Network medicine: a network-based approach to human disease

Barabási, Albert‐László; Gulbahce, Natali; Loscalzo, Joseph

doi:10.1038/nrg2918

Cited by 4,173 publications

(3,828 citation statements)

References 135 publications

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“…18,19 This seems intuitive since organisms demonstrate modularity and conservation of biology across evolution. 20,21 One such approach, weighted gene co-expression network analysis (WGCNA), uses the property of coexpression to organize genes into gene networks or modules. 22 Here we develop a co-expression framework called the 'toxicogenomic module associations with pathogenesis' (the TXG-MAP) and integrate it with standard pathology evaluation to characterize mechanisms of drug-induced liver injury.…”

Section: Introductionmentioning

confidence: 99%

Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

et al. 2017

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Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome (the 'TXG-MAP'). Accounting for the overall transcriptional activity resulting from treatment, we explain mechanisms of toxicity and predict distinct toxicity phenotypes using module associations. We demonstrate that early network responses complement traditional histology-based assessment in predicting outcomes for longer studies and identify a novel mechanism of hepatotoxicity involving endoplasmic reticulum stress and Nrf2 activation. Module-based molecular subtypes of cholestatic injury derived using rat translate to human. Moreover, compared to gene-level analysis alone, combining module and gene-level analysis performed in sequence identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.

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Section: Introductionmentioning

confidence: 99%

Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

et al. 2017

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“…Molecular biologists have come to recognize the importance of this, as witnessed by discussions of 'gene regulatory networks' and the recent terminological shifts from 'metabolic pathway' to 'metabolic network' and from 'signaling pathway' to 'signaling network.' Many researchers emphasize that since there is 'cross-talk' between what deemed to be separate pathways, the larger network needs to be studied (Barabási et al, 2011;Fraser & Germain, 2009;Jørgensen & Linding, 2010;Layek et al, 2011;Wing et al, 2011). Furthermore, the organization of causal interactions is not unidirectional and acyclic, but mechanisms have feedback loops (Bechtel, 2011).…”

Section: Mechanismsmentioning

confidence: 99%

Systems biology and the integration of mechanistic explanation and mathematical explanation

Brigandt

2013

Studies in History and Philosophy of Science Part C: Studies in

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The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models-which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation (as the analysis of a whole in terms of its structural parts and their qualitative interactions) have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena (rather than the explanation of quantitative details), where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism's ability to respond to perturbations (beyond its actual operation). I offer general conclusions for philosophical accounts of explanation.

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“…On the other hand, it also calls for reconsideration of the dominant health policy thinking, which is too deterministic and not equipped to take into account the interconnected nature of many diseases as well as their social determinants [4] Furthermore, looking at specific networks as determinants of health or disease allows us to reconsider the relationship between genes and environment. For instance, by analysing data on genetic structure in a large scale population provided by the National Longitudinal Study of Adolescent Health and the Framingham Heart Study, Fowler et al [23] showed that the genetic structure may be less determined by reproduction constraints (e.g.…”

Section: The Network Medicine Approach To Public Health Policymentioning

confidence: 99%

Towards a Complexity-Friendly Policy: Breaking the Vicious Circle of Equilibrium Thinking in Economics and Public Policy

Squazzoni

2017

Understanding Complex Systems

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This chapter aims to discuss certain limitations of the dominant equilibrium thinking in policy and explore more complexity-friendly alternatives. If societies and markets are viewed as complex, non-equilibrium systems, understanding nonlinear, adaptive and evolving patterns emerging from agent behaviour in network structures is fundamental for policy purposes. This requires improved realism of the behavioural and social foundations on which policies are based. We must also reconsider the mantra of incentivisation, institutional design and the top-down regulation that typically dominates conventional policy. Recent cases of financial market regulation and health policies can help us to understand the importance of looking at the subtle ways in which people or organisations behave when exposed to social influence, and pre-existing social norms and network externalities. Changing the current policy narrative and exploring complexity-friendly concepts, instruments and methods requires a shift of focus of policy-making from forecast and prediction of system equilibrium in order to understand and manage complex social systems better.

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Network medicine: a network-based approach to human disease

Cited by 4,173 publications

References 135 publications

Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

Systems biology and the integration of mechanistic explanation and mathematical explanation

Towards a Complexity-Friendly Policy: Breaking the Vicious Circle of Equilibrium Thinking in Economics and Public Policy

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