Data-driven modelling of signal-transduction networks

Janes, Kevin A.; Yaffe, Michael B.

doi:10.1038/nrm2041

Cited by 345 publications

(353 citation statements)

References 43 publications

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“…Cell behaviour and phenotype are directly controlled by dynamic networks of protein interactions in response to external or internal cues/stimuli (Figures 1, 2). Signalling networks, such as those regulated by kinases and phosphobinding domains (eg the SH2 domains) are essential for the integrative response a cell must make at any given point in time to extra-or intracellular cues [4][5][6][7][8][9][10][11].…”

Section: The Cue Signal and Response Model Of Cell Signallingmentioning

confidence: 99%

“…We, and others, have shown that the network models pertaining to cellular signalling can also be utilized for fine-grained prediction of human disease 292 JT Erler et al [15][16][17][18]. Through quantitative systematic measurements of these networks and integration of multiple types of data, it is now possible to define dynamic changes to these networks and perform computational modelling of the networks that enables predictions to be made about specific responses such systems should elicit [4,7,8,13]. In particular, we have recently shown that, integrating systems genetics data [19] with phospho-proteomics data and computational models of cellular kinase specificity [11], we could derive an integrative network model of JNK regulation in the fruit fly [12].…”

Section: Integrative Network Biologymentioning

confidence: 99%

“…As mass-spectrometry proteomics continues to be a challenging technology to master, it is likely that network models established by quantitative mass spectrometry in the first instance will be easiest to implement in the clinic as affinity-based microarrays [4]. However, this will also require the development of new computational algorithms to analyse such data [4,8,13].…”

Section: Future Biomarkers -Network Signaturesmentioning

confidence: 99%

“…An essential aspect of cellular signalling networks is that at any given time they are in a given state that impacts directly on the cellular response to an environmental stimulation (or cue) [4][5][6][7][8]. This multivariate nature enables cells to respond to multiple input cues in an integrative and quantitative manner [7] (Figure 2).…”

Section: Introductionmentioning

confidence: 99%

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Network‐based drugs and biomarkers

Erler

Linding

2009

The Journal of Pathology

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The structure and dynamics of protein signalling networks governs cell decision processes and the formation of tissue boundaries. Complex diseases such as cancer and diabetes are diseases of such networks. Therefore approaches that can give insight into how these networks change during disease progression are crucial for better understanding, detection and intervention. The era of network medicine has begun; however, there are fundamental principles associated with molecular networks that are essential to consider for this field to succeed. Here, we introduce network biology and some of its associated technologies. We then focus on the multivariate nature of cellular networks and how this has implications for biomarker and drug discovery using cancer metastasis as an example.

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Section: The Cue Signal and Response Model Of Cell Signallingmentioning

confidence: 99%

Section: Integrative Network Biologymentioning

confidence: 99%

Section: Future Biomarkers -Network Signaturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network‐based drugs and biomarkers

Erler

Linding

2009

The Journal of Pathology

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“…Commonly used genomescale networks include protein-protein interaction (PPI) network (Rual et al, 2005), transcriptional regulatory network (Licatalosi and Darnell, 2010), signal transduction network (Janes and Yaffe, 2006), metabolomic network (Duarte et al, 2007), etc. Jointly analyzing high-throughput data with the networks can yield robust network markers, i.e.…”

Section: Introductionmentioning

confidence: 99%

Bayesian network feature finder (BANFF): an R package for gene network feature selection

et al. 2016

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Motivation: Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model has been developed and successfully applied for selecting genes and gene subnetworks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand. Results: We extended the method and developed an R package, the Bayesian network feature finder (BANFF), providing a package of posterior inference, model comparison and graphical illustration of model fitting. The model was extended to a more general form, and a parallel computing algorithm for the Markov chain Monte Carlo -based posterior inference and an expectation maximization-based algorithm for posterior approximation were added. Based on simulation studies, we demonstrate the use of BANFF on analyzing gene expression on a protein-protein interaction network.

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Mathematical Modelling of Biological Signaling Networks

Haugh

2008

Wiley Encyclopedia of Chemical Biology

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Intracellular signaling networks, which are composed of interconnected biochemical pathways, regulate and actuate responses such as cell‐cycle progression and cell migration, survival, and differentiation. Although our knowledge of the intricate biochemical mechanisms at the level of individual proteins and molecular interactions is ever expanding, those details leave us with an even murkier view of how the complex network operates as a whole. True understanding requires knowing not only what happens at the molecular level but also how these mechanisms influence the precise magnitude, timing, and spatial localization of signal transduction processes. Hence, mathematical modeling and analysis has emerged in recent years as a legitimate approach for interpreting experimental results and generating novel hypotheses for additional study and model refinement. Once conducted in isolation and scorned by most biologists, quantitative modeling has moved into the mainstream as a powerful tool for the analysis of cell signaling. In this article, the biological, chemical, and physical underpinnings of this approach are presented, as are its current applications and future challenges.

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Data-driven modelling of signal-transduction networks

Cited by 345 publications

References 43 publications

Network‐based drugs and biomarkers

Network‐based drugs and biomarkers

Bayesian network feature finder (BANFF): an R package for gene network feature selection

Mathematical Modelling of Biological Signaling Networks

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