Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics

Kwon, Yung-Keun; Cho, Kwang-Hyun

doi:10.1093/bioinformatics/btn060

Cited by 89 publications

(59 citation statements)

References 38 publications

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“…A substantial number of studies have shown that network robustness against mutation is correlated to other structural characteristics, for example feedback loops [21]. Therefore, analysing the effects of the collective structural properties, including modularity and other aspects of network robustness, will hold promise as a future area of study within this field.…”

Section: Resultsmentioning

confidence: 99%

The relationship between modularity and robustness in signalling networks

Tran

Kwon

2013

J. R. Soc. Interface.

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Many biological networks tend to have a high modularity structural property and the dynamic characteristic of high robustness against perturbations. However, the relationship between modularity and robustness is not well understood. To investigate this relationship, we examined real signalling networks and conducted simulations using a random Boolean network model. As a result, we first observed that the network robustness is negatively correlated with the network modularity. In particular, this negative correlation becomes more apparent as the network density becomes sparser. Even more interesting is that, the negative relationship between the network robustness and the network modularity occurs mainly because nodes in the same module with the perturbed node tend to be more sensitive to the perturbation than those in other modules. This result implies that dynamically similar nodes tend to be located in the same module of a network. To support this, we show that a pair of genes associated with the same disease or a pair of functionally similar genes is likely to belong to the same module in a human signalling network.

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Section: Resultsmentioning

confidence: 99%

The relationship between modularity and robustness in signalling networks

Tran

Kwon

2013

J. R. Soc. Interface.

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“…Again, Boolean models have been used to great effect in studies of coupled feedback loops. Using random Boolean networks, a series of papers by Kwon et al has shown that networks with more coupled positive feedback loops are associated with multistability (i.e., fixed point dynamics), while those with more coupled negative feedback loops are associated with cyclical behavior [39,[51][52][53]. Moreover, the existence of coupled feedback loops is associated with increased robustness of the network (i.e., more resistant to perturbation); a finding that would explain why coupled feedback loops are so common in biological networks.…”

Section: Mechanisms Of Dynamics; Boolean Studies Of the Feedback Loopmentioning

confidence: 99%

Boolean Modeling of Biochemical Networks

Helikar¹,

Kochi

Konvalina

et al. 2011

TOBIOIJ

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Abstract:The use of modeling to observe and analyze the mechanisms of complex biochemical network function is becoming an important methodological tool in the systems biology era. Number of different approaches to model these networks have been utilized--they range from analysis of static connection graphs to dynamical models based on kinetic interaction data. Dynamical models have a distinct appeal in that they make it possible to observe these networks in action, but they also pose a distinct challenge in that they require detailed information describing how the individual components of these networks interact in living cells. Because this level of detail is generally not known, dynamic modeling requires simplifying assumptions in order to make it practical. In this review Boolean modeling will be discussed, a modeling method that depends on the simplifying assumption that all elements of a network exist only in one of two states.Keywords: Systems biology, dynamical modeling, signal transduction, boolean networks.The modern era of cellular biochemistry has been characterized by the discovery of large interaction networks that are notable for their highly nonlinear connection structures. Such structures complicate study since they are often quite difficult to reduce into linear subsets that function in isolation from the embedding network [1,2]. It is for this reason that the concept of "systems biology" has been developed and is now being applied to these complicated biochemical networks [1,3,4].One of the first steps taken toward applying a systems approach to understanding biochemical networks was to begin organizing the connection information determined in the laboratory by creating connection maps (wiring diagrams) that could then be analyzed using graph theory concepts. While these studies have been highly successful [5], the fact that cells are dynamical systems means the next major step in the study of these systems involves the creation of models that take into account how they move and function in time. Thus we are now in a new era of creating large-scale dynamical models of biochemical systems.An obvious choice for the modeling of dynamical systems is to describe the system with a system of differential equations, and this approach has been used extensively to study protein and/or gene interactions in a variety of relatively small models (for examples, see [6][7][8][9]). One of the advantages of continuous modeling via differential equations is that one can potentially describe molecular interactions with high precision and in quantitative terms that correspond to realistic laboratory measurements. However, that precision tends to make them computationally unwieldy for the large scale models necessary for the systems approach. There *Address correspondence to this author at the Department of Mathematics, University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE 68182, USA; Tel: 402 554-3110: Fax: 402 554-2975; E-mail: jrogers@unmc.edu is currently a great deal of work being done in order to simpl...

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“…An interesting application of a Boolean network is the study of feedback cycles in GRNs (Kwon and Cho 2008;Kim et al 2008). By studying the structure of the state space and particular trajectories, the dynamic behaviour of the system can be analysed without any actual simulation of the system.…”

Section: Simulation Modelling and Analysismentioning

confidence: 99%

Network modelling of gene regulation

Charleston

2010

Biophys Rev

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Gene regulatory network (GRN) modelling has gained increasing attention in the past decade. Many computational modelling techniques have been proposed to facilitate the inference and analysis of GRN. However, there is often confusion about the aim of GRN modelling, and how a gene network model can be fully utilised as a tool for systems biology. The aim of the present article is to provide an overview of this rapidly expanding subject. In particular, we review some fundamental concepts of systems biology and discuss the role of network modelling in understanding complex biological systems. Several commonly used network modelling paradigms are surveyed with emphasis on their practical use in systems biology research.

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Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics

Cited by 89 publications

References 38 publications

The relationship between modularity and robustness in signalling networks

The relationship between modularity and robustness in signalling networks

Boolean Modeling of Biochemical Networks

Network modelling of gene regulation

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