Emergent decision-making in biological signal transduction networks

Helikar, Tomáš; Konvalina, John; Heidel, Jack; Rogers, Jim

doi:10.1073/pnas.0705088105

Cited by 185 publications

(232 citation statements)

References 40 publications

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“…Thus, these six proteins are considered to be output nodes of the network even though they have outgoing edges to other intermediate nodes of the network. Helikar et al 19 validated their model by comparing the predicted input/output behaviors of different pairs of nodes with the existing experimental data, and the network was seen to agree with biological results.…”

Section: The Boolean Network Modelmentioning

confidence: 86%

“…In fact, with the recent progress in the experimental characterization of biological processes, BN models have been successfully applied to the study of gene regulatory networks, [13][14][15][16] cellular differentiation, 17 evolution, 4 and signal transduction in the cell. [18][19][20][21] Those studies provided evidence that sequences of events can indeed be reproduced by simple discrete dynamic models such as BNs. In this work, we are interested in the dynamical behavior of a biologically realistic BN model of the signal transduction machinery in human fibroblasts.…”

Section: Introductionmentioning

confidence: 92%

“…Here, we focus on a BN model recently proposed by Helikar et al 19 that describes the signal transduction network of a typical human fibroblast. The model was built from manual inspection of a large body of literature, resulting in the network shown in Fig.…”

Section: The Boolean Network Modelmentioning

confidence: 99%

“…In this work, we are interested in the dynamical behavior of a biologically realistic BN model of the signal transduction machinery in human fibroblasts. 19 We focus on the relaxation dynamics of the signaling network, the robustness of this relaxation to noise, and its influence on the frequency response of the network to a periodic modulation of the input signals. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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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: The Boolean Network Modelmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 92%

Section: The Boolean Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…With their comprehensive model of the influenza-host interactions during infection, Madrahimov et al demonstrate this capability. Here, the Boolean model is synchronously updated and, following the approach introduced in Helikar et al (2008), simulation specification allows one to define for each external signal, the probability that its state is 'ON' at each time step. Then a series of simulations is performed, leading to semi-quantitative representations of the activity levels of some molecular species.…”

mentioning

confidence: 99%

Logical Modelling of Regulatory Networks, Methods and Applications

Chaouiya

Rémy

2013

Bull Math Biol

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About 40 years ago, seminal work by S. Kauffman (1969) and R. Thomas (1973) paved the way to the establishment of a coarse-grained, "logical" modelling of gene regulatory networks. This gave rise to an increasingly active field of research, which ranges from theoretical studies to models of networks controlling a variety of cellular processes (Bornholdt 2008;Glass and Siegelmann 2010). Briefly, in these models, genes (or regulatory components) are assigned discrete values that account for their functional levels of expression (or activity). A regulatory function defines the evolution of the gene level, depending on the levels of its regulators. This abstracted representation of molecular mechanisms is very convenient for handling large networks for which precise kinetic data are lacking.Within the logical framework, one can distinguish several approaches that differ in the way of defining a model and its behaviour. In random Boolean networks, first introduced by S. Kauffman, the components are randomly assigned a set of regulators and a regulatory function that drives their evolution, depending on these regulators (Kauffman 1969). In threshold Boolean networks, the function is derived from a given regulatory structure as a sum of the input signals, possibly considering a threshold (Bornholdt 2008;Li et al. 2004). In the generalised logical approach introduced by R. Thomas, the logical functions are general, but are constrained by the regulatory graph. Whenever necessary or useful, the formalism supports multi-valued variables C. Chaouiya ( )

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Normalizing Input–Output Relationships of Cancer Networks for Reversion Therapy

2023

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Accumulated genetic alterations in cancer cells distort cellular stimulus‐response (or input–output) relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within a cell implicates a possibility of restoring such distorted input–output relationships by rewiring the signal flow through controlling hidden molecular switches. Here, a system framework of analyzing cellular input–output relationships in consideration of various genetic alterations and identifying possible molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis is presented. Such reversion is demonstrated by the analysis of a number of cancer molecular networks together with a focused case study on bladder cancer with in vitro experiments and patient survival data analysis. The origin of reversibility from an evolutionary point of view based on the redundancy and robustness intrinsically embedded in complex molecular regulatory networks is further discussed.

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Emergent decision-making in biological signal transduction networks

Cited by 185 publications

References 40 publications

Relaxation dynamics and frequency response of a noisy cell signaling network

Relaxation dynamics and frequency response of a noisy cell signaling network

Logical Modelling of Regulatory Networks, Methods and Applications

Normalizing Input–Output Relationships of Cancer Networks for Reversion Therapy

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