Finding the positive feedback loops underlying multi-stationarity

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Finding the positive feedback loops underlying multi-stationarity. / Feliu, Elisenda; Wiuf, Carsten.

I: B M C Systems Biology, Bind 9, 22, 2015.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Feliu, E & Wiuf, C 2015, 'Finding the positive feedback loops underlying multi-stationarity', B M C Systems Biology, bind 9, 22. https://doi.org/10.1186/s12918-015-0164-0

APA

Feliu, E., & Wiuf, C. (2015). Finding the positive feedback loops underlying multi-stationarity. B M C Systems Biology, 9, [22]. https://doi.org/10.1186/s12918-015-0164-0

Vancouver

Feliu E, Wiuf C. Finding the positive feedback loops underlying multi-stationarity. B M C Systems Biology. 2015;9. 22. https://doi.org/10.1186/s12918-015-0164-0

Author

Feliu, Elisenda ; Wiuf, Carsten. / Finding the positive feedback loops underlying multi-stationarity. I: B M C Systems Biology. 2015 ; Bind 9.

Bibtex

@article{c35738e3f42e4edcbe58e3195a474d0e,
title = "Finding the positive feedback loops underlying multi-stationarity",
abstract = "BACKGROUND: Bistability is ubiquitous in biological systems. For example, bistability is found in many reaction networks that involve the control and execution of important biological functions, such as signaling processes. Positive feedback loops, composed of species and reactions, are necessary for bistability, and generally for multi-stationarity, to occur. These loops are therefore often used to illustrate and pinpoint the parts of a multi-stationary network that are relevant ('responsible') for the observed multi-stationarity. However positive feedback loops are generally abundant in reaction networks but not all of them are important for understanding the network's dynamics.RESULTS: We present an automated procedure to determine the relevant positive feedback loops of a multi-stationary reaction network. The procedure only reports the loops that are relevant for multi-stationarity (that is, when broken multi-stationarity disappears) and not all positive feedback loops of the network. We show that the relevant positive feedback loops must be understood in the context of the network (one loop might be relevant for one network, but cannot create multi-stationarity in another). Finally, we demonstrate the procedure by applying it to several examples of signaling processes, including a ubiquitination and an apoptosis network, and to models extracted from the Biomodels database. The procedure is implemented in Maple.CONCLUSIONS: We have developed and implemented an automated procedure to find relevant positive feedback loops in reaction networks. The results of the procedure are useful for interpretation and summary of the network's dynamics.",
author = "Elisenda Feliu and Carsten Wiuf",
year = "2015",
doi = "10.1186/s12918-015-0164-0",
language = "English",
volume = "9",
journal = "B M C Systems Biology",
issn = "1752-0509",
publisher = "BioMed Central Ltd.",

}

RIS

TY - JOUR

T1 - Finding the positive feedback loops underlying multi-stationarity

AU - Feliu, Elisenda

AU - Wiuf, Carsten

PY - 2015

Y1 - 2015

N2 - BACKGROUND: Bistability is ubiquitous in biological systems. For example, bistability is found in many reaction networks that involve the control and execution of important biological functions, such as signaling processes. Positive feedback loops, composed of species and reactions, are necessary for bistability, and generally for multi-stationarity, to occur. These loops are therefore often used to illustrate and pinpoint the parts of a multi-stationary network that are relevant ('responsible') for the observed multi-stationarity. However positive feedback loops are generally abundant in reaction networks but not all of them are important for understanding the network's dynamics.RESULTS: We present an automated procedure to determine the relevant positive feedback loops of a multi-stationary reaction network. The procedure only reports the loops that are relevant for multi-stationarity (that is, when broken multi-stationarity disappears) and not all positive feedback loops of the network. We show that the relevant positive feedback loops must be understood in the context of the network (one loop might be relevant for one network, but cannot create multi-stationarity in another). Finally, we demonstrate the procedure by applying it to several examples of signaling processes, including a ubiquitination and an apoptosis network, and to models extracted from the Biomodels database. The procedure is implemented in Maple.CONCLUSIONS: We have developed and implemented an automated procedure to find relevant positive feedback loops in reaction networks. The results of the procedure are useful for interpretation and summary of the network's dynamics.

AB - BACKGROUND: Bistability is ubiquitous in biological systems. For example, bistability is found in many reaction networks that involve the control and execution of important biological functions, such as signaling processes. Positive feedback loops, composed of species and reactions, are necessary for bistability, and generally for multi-stationarity, to occur. These loops are therefore often used to illustrate and pinpoint the parts of a multi-stationary network that are relevant ('responsible') for the observed multi-stationarity. However positive feedback loops are generally abundant in reaction networks but not all of them are important for understanding the network's dynamics.RESULTS: We present an automated procedure to determine the relevant positive feedback loops of a multi-stationary reaction network. The procedure only reports the loops that are relevant for multi-stationarity (that is, when broken multi-stationarity disappears) and not all positive feedback loops of the network. We show that the relevant positive feedback loops must be understood in the context of the network (one loop might be relevant for one network, but cannot create multi-stationarity in another). Finally, we demonstrate the procedure by applying it to several examples of signaling processes, including a ubiquitination and an apoptosis network, and to models extracted from the Biomodels database. The procedure is implemented in Maple.CONCLUSIONS: We have developed and implemented an automated procedure to find relevant positive feedback loops in reaction networks. The results of the procedure are useful for interpretation and summary of the network's dynamics.

U2 - 10.1186/s12918-015-0164-0

DO - 10.1186/s12918-015-0164-0

M3 - Journal article

C2 - 26013004

VL - 9

JO - B M C Systems Biology

JF - B M C Systems Biology

SN - 1752-0509

M1 - 22

ER -

ID: 138418551