Control of multilayer biological networks and applied to target identification of complex diseases

Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-lay...
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Autor*in:	Wei Zheng [verfasserIn] Dingjie Wang [verfasserIn] Xiufen Zou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Multilayer networks Network control Nonlinear dynamical systems IISG algorithm Driver nodes

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 20(2019), 1, Seite 12
Übergeordnetes Werk:	volume:20 ; year:2019 ; number:1 ; pages:12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12859-019-2841-2

Katalog-ID:	DOAJ05395470X

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520			\|a Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases.
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allfields	10.1186/s12859-019-2841-2 doi (DE-627)DOAJ05395470X (DE-599)DOAJdc3fa63b35f548e0922ea518afe0095e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Wei Zheng verfasserin aut Control of multilayer biological networks and applied to target identification of complex diseases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases. Multilayer networks Network control Nonlinear dynamical systems IISG algorithm Driver nodes Computer applications to medicine. Medical informatics Biology (General) Dingjie Wang verfasserin aut Xiufen Zou verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 12 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:12 https://doi.org/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/article/dc3fa63b35f548e0922ea518afe0095e kostenfrei http://link.springer.com/article/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 12
spelling	10.1186/s12859-019-2841-2 doi (DE-627)DOAJ05395470X (DE-599)DOAJdc3fa63b35f548e0922ea518afe0095e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Wei Zheng verfasserin aut Control of multilayer biological networks and applied to target identification of complex diseases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases. Multilayer networks Network control Nonlinear dynamical systems IISG algorithm Driver nodes Computer applications to medicine. Medical informatics Biology (General) Dingjie Wang verfasserin aut Xiufen Zou verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 12 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:12 https://doi.org/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/article/dc3fa63b35f548e0922ea518afe0095e kostenfrei http://link.springer.com/article/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 12
allfields_unstemmed	10.1186/s12859-019-2841-2 doi (DE-627)DOAJ05395470X (DE-599)DOAJdc3fa63b35f548e0922ea518afe0095e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Wei Zheng verfasserin aut Control of multilayer biological networks and applied to target identification of complex diseases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases. Multilayer networks Network control Nonlinear dynamical systems IISG algorithm Driver nodes Computer applications to medicine. Medical informatics Biology (General) Dingjie Wang verfasserin aut Xiufen Zou verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 12 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:12 https://doi.org/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/article/dc3fa63b35f548e0922ea518afe0095e kostenfrei http://link.springer.com/article/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 12
allfieldsGer	10.1186/s12859-019-2841-2 doi (DE-627)DOAJ05395470X (DE-599)DOAJdc3fa63b35f548e0922ea518afe0095e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Wei Zheng verfasserin aut Control of multilayer biological networks and applied to target identification of complex diseases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases. Multilayer networks Network control Nonlinear dynamical systems IISG algorithm Driver nodes Computer applications to medicine. Medical informatics Biology (General) Dingjie Wang verfasserin aut Xiufen Zou verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 12 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:12 https://doi.org/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/article/dc3fa63b35f548e0922ea518afe0095e kostenfrei http://link.springer.com/article/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 12
allfieldsSound	10.1186/s12859-019-2841-2 doi (DE-627)DOAJ05395470X (DE-599)DOAJdc3fa63b35f548e0922ea518afe0095e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Wei Zheng verfasserin aut Control of multilayer biological networks and applied to target identification of complex diseases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases. Multilayer networks Network control Nonlinear dynamical systems IISG algorithm Driver nodes Computer applications to medicine. Medical informatics Biology (General) Dingjie Wang verfasserin aut Xiufen Zou verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 12 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:12 https://doi.org/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/article/dc3fa63b35f548e0922ea518afe0095e kostenfrei http://link.springer.com/article/10.1186/s12859-019-2841-2 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 12
language	English
source	In BMC Bioinformatics 20(2019), 1, Seite 12 volume:20 year:2019 number:1 pages:12
sourceStr	In BMC Bioinformatics 20(2019), 1, Seite 12 volume:20 year:2019 number:1 pages:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multilayer networks Network control Nonlinear dynamical systems IISG algorithm Driver nodes Computer applications to medicine. Medical informatics Biology (General)
isfreeaccess_bool	true
container_title	BMC Bioinformatics
authorswithroles_txt_mv	Wei Zheng @@aut@@ Dingjie Wang @@aut@@ Xiufen Zou @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	326644814
id	DOAJ05395470X
language_de	englisch
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callnumber-first	R - Medicine
author	Wei Zheng
spellingShingle	Wei Zheng misc R858-859.7 misc QH301-705.5 misc Multilayer networks misc Network control misc Nonlinear dynamical systems misc IISG algorithm misc Driver nodes misc Computer applications to medicine. Medical informatics misc Biology (General) Control of multilayer biological networks and applied to target identification of complex diseases
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topic_title	R858-859.7 QH301-705.5 Control of multilayer biological networks and applied to target identification of complex diseases Multilayer networks Network control Nonlinear dynamical systems IISG algorithm Driver nodes
topic	misc R858-859.7 misc QH301-705.5 misc Multilayer networks misc Network control misc Nonlinear dynamical systems misc IISG algorithm misc Driver nodes misc Computer applications to medicine. Medical informatics misc Biology (General)
topic_unstemmed	misc R858-859.7 misc QH301-705.5 misc Multilayer networks misc Network control misc Nonlinear dynamical systems misc IISG algorithm misc Driver nodes misc Computer applications to medicine. Medical informatics misc Biology (General)
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title	Control of multilayer biological networks and applied to target identification of complex diseases
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title_full	Control of multilayer biological networks and applied to target identification of complex diseases
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author_browse	Wei Zheng Dingjie Wang Xiufen Zou
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title_sort	control of multilayer biological networks and applied to target identification of complex diseases
callnumber	R858-859.7
title_auth	Control of multilayer biological networks and applied to target identification of complex diseases
abstract	Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases.
abstractGer	Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases.
abstract_unstemmed	Abstract Background Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. Result In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. Conclusions The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases.
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title_short	Control of multilayer biological networks and applied to target identification of complex diseases
url	https://doi.org/10.1186/s12859-019-2841-2 https://doaj.org/article/dc3fa63b35f548e0922ea518afe0095e http://link.springer.com/article/10.1186/s12859-019-2841-2 https://doaj.org/toc/1471-2105
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up_date	2024-07-03T20:30:22.304Z
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Control of multilayer biological networks and applied to target identification of complex diseases

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