Extending Combinatorial Regulatory Network Modeling to Include Activity Control and Decay Modulation

Bree Cummins; Marcio Gameiro; Tomas Gedeon; Shane Kepley; Konstantin Mischaikow; Lun Zhang

doi:10.1137/21M1456832

Extending Combinatorial Regulatory Network Modeling to Include Activity Control and Decay Modulation

Bree Cummins, Marcio Gameiro, Tomas Gedeon, Shane Kepley, Konstantin Mischaikow, Lun Zhang

Mathematics

Research output: Contribution to Journal › Article › Academic › peer-review

36 Downloads (Pure)

Abstract

Understanding how the structure of within-system interactions affects the dynamics of the system is important in many areas of science. We extend a network dynamics modeling platform DSGRN, which combinatorializes both dynamics and parameter space to construct finite but accurate summaries of network dynamics, to new types of interactions. While the standard DSGRN assumes that each network edge controls the rate of abundance of the target node, the new edges may control either activity level or a decay rate of its target. While motivated by processes of post-transcriptional modification and ubiquitination in systems biology, our extension is applicable to the dynamics of any signed directed network.

Original language	English
Pages (from-to)	2096-2125
Number of pages	30
Journal	SIAM Journal on Applied Dynamical Systems
Volume	21
Issue number	3
Early online date	11 Aug 2022
DOIs	https://doi.org/10.1137/21M1456832
Publication status	Published - 2022

Bibliographical note

Funding Information:
∗Received by the editors November 2, 2021; accepted for publication (in revised form) by T. Wanner May 3, 2022; published electronically August 11, 2022. https://doi.org/10.1137/21M1456832 Funding: The work of the first and third authors was partially supported by NSF grant DMS-1839299, DARPA FA8750-17-C-0054, and NIH 5R01GM126555-01. The work of the second, fourth, fifth, and sixth authors was partially supported by NSF grant DMS-1839294 and HDR TRIPODS award CCF-1934924, DARPA contract HR0011-16-2-0033, and National Institutes of Health award R01 GM126555. The work of the third author was also partially supported by FAPESP grant 2019/06249-7 and by CNPq grant 309073/2019-7. The work of the fifth author was also supported by a grant from the Simons Foundation. †Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717 USA (breschine. [email protected], [email protected]). ‡Department of Mathematics, Rutgers University, Piscataway, NJ 08854 USA, and Instituto de Ciências Matemáticas e de Computac¸ão, Universidade de São Paulo, São Carlos, São Paulo, Brazil (gameiro@math. rutgers.edu). §Department of Mathematics, VU Amsterdam, 1081 HV Amsterdam, The Netherlands ([email protected]). ¶Department of Mathematics, Rutgers University, Piscataway, NJ 08854 USA ([email protected], [email protected]).

Publisher Copyright:
© 2022 Society for Industrial and Applied Mathematics.

Funding

∗Received by the editors November 2, 2021; accepted for publication (in revised form) by T. Wanner May 3, 2022; published electronically August 11, 2022. https://doi.org/10.1137/21M1456832 Funding: The work of the first and third authors was partially supported by NSF grant DMS-1839299, DARPA FA8750-17-C-0054, and NIH 5R01GM126555-01. The work of the second, fourth, fifth, and sixth authors was partially supported by NSF grant DMS-1839294 and HDR TRIPODS award CCF-1934924, DARPA contract HR0011-16-2-0033, and National Institutes of Health award R01 GM126555. The work of the third author was also partially supported by FAPESP grant 2019/06249-7 and by CNPq grant 309073/2019-7. The work of the fifth author was also supported by a grant from the Simons Foundation. †Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717 USA (breschine. [email protected], [email protected]). ‡Department of Mathematics, Rutgers University, Piscataway, NJ 08854 USA, and Instituto de Ciências Matemáticas e de Computac¸ão, Universidade de São Paulo, São Carlos, São Paulo, Brazil (gameiro@math. rutgers.edu). §Department of Mathematics, VU Amsterdam, 1081 HV Amsterdam, The Netherlands ([email protected]). ¶Department of Mathematics, Rutgers University, Piscataway, NJ 08854 USA ([email protected], [email protected]).

Funders	Funder number
Simons Foundation
National Institutes of Health	CCF-1934924, DMS-1839294, HR0011-16-2-0033, R01 GM126555
Defense Advanced Research Projects Agency	FA8750-17-C-0054
Conselho Nacional de Desenvolvimento Científico e Tecnológico	309073/2019-7
National Science Foundation	DMS-1839299
Fundação de Amparo à Pesquisa do Estado de São Paulo	2019/06249-7

Keywords

gene regulation
mathematical biology
network dynamics

Access to Document

10.1137/21M1456832

Extending Combinatorial Regulatory Network Modeling to Include Activity Control and Decay ModulationFinal published version, 470 KBLicence: Article 25fa Dutch Copyright Act

Persistent URL (handle)

Persistent link

Cite this

@article{3b9b2315d10d44ed82d12d8eab3612ff,

title = "Extending Combinatorial Regulatory Network Modeling to Include Activity Control and Decay Modulation",

abstract = "Understanding how the structure of within-system interactions affects the dynamics of the system is important in many areas of science. We extend a network dynamics modeling platform DSGRN, which combinatorializes both dynamics and parameter space to construct finite but accurate summaries of network dynamics, to new types of interactions. While the standard DSGRN assumes that each network edge controls the rate of abundance of the target node, the new edges may control either activity level or a decay rate of its target. While motivated by processes of post-transcriptional modification and ubiquitination in systems biology, our extension is applicable to the dynamics of any signed directed network. ",

keywords = "gene regulation, mathematical biology, network dynamics",

author = "Bree Cummins and Marcio Gameiro and Tomas Gedeon and Shane Kepley and Konstantin Mischaikow and Lun Zhang",

note = "Funding Information: ∗Received by the editors November 2, 2021; accepted for publication (in revised form) by T. Wanner May 3, 2022; published electronically August 11, 2022. https://doi.org/10.1137/21M1456832 Funding: The work of the first and third authors was partially supported by NSF grant DMS-1839299, DARPA FA8750-17-C-0054, and NIH 5R01GM126555-01. The work of the second, fourth, fifth, and sixth authors was partially supported by NSF grant DMS-1839294 and HDR TRIPODS award CCF-1934924, DARPA contract HR0011-16-2-0033, and National Institutes of Health award R01 GM126555. The work of the third author was also partially supported by FAPESP grant 2019/06249-7 and by CNPq grant 309073/2019-7. The work of the fifth author was also supported by a grant from the Simons Foundation. †Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717 USA (breschine. [email protected], [email protected]). ‡Department of Mathematics, Rutgers University, Piscataway, NJ 08854 USA, and Instituto de Ci{\^e}ncias Matem{\'a}ticas e de Computac¸{\~a}o, Universidade de S{\~a}o Paulo, S{\~a}o Carlos, S{\~a}o Paulo, Brazil (gameiro@math. rutgers.edu). §Department of Mathematics, VU Amsterdam, 1081 HV Amsterdam, The Netherlands ([email protected]). ¶Department of Mathematics, Rutgers University, Piscataway, NJ 08854 USA ([email protected], [email protected]). Publisher Copyright: {\textcopyright} 2022 Society for Industrial and Applied Mathematics.",

year = "2022",

doi = "10.1137/21M1456832",

language = "English",

volume = "21",

pages = "2096--2125",

journal = "SIAM Journal on Applied Dynamical Systems",

issn = "1536-0040",

publisher = "Society for Industrial and Applied Mathematics Publications",

number = "3",

}

TY - JOUR

T1 - Extending Combinatorial Regulatory Network Modeling to Include Activity Control and Decay Modulation

AU - Cummins, Bree

AU - Gameiro, Marcio

AU - Gedeon, Tomas

AU - Kepley, Shane

AU - Mischaikow, Konstantin

AU - Zhang, Lun

N1 - Funding Information: ∗Received by the editors November 2, 2021; accepted for publication (in revised form) by T. Wanner May 3, 2022; published electronically August 11, 2022. https://doi.org/10.1137/21M1456832 Funding: The work of the first and third authors was partially supported by NSF grant DMS-1839299, DARPA FA8750-17-C-0054, and NIH 5R01GM126555-01. The work of the second, fourth, fifth, and sixth authors was partially supported by NSF grant DMS-1839294 and HDR TRIPODS award CCF-1934924, DARPA contract HR0011-16-2-0033, and National Institutes of Health award R01 GM126555. The work of the third author was also partially supported by FAPESP grant 2019/06249-7 and by CNPq grant 309073/2019-7. The work of the fifth author was also supported by a grant from the Simons Foundation. †Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717 USA (breschine. [email protected], [email protected]). ‡Department of Mathematics, Rutgers University, Piscataway, NJ 08854 USA, and Instituto de Ciências Matemáticas e de Computac¸ão, Universidade de São Paulo, São Carlos, São Paulo, Brazil (gameiro@math. rutgers.edu). §Department of Mathematics, VU Amsterdam, 1081 HV Amsterdam, The Netherlands ([email protected]). ¶Department of Mathematics, Rutgers University, Piscataway, NJ 08854 USA ([email protected], [email protected]). Publisher Copyright: © 2022 Society for Industrial and Applied Mathematics.

PY - 2022

Y1 - 2022

N2 - Understanding how the structure of within-system interactions affects the dynamics of the system is important in many areas of science. We extend a network dynamics modeling platform DSGRN, which combinatorializes both dynamics and parameter space to construct finite but accurate summaries of network dynamics, to new types of interactions. While the standard DSGRN assumes that each network edge controls the rate of abundance of the target node, the new edges may control either activity level or a decay rate of its target. While motivated by processes of post-transcriptional modification and ubiquitination in systems biology, our extension is applicable to the dynamics of any signed directed network.

AB - Understanding how the structure of within-system interactions affects the dynamics of the system is important in many areas of science. We extend a network dynamics modeling platform DSGRN, which combinatorializes both dynamics and parameter space to construct finite but accurate summaries of network dynamics, to new types of interactions. While the standard DSGRN assumes that each network edge controls the rate of abundance of the target node, the new edges may control either activity level or a decay rate of its target. While motivated by processes of post-transcriptional modification and ubiquitination in systems biology, our extension is applicable to the dynamics of any signed directed network.

KW - gene regulation

KW - mathematical biology

KW - network dynamics

UR - http://www.scopus.com/inward/record.url?scp=85138453413&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85138453413&partnerID=8YFLogxK

U2 - 10.1137/21M1456832

DO - 10.1137/21M1456832

M3 - Article

AN - SCOPUS:85138453413

SN - 1536-0040

VL - 21

SP - 2096

EP - 2125

JO - SIAM Journal on Applied Dynamical Systems

JF - SIAM Journal on Applied Dynamical Systems

IS - 3

ER -

Extending Combinatorial Regulatory Network Modeling to Include Activity Control and Decay Modulation

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Cite this