Integrating Proteomic, Transcriptional, and Interactome Data Reveals Hidden Components of Signaling and Regulatory Networks

Huang, S.; Fraenkel, Ernest

doi:10.1126/scisignal.2000350

Cited by 163 publications

(235 citation statements)

References 66 publications

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“…6 (ii) Other approaches are primarily based on the incorporation of prior knowledge of signaling networks or transcription regulation in addition to the gene expression data. [7][8][9] For example, in the work by Ziemek et al, 8 the Selventa knowledge-base was used that includes causal, condition specific relationships between signaling proteins and gene expressions, and a Bayesian inference approach was used to identify subsets of this knowledge base that are most probably active in the specific biological context. Ziemek et al were able to identify the key regulators that govern gene expression, but they could only capture limited mechanistic aspects of the intermediates in signal transduction, i.e.…”

Section: Introductionmentioning

confidence: 99%

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

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Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury. Insight, innovation, integrationIn this manuscript we introduce a novel approach for the identification of signaling pathways that are functional in a specific biological context, by leveraging gene expression data and prior knowledge of protein connectivity. In more detail, we introduce a linear programming formulation to model signal transduction from one node to the next in a Prior Knowledge Network (PKN), and by minimizing the mismatch between model predictions and experimental data, we are able to identify subsets of the PKN that are most probably functional in the specific biological context. More specifically, we address the problem of identifying the modes of action of drugs that have been reported to induce respiratory side effects, based on their gene expression profiles, and subsequently, merge the drug specific pathways together to construct a signaling network that captures the signaling mechanisms underlying Drug Induced Lung Disease (DILD). Moreover, to demonstrate the predictive power and biological relevance of the DILD network, we use it to suggest potential drug repositioning for treating lung injury.

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Section: Introductionmentioning

confidence: 99%

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

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“…How transcription-factor binding sites contribute to gene expression is complicated, but systematic analyses are beginning to suggest that promoter activity is largely a function of binding-site location and multiplicity (MacIsaac et al, 2010;Segal et al, 2008;Sharon et al, 2012). We thus expect that many new computational models will be developed that link signalling dynamics to transcriptional signatures (Cheng et al, 2011;Huang and Fraenkel, 2009). Likewise, as tools advance for studying single cells at the network level, we anticipate improved models of cell-cell communication, cell heterogeneity and multi-cell properties (Anderson et al, 2006;Feinerman et al, 2008;Jørgensen et al, 2009; Box 3.…”

Section: Future Perspectivesmentioning

confidence: 99%

Models of signalling networks – what cell biologists can gain from them and give to them

Janes

Lauffenburger

2013

Journal of Cell Science

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SummaryComputational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are crosstrained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally. Key words: Cell signalling, Computational biology, Systems biologyIntroduction A decade ago, we welcomed the first signalling-network models that were strongly grounded in wet-lab experiments (Hoffmann et al., 2002;Schoeberl et al., 2002). Excellent models now exist for many canonical signalling circuits in a variety of biological settings. However, such models should not be viewed as an end product but rather as a tool for addressing systems-level challenges in cell biology . Have models fulfilled this role and have they provided biological insights that experimentalists should bother to care about? Here, we answer 'Yes' to both questions and predict that signallingnetwork models will soon become indispensable for modern research in the field. Fortunately, the current wealth of dataintensive methods has primed today's cell biologists to embrace modelling, even though they may lack formal training in the underlying mathematics.In this Opinion, we propose that empirical cell biologists have much to gain from signalling-network models, and much to give by ensuring that these models stay in touch with reality. We begin with a brief primer on how computational models can be critically assessed from a biological standpoint. Then, we walk through a series of important insights about cell signalling that have stemmed from computational-systems modelling. We conclude with future perspectives on where signalling-network models are just beginning to have an impact and will continue to do so in the coming years.

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“…An alternative approach, an award gathering Steiner tree, was used to identify changes driven by protein interactions in the yeast pheromone response. 20 The Steiner tree was successful in balancing the introduction of false positive interactions from experimental data with the loss of key interactions.…”

Section: Analysis Approaches and Toolsmentioning

confidence: 99%

Identification of Aberrant Pathway and Network Activity From High-Throughput Data

et al. 2011

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The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of mutations or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.

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Integrating Proteomic, Transcriptional, and Interactome Data Reveals Hidden Components of Signaling and Regulatory Networks

Cited by 163 publications

References 66 publications

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Models of signalling networks – what cell biologists can gain from them and give to them

Identification of Aberrant Pathway and Network Activity From High-Throughput Data

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