A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks

AlQuraishi, Mohammed; Koytiger, Grigoriy; Jenney, Anne; MacBeath, Gavin; Sorger, Peter K.

doi:10.1038/ng.3138

Cited by 60 publications

(55 citation statements)

References 37 publications

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“…In this study, we mainly focus on several classic systemic modeling approaches and their applications in current cancer research. These popular modeling approaches can simulate the dynamic changes of regulatory networks (signaling pathways and metabolic pathways), tumor growth, and its microenvironments, such as ordinary differential equations (ODEs) [10], Boolean network [18], Petri nets [19], linear programming (LP) based model [9, 20], agent-based model [11], and the system biology modeling approach considering genetic variation [21]. We present these models in Figure 1.…”

Section: Several Classical Systemic Modeling Approachesmentioning

confidence: 99%

“…How to integrate the information of genetic variations in the systemic modeling work is now a new topic . AlQuraishi et al first proposed a multiscale statistical mechanical framework integrated genomic, binding, and structural data to predict the effects of specific mutations on PPI networks and cancer-related pathways [21]. Based on the concept of Hamiltonian, they modeled how the mutations in SH2 domains induced network alterations and the experimental results validated the proposed model.…”

Section: Several Classical Systemic Modeling Approachesmentioning

confidence: 99%

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Mathematical and Computational Modeling in Complex Biological Systems

Yan

et al. 2017

BioMed Research International

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The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

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Section: Several Classical Systemic Modeling Approachesmentioning

confidence: 99%

Section: Several Classical Systemic Modeling Approachesmentioning

confidence: 99%

Mathematical and Computational Modeling in Complex Biological Systems

Yan

et al. 2017

BioMed Research International

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“…In addition, pathways can be inferred from “omics” analyses of biological samples. 16,17 In particular, samples that are blood based (i.e., liquid biopsies) can easily be collected longitudinally and correlated with disease progression. 18 …”

Section: Present Focus On Target-centric and Phenotypic Discovery: A mentioning

confidence: 99%

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

et al. 2016

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Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)–driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.

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“…How genes and proteins interact with each other is the basis of molecular biology and disease pathogenesis 1,2 . These functional interactions, which biologists place into pathways, have been characterized through hypothesis-driven experiments and then manually defined in the past 3,4 .…”

Section: Introductionmentioning

confidence: 99%

Discovery of Functional and Disease Pathways by Community Detection in Protein-Protein Interaction Networks

et al. 2016

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Advances in cellular, molecular, and disease biology depend on the comprehensive characterization of gene interactions and pathways. Traditionally, these pathways are curated manually, limiting their efficient annotation and, potentially, reinforcing field-specific bias. Here, in order to test objective and automated identification of functionally cooperative genes, we compared a novel algorithm with three established methods to search for communities within gene interaction networks. Communities identified by the novel approach and by one of the established method overlapped significantly (q < 0.1) with control pathways. With respect to disease, these communities were biased to genes with pathogenic variants in ClinVar (p ≪ 0.01), and often genes from the same community were co-expressed, including in breast cancers. The interesting subset of novel communities, defined by poor overlap to control pathways also contained co-expressed genes, consistent with a possible functional role. This work shows that community detection based on topological features of networks suggests new, biologically meaningful groupings of genes that, in turn, point to health and disease relevant hypotheses.

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A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks

Cited by 60 publications

References 37 publications

Mathematical and Computational Modeling in Complex Biological Systems

Mathematical and Computational Modeling in Complex Biological Systems

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

Discovery of Functional and Disease Pathways by Community Detection in Protein-Protein Interaction Networks

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