Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks.
Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear inter...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010183 |
| _version_ | 1817970526468964352 |
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| author | Timon Wittenstein Nava Leibovich Andreas Hilfinger |
| author_facet | Timon Wittenstein Nava Leibovich Andreas Hilfinger |
| author_sort | Timon Wittenstein |
| collection | DOAJ |
| description | Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations. |
| first_indexed | 2024-04-13T20:35:28Z |
| format | Article |
| id | doaj.art-71ffb20099dc42b6b6b356377245f6f6 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-13T20:35:28Z |
| publishDate | 2022-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-71ffb20099dc42b6b6b356377245f6f62022-12-22T02:31:03ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-06-01186e101018310.1371/journal.pcbi.1010183Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks.Timon WittensteinNava LeibovichAndreas HilfingerQuantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations.https://doi.org/10.1371/journal.pcbi.1010183 |
| spellingShingle | Timon Wittenstein Nava Leibovich Andreas Hilfinger Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks. PLoS Computational Biology |
| title | Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks. |
| title_full | Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks. |
| title_fullStr | Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks. |
| title_full_unstemmed | Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks. |
| title_short | Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks. |
| title_sort | quantifying biochemical reaction rates from static population variability within incompletely observed complex networks |
| url | https://doi.org/10.1371/journal.pcbi.1010183 |
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