Data-Driven Flow-Map Models for Data-Efficient Discovery of Dynamics and Fast Uncertainty Quantification of Biological and Biochemical Systems Article Swipe
Ali Mesbah
,
Georgios Makrygiorgos
,
Aaron J. Berliner
,
Fengzhe Shi
,
Douglas B. Clark
,
Adam P. Arkin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.22541/au.164873215.58987330/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.22541/au.164873215.58987330/v1
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-to-evaluate 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.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.22541/au.164873215.58987330/v1
- https://radiopharmaconnect.srsweb.org/doi/pdf/10.22541/au.164873215.58987330
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226296409
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https://openalex.org/W4226296409Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22541/au.164873215.58987330/v1Digital Object Identifier
- Title
-
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
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2022Year of publication
- Publication date
-
2022-03-31Full publication date if available
- Authors
-
Ali Mesbah, Georgios Makrygiorgos, Aaron J. Berliner, Fengzhe Shi, Douglas B. Clark, Adam P. ArkinList of authors in order
- Landing page
-
https://doi.org/10.22541/au.164873215.58987330/v1Publisher landing page
- PDF URL
-
https://radiopharmaconnect.srsweb.org/doi/pdf/10.22541/au.164873215.58987330Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://radiopharmaconnect.srsweb.org/doi/pdf/10.22541/au.164873215.58987330Direct OA link when available
- Concepts
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Dynamical systems theory, Computer science, Uncertainty quantification, Black box, System dynamics, Benchmark (surveying), Polynomial chaos, Machine learning, Data mining, Artificial intelligence, Mathematics, Geography, Monte Carlo method, Physics, Quantum mechanics, Geodesy, StatisticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.used | 4 |
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| abstract_inverted_index.when | 48 |
| abstract_inverted_index.based | 170 |
| abstract_inverted_index.chaos | 173 |
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| abstract_inverted_index.from, | 37 |
| abstract_inverted_index.hand, | 47 |
| abstract_inverted_index.learn | 110 |
| abstract_inverted_index.model | 61 |
| abstract_inverted_index.often | 38 |
| abstract_inverted_index.other | 46 |
| abstract_inverted_index.paper | 73 |
| abstract_inverted_index.serve | 134 |
| abstract_inverted_index.such, | 131 |
| abstract_inverted_index.task. | 71 |
| abstract_inverted_index.these | 89 |
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| abstract_inverted_index.would | 30 |
| abstract_inverted_index.design | 220 |
| abstract_inverted_index.known, | 28 |
| abstract_inverted_index.models | 1, 50, 94, 106, 146, 209 |
| abstract_inverted_index.noisy, | 41 |
| abstract_inverted_index.notion | 76 |
| abstract_inverted_index.system | 23, 34, 148, 156 |
| abstract_inverted_index.useful | 95 |
| abstract_inverted_index.(fully) | 27 |
| abstract_inverted_index.address | 86 |
| abstract_inverted_index.arduous | 70 |
| abstract_inverted_index.complex | 10 |
| abstract_inverted_index.effects | 59 |
| abstract_inverted_index.limited | 39 |
| abstract_inverted_index.manner, | 122 |
| abstract_inverted_index.predict | 8 |
| abstract_inverted_index.present | 81, 162 |
| abstract_inverted_index.subject | 192 |
| abstract_inverted_index.systems | 187, 214 |
| abstract_inverted_index.various | 185 |
| abstract_inverted_index.Kriging. | 174 |
| abstract_inverted_index.analyses | 211 |
| abstract_inverted_index.approach | 138, 165, 176 |
| abstract_inverted_index.behavior | 100 |
| abstract_inverted_index.deriving | 140 |
| abstract_inverted_index.directly | 36, 109 |
| abstract_inverted_index.dynamics | 11, 35, 157, 183 |
| abstract_inverted_index.external | 194 |
| abstract_inverted_index.flexible | 137 |
| abstract_inverted_index.flow-map | 78, 105, 168 |
| abstract_inverted_index.forcing, | 195 |
| abstract_inverted_index.interest | 66 |
| abstract_inverted_index.learning | 32, 92 |
| abstract_inverted_index.modeling | 169 |
| abstract_inverted_index.observed | 42 |
| abstract_inverted_index.reactor. | 206 |
| abstract_inverted_index.systems. | 16, 103, 228 |
| abstract_inverted_index.benchmark | 186 |
| abstract_inverted_index.black-box | 121 |
| abstract_inverted_index.capturing | 97 |
| abstract_inverted_index.discovery | 180 |
| abstract_inverted_index.dynamical | 99, 213 |
| abstract_inverted_index.dynamics, | 149 |
| abstract_inverted_index.efficient | 55 |
| abstract_inverted_index.equations | 19, 118 |
| abstract_inverted_index.expensive | 49, 144 |
| abstract_inverted_index.framework | 83 |
| abstract_inverted_index.governing | 18, 116 |
| abstract_inverted_index.leverages | 74 |
| abstract_inverted_index.long-term | 155 |
| abstract_inverted_index.microbial | 204 |
| abstract_inverted_index.operators | 113 |
| abstract_inverted_index.paramount | 217 |
| abstract_inverted_index.structure | 125 |
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| abstract_inverted_index.biological | 13 |
| abstract_inverted_index.bioreactor | 191 |
| abstract_inverted_index.challenges | 90 |
| abstract_inverted_index.co-culture | 190 |
| abstract_inverted_index.equations. | 129 |
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| abstract_inverted_index.surrogates | 142 |
| abstract_inverted_index.systematic | 53 |
| abstract_inverted_index.underlying | 128 |
| abstract_inverted_index.Data-driven | 104 |
| abstract_inverted_index.biochemical | 15, 22, 102 |
| abstract_inverted_index.data-driven | 93, 167, 208 |
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| abstract_inverted_index.investigate | 6 |
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| abstract_inverted_index.bioprocesses | 224 |
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| abstract_inverted_index.alternatively, | 151 |
| abstract_inverted_index.data-efficient | 164 |
| abstract_inverted_index.quantification | 56, 201 |
| abstract_inverted_index.reconstructing | 153 |
| abstract_inverted_index.(de)compositions | 79 |
| abstract_inverted_index.biomanufacturing | 227 |
| abstract_inverted_index.electrosynthesis | 205 |
| abstract_inverted_index.fast-to-evaluate | 141 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.04777573 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |