PAIRS: Prediction of Activation/Inhibition Regulation Signaling Pathway

Wang, Tengjiao; Feng, Yanghe; Wang, Qi

doi:10.1155/2017/7024516

“…The covariance structure must be informed by the graph structure of the network, as well as the nature of the link. In Eukaryotes, inducing links account for approximately 75% to 80% of regulators (McDonald et al, 2008;Wang et al, 2017). McDonald et al (2008) report that the average proportion of activations for circadian networks is 0.74 in Arabidopsis and Drosophila, while generally for Eukaryotic signalling networks the average is 0.83.…”

Section: Covariance Structurementioning

confidence: 99%

Capturing context-specific regulation in molecular interaction networks

Rush

¹

,

Repsilber

²

2018

Preprint

View full text Add to dashboard Cite

Motivation: Gene expression changes over time in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase power to detect differential signals as well as for describing regulated modules from a mechanistic point of view. Results: We present an effective procedure for identifying differentially activated subnetworks in molecular interaction networks. Differential gene expression coherent with the regulatory nature of the network is identified. Sequentially controlling error on genes and links results in more efficient inference. By focusing on local inference, our method is ignorant of the global topology, and as a result equally effective on exponential and scale-free networks. We apply our procedure both to systematically simulated data, comparing its performance to alternative methods, and to the transcription regulatory network in the context of particle-induced pulmonary inflammation, recapitulating and proposing additional candidates to some previously obtained results.

show abstract

“…These connections frequently occur in gene regulatory and metabolic/biochemical networks, while undirected edges are commonly present in chemical similarity or PPI networks to, for instance, represent symmetric binding relationships. For the latter group of edges, several methods [ 6 , 7 ] have emerged to assign directionality to interaction pairs (e.g., characterizing regulatory relationships as activation or inhibition relations) in order to assert causality which can be useful for various purposes. An example lies in discerning whether causal interactions between a drug target and intermediary proteins will inhibit a certain phenotype, a drug’s intended effect, or activate it instead.…”

Section: Introductionmentioning

confidence: 99%

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Rivas

¹

,

Mubeen

²

,

Bernat

³

et al. 2020

View full text Add to dashboard Cite

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

show abstract

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Rivas

¹

,

Mubeen

²

,

Guim-Bernat

³

et al. 2020

Preprint

View full text Add to dashboard Cite

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

show abstract

PAIRS: Prediction of Activation/Inhibition Regulation Signaling Pathway

Cited by 5 publications

References 21 publications

Capturing context-specific regulation in molecular interaction networks

Capturing context-specific regulation in molecular interaction networks

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Contact Info

Product

Resources

About