication date if available
- Authors
-
Georgios Makrygiorgos, Aaron J. Berliner, Fengzhe Shi, Douglas S. Clark, Adam P. Arkin, Ali MesbahList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.02.19.481146Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/02/22/2022.02.19.481146.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2022/02/22/2022.02.19.481146.full.pdfDirect OA link when available
- Concepts
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Computer science, Dynamical systems theory, Uncertainty quantification, System dynamics, Polynomial chaos, Black box, Benchmark (surveying), Forcing (mathematics), Propagation of uncertainty, Data mining, Biological system, Machine learning, Artificial intelligence, Algorithm, Mathematics, Monte Carlo method, Statistics, Mathematical analysis, Geography, Biology, Geodesy, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1Per-year citation counts (last 5 years)
- References (count)
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77Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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