Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks

We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g. protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies to understand the 'extrinsic' source...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org
Main Authors: Bravi, Barbara, Rubin, Katy J, Sollich, Peter
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 01.07.2020
Subjects:
Biochemistry
Complex formation
Growth factors
Model reduction
Noise
Noise propagation
Proteins
ISSN:2331-8422
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g. protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies to understand the 'extrinsic' sources of stochasticity arising from the rest of the network. Our approaches are based on subnetwork dynamical equations derived by projection methods and by path integrals. The results provide a principled derivation of the different components of the extrinsic noise that is observed experimentally in cellular biochemical reactions, over and above the intrinsic noise from the stochasticity of biochemical events in the subnetwork. We explore several intermediate approximations to assess systematically the relative importance of different extrinsic noise components, including initial transients, long-time plateaus, temporal correlations, multiplicative noise terms and nonlinear noise propagation. The best approximations achieve excellent accuracy in quantitative tests on a simple protein network and on the epidermal growth factor receptor signalling network.
AbstractList We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g. protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies to understand the 'extrinsic' sources of stochasticity arising from the rest of the network. Our approaches are based on subnetwork dynamical equations derived by projection methods and by path integrals. The results provide a principled derivation of the different components of the extrinsic noise that is observed experimentally in cellular biochemical reactions, over and above the intrinsic noise from the stochasticity of biochemical events in the subnetwork. We explore several intermediate approximations to assess systematically the relative importance of different extrinsic noise components, including initial transients, long-time plateaus, temporal correlations, multiplicative noise terms and nonlinear noise propagation. The best approximations achieve excellent accuracy in quantitative tests on a simple protein network and on the epidermal growth factor receptor signalling network.
Author Sollich, Peter
Rubin, Katy J
Bravi, Barbara
Author_xml – sequence: 1
  givenname: Barbara
  surname: Bravi
  fullname: Bravi, Barbara
– sequence: 2
  givenname: Katy
  surname: Rubin
  middlename: J
  fullname: Rubin, Katy J
– sequence: 3
  givenname: Peter
  surname: Sollich
  fullname: Sollich, Peter
BookMark eNotjk1LAzEURYMoWGt_gLuA66mZfEzyllLUCgUXdm3JZF5oapvUSUbbf--Aru7m3HPvDbmMKSIhdzWbS6MUe7D9KXzPOWNizoxm8oJMuBB1ZSTn12SW844xxhvNlRIT8vF-zgUPtgRHD6nDPe2xG1wJKVJnj2XoMdOyRdqdoz0El2nyFE-lDzGPlZhCRhoibUNyWxwBu6d5aCOWn9R_5lty5e0-4-w_p2T9_LReLKvV28vr4nFVWcXrSnQeW6_H9062pgbVOsOUQKk7sIDgpQLGHShptAGmbIMj3IAH0B4aEFNy_6c99ulrwFw2uzT0cVzccKEBpNJQi18mv1gU
ContentType Paper
Copyright 2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 8FE
8FG
ABJCF
ABUWG
AFKRA
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
HCIFZ
L6V
M7S
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
DOI 10.48550/arxiv.2003.08704
DatabaseName ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest MSED
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials - QC
ProQuest Central
ProQuest Technology Collection
ProQuest One Community College
ProQuest Central Korea
SciTech Collection (ProQuest)
ProQuest Engineering Collection
Engineering Database
ProQuest Central Premium
ProQuest One Academic (New)
ProQuest Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
Engineering Collection
DatabaseTitle Publicly Available Content Database
Engineering Database
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Engineering Collection
ProQuest One Academic UKI Edition
ProQuest Central Korea
Materials Science & Engineering Collection
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
Engineering Collection
DatabaseTitleList Publicly Available Content Database
Database_xml – sequence: 1
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Physics
EISSN 2331-8422
Genre Working Paper/Pre-Print
GroupedDBID 8FE
8FG
ABJCF
ABUWG
AFKRA
ALMA_UNASSIGNED_HOLDINGS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
FRJ
HCIFZ
L6V
M7S
M~E
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
ID FETCH-LOGICAL-a521-3dfebf7550c4b8195bc8053e47d9a9e9f45902c954878905a6e75569f997f9693
IEDL.DBID BENPR
IngestDate Mon Jun 30 09:25:23 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a521-3dfebf7550c4b8195bc8053e47d9a9e9f45902c954878905a6e75569f997f9693
Notes SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
OpenAccessLink https://www.proquest.com/docview/2379945791?pq-origsite=%requestingapplication%
PQID 2379945791
PQPubID 2050157
ParticipantIDs proquest_journals_2379945791
PublicationCentury 2000
PublicationDate 20200701
PublicationDateYYYYMMDD 2020-07-01
PublicationDate_xml – month: 07
  year: 2020
  text: 20200701
  day: 01
PublicationDecade 2020
PublicationPlace Ithaca
PublicationPlace_xml – name: Ithaca
PublicationTitle arXiv.org
PublicationYear 2020
Publisher Cornell University Library, arXiv.org
Publisher_xml – name: Cornell University Library, arXiv.org
SSID ssj0002672553
Score 1.7269627
SecondaryResourceType preprint
Snippet We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g. protein interaction networks involving...
SourceID proquest
SourceType Aggregation Database
SubjectTerms Biochemistry
Complex formation
Growth factors
Model reduction
Noise
Noise propagation
Proteins
Title Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks
URI https://www.proquest.com/docview/2379945791
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV09T8MwELWgBYmJb_FRKg-spm3i2vGEBGoFElQR7VAWKvtiS1mSErcVPx_bTcuAxMJoxYqis-98uXt-D6HbxDDZBQ0EWAyEJn1NhIh6BKjssQiAUQiqJS98NEqmU5HWBTdbwyo3MTEE6qwEXyPvRDEXgva56N3PP4lXjfLd1VpCYxc1PVMZbaDmw2CUvm2rLBHjLmeO1-3MQN7VkdVXvgpEoHddt1npryAcTpbh4X-_6Qg1UznX1THa0cUJ2g-ITrCn6GO85WjGQe4GV56l1a8DBjn3jQOLXfaHs7UmvcWlwS5SV3nhXoCLMrca5wVWudfUCqQC2C5VsYaN2zM0GQ4mj0-kFlMg0p3QJM6MVoY7AwBVvnemIHH-pynPhBRaGOp5XMDTv_mrsX3JtJvMhBGCG8FEfI4aRVnoC4QViMylWd57GTWJJzyLtEqUzmLg0BOXqLWx1qx2CDv7MdXV34-v0UHkf2kDIraFGotqqW_QHqwWua3a9fq2PURz7Ebp82v6_g28SrTo
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V07T8MwELaqFgQTb_Eo4AHGUJq4djwgBqBq1Ycq0aEsRPbFkTKQlKQt9EfxH7HdpgxIbB2Yk1hy7vzd2Xf-PoSu_IiKW1DgAPXAIX5DOZy7dQeIqFMXgBKwqiVd1u_7oxEflNBXcRfGtFUWmGiBOkzBnJHXXI9xThqM1-_H745RjTLV1UJCY-EWHTX_0Fu2_K79qO177brNp-FDy1mqCjhChyrHCyMlI6YTcyDSFJEk-NoRFWEhF1zxiBhCEzA8aOaOaENQpV-mPOKcRZwa7iWN-BWifd0vo8qg3Ru8rA51XMp0iu4tqqeWK6wmss94ZnlHb2712iC_MN8GsubOP_sFu3rqYqyyPVRSyT7atP2qkB-g1-cVAzW2Yj44Mxy0xsswiLEpi-RY57Y4nCfiTX-C0wjrOJTFiR4AJ2mcKxwnWMZGMcxSJuB8KpNFU3x-iIbrmNERKidpoo4RlsBDnUQabKIk8g2dm6ukL1XoAYM6P0HVwjjBcrnnwY9lTv9-fIm2WsNeN-i2-50ztO2azbvt_a2i8iSbqnO0AbNJnGcXS9fCKFizJb8BtMwLwg
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Systematic+model+reduction+captures+the+dynamics+of+extrinsic+noise+in+biochemical+subnetworks&rft.jtitle=arXiv.org&rft.au=Bravi%2C+Barbara&rft.au=Rubin%2C+Katy+J&rft.au=Sollich%2C+Peter&rft.date=2020-07-01&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422&rft_id=info:doi/10.48550%2Farxiv.2003.08704