Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations Article Swipe
Jiancheng Zhang
,
Xiangting Li
,
Xiaolu Guo
,
Z. Y. You
,
Lucas Böttcher
,
Alex Mogilner
,
A. C. Abusleme Hoffman
,
Tom Chou
,
Mingtao Xia
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.11.642678
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.11.642678
Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because of (i) inherent stochasticity of intracellular biochemical reactions (“intrinsic noise”) and (ii) heterogeneity of cellular states across different cells that are influenced by external factors (“extrinsic noise”). In this work, we introduce an extrinsic-noise-driven neural stochastic differential equation (END-nSDE) framework that utilizes the Wasserstein distance to accurately reconstruct SDEs from trajectory data from a heterogeneous population of cells (extrinsic noise). We demonstrate the effectiveness of our approach using both simulated and experimental data from three different systems in cell biology: (i) circadian rhythms, (ii) RPA-DNA binding dynamics, and (iii) NF κ B signaling process. Our END-nSDE reconstruction method can model how cellular heterogeneity (extrinsic noise) modulates reaction dynamics in the presence of intrinsic noise. It also outperforms existing time-series analysis methods such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). By inferring cellular heterogeneities from data, our END-nSDE reconstruction method can reproduce noisy dynamics observed in experiments. In summary, the reconstruction method we propose offers a useful surrogate modeling approach for complex biophysical processes, where high-fidelity mechanistic models may be impractical.
Related Topics
Concepts
Stochastic differential equation
Dynamics (music)
Noise (video)
Stochastic dynamics
Artificial neural network
Computer science
Stochastic partial differential equation
Differential equation
Statistical physics
Control theory (sociology)
Mathematics
Applied mathematics
Artificial intelligence
Physics
Mathematical analysis
Acoustics
Control (management)
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.03.11.642678
- https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.11.642678.full.pdf
- OA Status
- green
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408420575
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4408420575Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2025.03.11.642678Digital Object Identifier
- Title
-
Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential EquationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-13Full publication date if available
- Authors
-
Jiancheng Zhang, Xiangting Li, Xiaolu Guo, Z. Y. You, Lucas Böttcher, Alex Mogilner, A. C. Abusleme Hoffman, Tom Chou, Mingtao XiaList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.03.11.642678Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.11.642678.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.11.642678.full.pdfDirect OA link when available
- Concepts
-
Stochastic differential equation, Dynamics (music), Noise (video), Stochastic dynamics, Artificial neural network, Computer science, Stochastic partial differential equation, Differential equation, Statistical physics, Control theory (sociology), Mathematics, Applied mathematics, Artificial intelligence, Physics, Mathematical analysis, Acoustics, Control (management), Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.By | 162 |
| abstract_inverted_index.In | 56, 179 |
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| abstract_inverted_index.NF | 118 |
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| abstract_inverted_index.by | 51 |
| abstract_inverted_index.in | 22, 106, 137, 177 |
| abstract_inverted_index.is | 8, 25 |
| abstract_inverted_index.of | 2, 29, 33, 42, 85, 93, 140 |
| abstract_inverted_index.to | 17, 74 |
| abstract_inverted_index.we | 59, 184 |
| abstract_inverted_index.κ | 119 |
| abstract_inverted_index.(i) | 30, 109 |
| abstract_inverted_index.Our | 123 |
| abstract_inverted_index.and | 5, 15, 39, 99, 116, 156 |
| abstract_inverted_index.are | 49 |
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| abstract_inverted_index.may | 200 |
| abstract_inverted_index.our | 94, 168 |
| abstract_inverted_index.the | 71, 91, 138, 181 |
| abstract_inverted_index.(ii) | 40, 112 |
| abstract_inverted_index.SDEs | 77 |
| abstract_inverted_index.also | 144 |
| abstract_inverted_index.both | 97 |
| abstract_inverted_index.cell | 3, 23, 107 |
| abstract_inverted_index.data | 80, 101 |
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| abstract_inverted_index.gene | 6 |
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| abstract_inverted_index.data, | 167 |
| abstract_inverted_index.model | 128 |
| abstract_inverted_index.noisy | 27, 174 |
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| abstract_inverted_index.(RNNs) | 155 |
| abstract_inverted_index.Proper | 0 |
| abstract_inverted_index.across | 45 |
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| abstract_inverted_index.offers | 186 |
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| abstract_inverted_index.systems | 105 |
| abstract_inverted_index.(LSTMs). | 161 |
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| abstract_inverted_index.approach | 95, 191 |
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| abstract_inverted_index.cellular | 12, 43, 130, 164 |
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| abstract_inverted_index.different | 46, 104 |
| abstract_inverted_index.dynamics, | 115 |
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| abstract_inverted_index.function, | 13 |
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| abstract_inverted_index.intrinsic | 141 |
| abstract_inverted_index.introduce | 60 |
| abstract_inverted_index.modulates | 134 |
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| abstract_inverted_index.reproduce | 173 |
| abstract_inverted_index.signaling | 4, 121 |
| abstract_inverted_index.simulated | 98 |
| abstract_inverted_index.surrogate | 189 |
| abstract_inverted_index.(END-nSDE) | 67 |
| abstract_inverted_index.(extrinsic | 87, 132 |
| abstract_inverted_index.accurately | 75 |
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| abstract_inverted_index.population | 84 |
| abstract_inverted_index.processes, | 195 |
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| abstract_inverted_index.reconstruct | 76 |
| abstract_inverted_index.time-series | 147 |
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| abstract_inverted_index.experimental | 100 |
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| abstract_inverted_index.(“intrinsic | 37 |
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| abstract_inverted_index.environmental | 18 |
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| abstract_inverted_index.heterogeneous | 83 |
| abstract_inverted_index.high-fidelity | 197 |
| abstract_inverted_index.intracellular | 34 |
| abstract_inverted_index.stochasticity | 32 |
| abstract_inverted_index.reconstruction | 125, 170, 182 |
| abstract_inverted_index.heterogeneities | 165 |
| abstract_inverted_index.extrinsic-noise-driven | 62 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.07360454 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |