Logic Modeling in Quantitative Systems Pharmacology

Traynard, Pauline; Tobalina, Luis; Eduati, Federica; Calzone, Laurence; Sáez-Rodríguez, Julio

doi:10.1002/psp4.12225

Cited by 29 publications

(25 citation statements)

References 86 publications

(173 reference statements)

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“…The main challenge is how to extract the knowledge network (NK) from the drug-related dataset. The first knowledge network is normally referred to as the prior knowledge network (PNK) which encapsulates the biological knowledge already known for the main compounds involved in the process being studied (Traynard et al, 2017 ). Most of the PNKs came from the literature, and a very few of them came from the data mining related technologies.…”

Section: Methodsmentioning

confidence: 99%

“…Boolean modeling is the simplest model for GRNs without the need to consider any effects at the intermediate levels (Liang et al, 1998 ; Tušek and Kurtanjek, 2012 ; Abou-Jaoude et al, 2016 ; Barberis et al, 2017 ; Traynard et al, 2017 ). This modeling was initially introduced by Kauffman (Kauffman, 1969 ; Kauffman et al, 2003 ) in 1969 following the discovery of the first gene regulatory mechanisms in bacteria (Jacob and Monod, 1961 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Data-Driven Boolean Model for Genetic Regulatory Networks

2018

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A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Data-Driven Boolean Model for Genetic Regulatory Networks

2018

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“…The best models are able to capture causal signaling relationships and can simulate their dynamics in response to various perturbations, be it pharmacological targeting or a muta-tion in a key component. The quality of such models itself depends on information from conventional studies, including the biochemical properties of individual signaling components, their temporal behavior, and their spatial organization (11,132).…”

Section: Integration Of Drug Therapy Approaches With Systems Biologymentioning

confidence: 99%

Cracking the context-specific PI3K signaling code

Madsen

Vanhaesebroeck

2020

Sci. Signal.

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Specificity in signal transduction is determined by the ability of cells to “encode” and subsequently “decode” different environmental signals. Akin to computer software, this “signaling code” governs context-dependent execution of cellular programs through modulation of signaling dynamics and can be corrupted by disease-causing mutations. Class IA phosphoinositide 3-kinase (PI3K) signaling is critical for normal growth and development and is dysregulated in human disorders such as benign overgrowth syndromes, cancer, primary immune deficiency, and metabolic syndrome. Despite decades of PI3K research, understanding of context-dependent regulation of the PI3K pathway and of the underlying signaling code remains rudimentary. Here, we review current knowledge on context-specific PI3K signaling and how technological advances now make it possible to move from a qualitative to quantitative understanding of this pathway. Insight into how cellular PI3K signaling is encoded or decoded may open new avenues for rational pharmacological targeting of PI3K-associated diseases. The principles of PI3K context-dependent signal encoding and decoding described here are likely applicable to most, if not all, major cell signaling pathways.

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“…Logic-based network models use Boolean relationships to define stimulatory or inhibiting relationships between nodes; they are increasingly being used in QSP as middle-out network models that do not require full kinetic parametrization but are much more informative than an undirected network (105)(106)(107). Several variants of logic modeling have been used, including those that utilize fuzzy logic and those that use Hill equations (105,106,108). Logic models have been shown to be relevant to the prediction of drug combination effects.…”

Section: Analysis Of Molecular Drug-disease Network Interactionsmentioning

confidence: 99%

Systems Pharmacology: Defining the Interactions of Drug Combinations

Hasselt

Iyengar

2019

Annu. Rev. Pharmacol. Toxicol.

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The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

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Logic Modeling in Quantitative Systems Pharmacology

Cited by 29 publications

References 86 publications

A Novel Data-Driven Boolean Model for Genetic Regulatory Networks

A Novel Data-Driven Boolean Model for Genetic Regulatory Networks

Cracking the context-specific PI3K signaling code

Systems Pharmacology: Defining the Interactions of Drug Combinations

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