Derivation of stationary distributions of biochemical reaction networks via structure transformation
Long-term behaviors of biochemical reaction networks (BRNs) are described by steady states in deterministic models and stationary distributions in stochastic models. Unlike deterministic steady states, stationary distributions capturing inherent fluctuations of reactions are extremely difficult to derive analytically due to the curse of dimensionality. Here, we develop a method to derive analytic stationary distributions from deterministic steady states by transforming BRNs to have a special dynamic property, called complex balancing. Specifically, we merge nodes and edges of BRNs to match in- and out-flows of each node. This allows us to derive the stationary distributions of a large class of BRNs, including autophosphorylation networks of EGFR, PAK1, and Aurora B kinase and a genetic toggle switch. This reveals the unique properties of their stochastic dynamics such as robustness, sensitivity, and multi-modality. Importantly, we provide a user-friendly computational package, CASTANET, that automatically derives symbolic expressions of the stationary distributions of BRNs to understand their long-term stochasticity.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:4 |
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| Enthalten in: |
Communications biology - 4(2021), 1 vom: 24. Mai, Seite 620 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hong, Hyukpyo [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 09.08.2021 Date Revised 02.02.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s42003-021-02117-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM325807256 |
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| 100 | 1 | |a Hong, Hyukpyo |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Derivation of stationary distributions of biochemical reaction networks via structure transformation |
| 264 | 1 | |c 2021 | |
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| 500 | |a Date Completed 09.08.2021 | ||
| 500 | |a Date Revised 02.02.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Long-term behaviors of biochemical reaction networks (BRNs) are described by steady states in deterministic models and stationary distributions in stochastic models. Unlike deterministic steady states, stationary distributions capturing inherent fluctuations of reactions are extremely difficult to derive analytically due to the curse of dimensionality. Here, we develop a method to derive analytic stationary distributions from deterministic steady states by transforming BRNs to have a special dynamic property, called complex balancing. Specifically, we merge nodes and edges of BRNs to match in- and out-flows of each node. This allows us to derive the stationary distributions of a large class of BRNs, including autophosphorylation networks of EGFR, PAK1, and Aurora B kinase and a genetic toggle switch. This reveals the unique properties of their stochastic dynamics such as robustness, sensitivity, and multi-modality. Importantly, we provide a user-friendly computational package, CASTANET, that automatically derives symbolic expressions of the stationary distributions of BRNs to understand their long-term stochasticity | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 7 | |a EGFR protein, human |2 NLM | |
| 650 | 7 | |a EC 2.7.10.1 |2 NLM | |
| 650 | 7 | |a ErbB Receptors |2 NLM | |
| 650 | 7 | |a EC 2.7.10.1 |2 NLM | |
| 650 | 7 | |a AURKB protein, human |2 NLM | |
| 650 | 7 | |a EC 2.7.11.1 |2 NLM | |
| 650 | 7 | |a Aurora Kinase B |2 NLM | |
| 650 | 7 | |a EC 2.7.11.1 |2 NLM | |
| 650 | 7 | |a PAK1 protein, human |2 NLM | |
| 650 | 7 | |a EC 2.7.11.1 |2 NLM | |
| 650 | 7 | |a p21-Activated Kinases |2 NLM | |
| 650 | 7 | |a EC 2.7.11.1 |2 NLM | |
| 700 | 1 | |a Kim, Jinsu |e verfasserin |4 aut | |
| 700 | 1 | |a Ali Al-Radhawi, M |e verfasserin |4 aut | |
| 700 | 1 | |a Sontag, Eduardo D |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Jae Kyoung |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Communications biology |d 2018 |g 4(2021), 1 vom: 24. Mai, Seite 620 |w (DE-627)NLM284287245 |x 2399-3642 |7 nnns |
| 773 | 1 | 8 | |g volume:4 |g year:2021 |g number:1 |g day:24 |g month:05 |g pages:620 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s42003-021-02117-x |3 Volltext |
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