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The Role of Canalization in the Spre...

Manicka, Santosh Venkatiah Sudharshan.

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The Role of Canalization in the Spreading of Perturbations in Boolean Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Role of Canalization in the Spreading of Perturbations in Boolean Networks./
Author:	Manicka, Santosh Venkatiah Sudharshan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	225 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
Subject:	Systems science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10277023
ISBN:	9781369768244

The Role of Canalization in the Spreading of Perturbations in Boolean Networks.
Manicka, Santosh Venkatiah Sudharshan.

The Role of Canalization in the Spreading of Perturbations in Boolean Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 225 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--Indiana University, 2017.

Canalization is a property of Boolean automata that characterizes the extent to which subsets of inputs determine (canalize) the output. Here, we investigate the role of canalization as a characteristic of perturbation-spreading in random Boolean networks (BN) with homogeneous connectivity via numerical simulations. We consider two different measures of canalization introduced by Marques-Pita and Rocha, namely `effective connectivity' and `input symmetry', in a three-pronged approach. First, we show that the mean `effective connectivity', a measure of the true mean in-degree of a BN, is a better predictor of the dynamical regime (order or chaos) of the BN than the mean in-degree. Next, we combine effective connectivity and input symmetry in a single measure of `unified canalization' by using a common yardstick of Boolean hypercube ``dimension", a form of fractal dimension. We show that the unified measure is a better predictor of dynamical regime than effective connectivity alone for BNs with large in-degrees. When considered separately, the relative contributions of the two components of the unified measure changes systematically with the mean in-degree, where input symmetry becomes increasingly more dominant with larger in-degrees. As an application, we show that the said measures of canalization characterize the dynamical regimes of a suite of Systems biology models better than the in-degree. Finally, we introduce `integrated effective connectivity' as an extension of effective connectivity that characterizes the canalization present in BNs with arbitrary timescales obtained by iteratively composing a BN with itself. We show that the integrated measure is a better predictor of long-term dynamical regime than just effective connectivity for a small class of BNs known as the elementary cellular automata. This dissertation will advance theoretical understanding of BNs, allowing us to more accurately predict their short-term and long-term dynamic character, based on canalization. As BNs are generic models of complex systems, combining interaction graphs with multivariate dynamics, these results contribute to the complex networks and systems field. Moreover, as BNs are important models of choice in Systems biology, our methods contribute to the burgeoning toolkit of the field.

ISBN: 9781369768244Subjects--Topical Terms:

3168411
Systems science.

The Role of Canalization in the Spreading of Perturbations in Boolean Networks.
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245 1 4 $a The Role of Canalization in the Spreading of Perturbations in Boolean Networks.
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300 $a 225 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Adviser: Luis M. Rocha.
502 $a Thesis (Ph.D.)--Indiana University, 2017.
520 $a Canalization is a property of Boolean automata that characterizes the extent to which subsets of inputs determine (canalize) the output. Here, we investigate the role of canalization as a characteristic of perturbation-spreading in random Boolean networks (BN) with homogeneous connectivity via numerical simulations. We consider two different measures of canalization introduced by Marques-Pita and Rocha, namely `effective connectivity' and `input symmetry', in a three-pronged approach. First, we show that the mean `effective connectivity', a measure of the true mean in-degree of a BN, is a better predictor of the dynamical regime (order or chaos) of the BN than the mean in-degree. Next, we combine effective connectivity and input symmetry in a single measure of `unified canalization' by using a common yardstick of Boolean hypercube ``dimension", a form of fractal dimension. We show that the unified measure is a better predictor of dynamical regime than effective connectivity alone for BNs with large in-degrees. When considered separately, the relative contributions of the two components of the unified measure changes systematically with the mean in-degree, where input symmetry becomes increasingly more dominant with larger in-degrees. As an application, we show that the said measures of canalization characterize the dynamical regimes of a suite of Systems biology models better than the in-degree. Finally, we introduce `integrated effective connectivity' as an extension of effective connectivity that characterizes the canalization present in BNs with arbitrary timescales obtained by iteratively composing a BN with itself. We show that the integrated measure is a better predictor of long-term dynamical regime than just effective connectivity for a small class of BNs known as the elementary cellular automata. This dissertation will advance theoretical understanding of BNs, allowing us to more accurately predict their short-term and long-term dynamic character, based on canalization. As BNs are generic models of complex systems, combining interaction graphs with multivariate dynamics, these results contribute to the complex networks and systems field. Moreover, as BNs are important models of choice in Systems biology, our methods contribute to the burgeoning toolkit of the field.
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