Information Routing Driven by Background Chatter in a Signaling Network

Domedel-Puig, Núria; Rué, Pau; Pons, Antonio J.; García‐Ojalvo, Jordi

doi:10.1371/journal.pcbi.1002297

Cited by 11 publications

(23 citation statements)

References 44 publications

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“…Furthermore, in some cases, noise is required for the network to transduce the oscillating input at the allowed range of frequencies. 22 The range of nontrivial frequency responses reported here ͑low-pass, band-pass, and high-pass͒ implies that the signaling network being considered does not behave simply as a linear cascade, since in that case, the information would be simply propagated with a certain delay, of the order of the cascade length. The average shortest path length from inputs to outputs in the network of Fig.…”

Section: Discussionmentioning

confidence: 94%

“…On the other hand, it maintains the characteristic relaxation times of the network and helps transducing structured inputs, in the form of periodic signals, into their corresponding outputs. 22 To some extent, the dynamics in the periodically driven case becomes a sequence of relaxations between the two states involved in the driving signal. The synergistic effect between noisy and structured inputs found here is an example of integration of signals by cells.…”

Section: -5mentioning

confidence: 99%

See 1 more Smart Citation

Relaxation dynamics and frequency response of a noisy cell signaling network

Rué

Pons

Domedel-Puig

et al. 2010

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We investigate the dynamics of cell signaling using an experimentally based Boolean model of the human fibroblast signal transduction network. We determine via systematic numerical simulations the relaxation dynamics of the network in response to a constant set of inputs, both in the absence and in the presence of environmental fluctuations. We then study the network's response to periodically modulated signals, uncovering different types of behaviors for different pairs of driven input and output nodes. The phenomena observed include low-pass, high-pass, and band-pass filtering of the input modulations, among other nontrivial responses, at frequencies around the relaxation frequency of the network. The results reveal that the dynamic response to the external modulation of biologically realistic signaling networks is versatile and robust to noise. © 2010 American Institute of Physics. ͓doi:10.1063/1.3524908͔One of the characteristic features of living cells is their ability to continuously monitor their environment and respond appropriately to extracellular signals, which instruct the cells to take decisions such as proliferating, stopping growth, secreting chemicals, or even committing suicide. This behavior is mediated by signal transduction networks, which are composed of large numbers of interacting proteins. These networks have an input layer consisting of receptor proteins on the cell membrane that activate upon binding extracellular signals and an output layer of enzymes and/or transcription factors whose activation produces physiological changes in the cell. Until recently, the structure of these networks was largely unknown, and as a result, their theoretical study had to assume random connectivity between the nodes (proteins). Nowadays, the rise of high-throughput screening techniques allows the mapping of signaling networks with multiple connected pathways, and thus examining the activity of biologically realistic networks has become feasible. Here, we study the dynamical behavior of a signaling network recently identified in human fibroblasts in terms of a Boolean description in which the network elements are either fully active or inactive. Using extensive and systematic numerical simulations, we quantify the response of the network to all its inputs, and especially the relaxation dynamics to the corresponding stationary attractors, both with and without noise in the input signals. We also examine the case of periodically modulated inputs and characterize the frequency response of the network, which is shown to be extremely diverse.

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Section: Discussionmentioning

confidence: 94%

Section: -5mentioning

confidence: 99%

Relaxation dynamics and frequency response of a noisy cell signaling network

Rué

Pons

Domedel-Puig

et al. 2010

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Thus, even though chatter destroys the periodic orbits, it maintains the fixed transient response at the population level (considering the average activity of different realizations of the dynamics). Besides, the average activity of the outputs also depends on the chatter level (Domedel-Puig et al, 2011). This property is a result of the structure of the biologically network studied here (its topology and its logic rules) because it is not observed in randomized versions of the network with the same topological properties but with different logic rules for the nodes.…”

Section: Network Dynamics and Chatter Effects On Information Flowmentioning

confidence: 96%

Integration of cellular signals in chattering environments

Rué¹,

Domedel-Puig²,

García-Ojalvo³

et al. 2012

Progress in Biophysics and Molecular Biology

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Cells are constantly exposed to fluctuating environmental conditions. External signals are sensed, processed and integrated by cellular signal transduction networks, which translate input signals into specific cellular responses by means of biochemical reactions. These networks have a complex nature, and we are still far from having a complete characterization of the process through which they integrate information, specially given the noisy environment in which that information is embedded. Guided by the many instances of constructive influences of noise that have been reported in the physical sciences in the last decades, here we explore how multiple signals are integrated in an eukaryotic cell in the presence of background noise, or chatter. To that end, we use a Boolean model of a typical human signal transduction network. Despite its complexity, we find that the network is able to display simple patterns of signal integration. Furthermore, our computational analysis shows that these integration patterns depend on the levels of fluctuating background activity carried by other cell inputs. Taken together, our results indicate that signal integration is sensitive to environmental fluctuations, and that this background noise effectively determines the information integration capabilities of the cell.

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“…In fact, logical models have become increasingly popular. They have been recently used to model complex dynamical behaviours and provide insights into numerous biological systems, including gene regulatory networks (e.g., [3][4][5][6]), signal transduction (e.g., [7][8][9][10][11][12][13][14]), as well as cell cycle (e.g., [15][16][17][18]), in species ranging from bacteria and viruses (e.g., [3,19,20]) to yeast (e.g., [17,[21][22][23]), flies (e.g., [24][25][26]), plants (e.g., [27,28]), and even to humans (e.g., [11,12,18,29]).…”

Section: Introductionmentioning

confidence: 99%

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

et al. 2013

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BackgroundQualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.ResultsWe present the Systems Biology Markup Language (SBML) Qualitative Models Package (“qual”), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.ConclusionsSBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

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Information Routing Driven by Background Chatter in a Signaling Network

Cited by 11 publications

References 44 publications

Relaxation dynamics and frequency response of a noisy cell signaling network

Relaxation dynamics and frequency response of a noisy cell signaling network

Integration of cellular signals in chattering environments

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

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