scholarly journals Advanced Boolean modeling of biological networks applied to systems pharmacology

2017 ◽  
pp. btw747 ◽  
Author(s):  
Itziar Irurzun-Arana ◽  
José Martín Pastor ◽  
Iñaki F. Trocóniz ◽  
José David Gómez-Mantilla
Keyword(s):  
Biological Networks ◽  
Systems Pharmacology ◽  
Boolean Modeling

scholarly journals Harnessing Big Data for Systems Pharmacology

2016 ◽  
Author(s):  
Lei Xie ◽  
Eli J. Draizen ◽  
Philip E. Bourne
Keyword(s):  
Big Data ◽  
Biological Networks ◽  
Drug Response ◽  
Conformational Dynamics ◽  
Model Management ◽  
Data Driven ◽  
Systems Pharmacology ◽  
Drug Actions ◽  
Data Driven Modeling ◽  
Big Data Technology

AbstractSystems pharmacology aims to holistically understand genetic, molecular, cellular, organismal, and environmental mechanisms of drug actions through developing mechanistic or predictive models. Data-driven modeling plays a central role in systems pharmacology, and has already enabled biologists to generate novel hypotheses. However, more is needed. The drug response is associated with genetic/epigenetic variants and environmental factors, is coupled with molecular conformational dynamics, is affected by possible off-targets, is modulated by the complex interplay of biological networks, and is dependent on pharmacokinetics. Thus, in order to gain a comprehensive understanding of drug actions, systems pharmacology requires integration of models across data modalities, methodologies, organismal hierarchies, and species. This imposes a great challenge on model management, integration, and translation. Here, we discuss several upcoming issues in systems pharmacology and potential solutions to them using big data technology. It will allow systems pharmacology modeling to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

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