Robustness and filtering properties of ubiquitous signaling network motifs * *This work has been funded by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre (SFB/Transregio) 141 Biological Design and Integrative Structures/project B05.

Paul, Debdas; Radde, Nicole

doi:10.1016/j.ifacol.2016.12.113

IFAC-PapersOnLine

2016

DOI: 10.1016/j.ifacol.2016.12.113

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Robustness and filtering properties of ubiquitous signaling network motifs * *This work has been funded by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre (SFB/Transregio) 141 Biological Design and Integrative Structures/project B05.

Debdas Paul

Nicole Radde

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Cited by 2 publications

References 31 publications

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The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs

Paul

Radde

2018

Journal of Theoretical Biology

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The relation between design principles of signaling network motifs and their robustness against intrinsic noise still remains illusive. In this work we investigate the role of cascading for coping with intrinsic noise due to stochasticity in molecular reactions. We use stochastic approaches to quantify fluctuations in the terminal kinase of phosphorylation-dephosphorylation cascade motifs and demonstrate that cascading highly affects these fluctuations. We show that this purely stochastic effect can be explained by time-varying sequestration of upstream kinase molecules. In particular, we discuss conditions on time scales and parameter regimes which lead to a reduction of output fluctuations. Our results are put into biological context by adapting rate parameters of our modeling approach to biologically feasible ranges for general binding-unbinding and phosphorylation-dephosphorylation mechanisms. Overall, this study reveals a novel role of stochastic sequestration for dynamic noise filtering in signaling cascade motifs.

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The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs

Paul

Radde

2018

Journal of Theoretical Biology

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Inferring links in directed complex networks through feed forward loop motifs

Roy

Musawi

Ghosh

2023

Humanit Soc Sci Commun

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Complex networks are mathematical abstractions of real-world systems using sets of nodes and edges representing the entities and their interactions. Prediction of unknown interactions in such networks is a problem of interest in biology, sociology, physics, engineering, etc. Most complex networks exhibit the recurrence of subnetworks, called network motifs. Within the realm of social science, link prediction (LP) models are employed to model opinions, trust, privacy, rumor spreading in social media, academic and corporate collaborations, liaisons among lawbreakers, and human mobility resulting in contagion. We present an LP metric based on a motif in directed complex networks, called feed-forward loop (FFL). Unlike nearest neighbor-based metrics and machine learning-based techniques that gauge the likelihood of a link based on node similarity, the proposed approach leverages a known dichotomy in the motif distribution of directed networks. Complex networks are sparse, causing most nodes and their associated links to have low motif participation. Yet, due to intrinsic network motif-richness, few links participate in many distinct motif substructures. Thus, the FFL-based metric combines the presence and absence of motifs as a signature to outperform baseline metrics on ten directed social and biological network datasets. We conclude with the future of the FFL-based metric in dynamic network inference as well as its use in designing combined metrics using network motifs of varying orders as features.

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Robustness and filtering properties of ubiquitous signaling network motifs * *This work has been funded by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre (SFB/Transregio) 141 Biological Design and Integrative Structures/project B05.

Cited by 2 publications

References 31 publications

The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs

The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs

Inferring links in directed complex networks through feed forward loop motifs

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