Abstract
| Original language | English |
|---|---|
| Article number | e1489 |
| Number of pages | 38 |
| Journal | Wiley Interdisciplinary Reviews-Systems Biology and Medicine |
| Volume | 12 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Nov 2020 |
Keywords
- big data
- molecular networks
- network medicine
- PROTEIN-INTERACTION NETWORKS
- GENE REGULATORY NETWORKS
- SYSTEMS BIOLOGY
- EXPRESSION DATA
- CELL-CULTURE
- EPIGENETIC REGULATION
- INTEGRATED ANALYSIS
- HUMAN GENOME
- STEM-CELL
- VISUALIZATION
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In: Wiley Interdisciplinary Reviews-Systems Biology and Medicine, Vol. 12, No. 6, e1489, 11.2020.
Research output: Contribution to journal › (Systematic) Review article › peer-review
TY - JOUR
T1 - Molecular networks in Network Medicine
T2 - Development and applications
AU - Silverman, Edwin K.
AU - Schmidt, Harald H. H. W.
AU - Anastasiadou, Eleni
AU - Altucci, Lucia
AU - Angelini, Marco
AU - Badimon, Lina
AU - Balligand, Jean-Luc
AU - Benincasa, Giuditta
AU - Capasso, Giovambattista
AU - Conte, Federica
AU - Di Costanzo, Antonella
AU - Farina, Lorenzo
AU - Fiscon, Giulia
AU - Gatto, Laurent
AU - Gentili, Michele
AU - Loscalzo, Joseph
AU - Marchese, Cinzia
AU - Napoli, Claudio
AU - Paci, Paola
AU - Petti, Manuela
AU - Quackenbush, John
AU - Tieri, Paolo
AU - Viggiano, Davide
AU - Vilahur, Gemma
AU - Glass, Kimberly
AU - Baumbach, Jan
N1 - Funding Information: This article was written by the Molecular Networks Working Group of the International Network Medicine Consortium. We thank the other consortium members for their comments and ideas relevant to this manuscript. E.K.S.: Supported by National Institutes of Health (USA) grants U01 HL089856, P01 HL114501, R01 HL133135, R01 HL 137927, and R01 HL147148. H.H.H.W.S.: Supported by REPO‐TRIAL: This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 777111. This reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains. Supported by FeatureCloud: This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No 826078. This reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains. Supported by SAVEBRAIN: This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 737586. This reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains. E.A.: None reported. L.A.: Supported by VALERE: Vanvitelli per la Ricerca, the Italian Association for Cancer Research (AIRC‐17217), MIUR20152TE5PK, iCURE (CUP B21c17000030007), FASE 2: IDEAL (CUP B63D18000560007), MIUR, proof of concept, CUP:B64I19000290008. M.A.: None reported. L.B. and G.V.: Supported by Plan Nacional de Salud (PNS) [SAF2016‐76819‐R to L.B. and PGC2018‐094025‐B‐I00 to G.V.] from the Spanish Ministry of Science and Innovation and funds FEDER “Una Manera de Hacer Europa” and CIBERCV (to L.B.). We thank the support of the Generalitat of Catalunya (Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat, 2017 SGR 1480) and the Fundación Investigación Cardiovascular‐Fundación Jesus Serra for their continuous support. J.‐L.B.: Supported by a WELBIO‐Fonds National de la Recherche Scientifique grant funded by the Walloon Region (AGR‐REN‐X500220F‐35045981). G.B.: As a PhD student of Translational Medicine, she is supported by an Educational Grant from the University of Campania “Luigi Vanvitelli”, Naples, Italy. G.C.: None reported. F.C.: None reported. A.D.C.: Supported by VALERE: Vanvitelli per la Ricerca, the Italian Association for Cancer Research (AIRC‐17217), MIUR20152TE5PK, iCURE (CUP B21c17000030007), FASE 2: IDEAL (CUP B63D18000560007), MIUR, proof of concept, CUP:B64I19000290008. L.F.: None reported. G.F.: None reported. L.G.: None reported. M.G.: Partially supported by grant number AR11916B32035A1F from Progetti per Avvio alla Ricerca ‐ Tipo 1 Sapienza University of Rome, Italy. J.L.: Supported by National Institutes of Health (USA) grants U54 HL1191145, U01 HG007690, and P50 GM107618, and American Heart Association grant 700382. C.M.: PRIN 2017 n. 2017F8ZB89_002 Minister of Health (ERC LS_7). C.N.: Supported by grant number PRIN2017F8ZB89 from Italian Ministry of Research (PI: Prof Napoli). P.P.: None reported. M.P.: None reported. J.Q.: Supported by National Institutes of Health (USA) grants R01HL111759, P01HL105339, and 1R35CA220523. P.T.: Partially supported by the EU H2020 project “iPC Individualized Pediatric Cure”, grant agreement No. 826121, and from COST project CA15120 OpenMultiMed. D.V.: None reported. K.G.: Supported by grant number K25HL133599 from the National Heart, Lung, and Blood Institute of the National Institutes of Health, USA. J.B.: Supported by H2020 grants RepoTrial (nr. 777111) and FeatureCloud (nr. 826078), as well as DFG Collaborative Research Centers Microbial Signatures (SFB1371) and Molecular Mechanisms in Plants (SFB924), and his VILLUM Young Investigator grant (nr. 13154). Funding Information: DFG Collaborative Research Centers Microbial Signatures, Grant/Award Number: SFB1371; DFG Collaborative Research Centers Molecular Mechanisms in Plants, Grant/Award Number: SFB924; European Cooperation in Science and Technology, Grant/Award Number: CA15120; Horizon 2020 Framework Programme, Grant/Award Numbers: 737586, 777111, 826078, 826121; Italian Ministry of Research, Grant/Award Number: PRIN2017F8ZB89; Ministero dell'Istruzione, dell'Università e della Ricerca, Grant/Award Number: MIUR20152TE5PK; National Institutes of Health, Grant/Award Numbers: 1R35CA220523, K25HL133599, P01HL105339, P01HL114501, P50GM107618, R01HL111759, R01HL133135, R01HL137927, R01HL147148, U01HG007690, U01HL089856, U54HL1191145; Plan Nacional de Salud Spanish Ministry of Science and Innovation, Grant/Award Numbers: PGC2018‐094025‐B‐100, SAF2016‐76819‐R; Progetti per Avvio alla Ricerca, Grant/Award Number: AR11916B32035A1F; VALERE: Vanvitelli per la Ricerca, the Italian Association for Cancer Research, Grant/Award Number: AIRC‐17217; Villum Young Investigator Grant, Grant/Award Number: 13154; WELBIO‐Fonds National de la Recherche Scientifique grant funded by the Walloon Region, Grant/Award Number: AGR‐REN‐X500220F‐35045981 Funding information Funding Information: E.K.S. received grant support from GSK and Bayer. J.L.—cofounder of Scipher Medicine, Inc. biotech start‐up, uses network medicine strategies to define biomarkers of therapeutic efficacy and to repurpose drugs. The other authors have declared no conflicts of interest for this article. Funding Information: This article was written by the Molecular Networks Working Group of the International Network Medicine Consortium. We thank the other consortium members for their comments and ideas relevant to this manuscript. E.K.S.: Supported by National Institutes of Health (USA) grants U01 HL089856, P01 HL114501, R01 HL133135, R01 HL 137927, and R01 HL147148. H.H.H.W.S.: Supported by REPO-TRIAL: This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 777111. This reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains. Supported by FeatureCloud: This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No 826078. This reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains. Supported by SAVEBRAIN: This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 737586. This reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains. E.A.: None reported. L.A.: Supported by VALERE: Vanvitelli per la Ricerca, the Italian Association for Cancer Research (AIRC-17217), MIUR20152TE5PK, iCURE (CUP B21c17000030007), FASE 2: IDEAL (CUP B63D18000560007), MIUR, proof of concept, CUP:B64I19000290008. M.A.: None reported. L.B. and G.V.: Supported by Plan Nacional de Salud (PNS) [SAF2016-76819-R to L.B. and PGC2018-094025-B-I00 to G.V.] from the Spanish Ministry of Science and Innovation and funds FEDER ?Una Manera de Hacer Europa? and CIBERCV (to L.B.). We thank the support of the Generalitat of Catalunya (Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat, 2017 SGR 1480) and the Fundaci?n Investigaci?n Cardiovascular-Fundaci?n Jesus Serra for their continuous support. J.-L.B.: Supported by a WELBIO-Fonds National de la Recherche Scientifique grant funded by the Walloon Region (AGR-REN-X500220F-35045981). G.B.: As a PhD student of Translational Medicine, she is supported by an Educational Grant from the University of Campania ?Luigi Vanvitelli?, Naples, Italy. G.C.: None reported. F.C.: None reported. A.D.C.: Supported by VALERE: Vanvitelli per la Ricerca, the Italian Association for Cancer Research (AIRC-17217), MIUR20152TE5PK, iCURE (CUP B21c17000030007), FASE 2: IDEAL (CUP B63D18000560007), MIUR, proof of concept, CUP:B64I19000290008. L.F.: None reported. G.F.: None reported. L.G.: None reported. M.G.: Partially supported by grant number AR11916B32035A1F from Progetti per Avvio alla Ricerca - Tipo 1 Sapienza University of Rome, Italy. J.L.: Supported by National Institutes of Health (USA) grants U54 HL1191145, U01 HG007690, and P50 GM107618, and American Heart Association grant 700382. C.M.: PRIN 2017 n. 2017F8ZB89_002 Minister of Health (ERC LS_7). C.N.: Supported by grant number PRIN2017F8ZB89 from Italian Ministry of Research (PI: Prof Napoli). P.P.: None reported. M.P.: None reported. J.Q.: Supported by National Institutes of Health (USA) grants R01HL111759, P01HL105339, and 1R35CA220523. P.T.: Partially supported by the EU H2020 project ?iPC Individualized Pediatric Cure?, grant agreement No. 826121, and from COST project CA15120 OpenMultiMed. D.V.: None reported. K.G.: Supported by grant number K25HL133599 from the National Heart, Lung, and Blood Institute of the National Institutes of Health, USA. J.B.: Supported by H2020 grants RepoTrial (nr. 777111) and FeatureCloud (nr. 826078), as well as DFG Collaborative Research Centers Microbial Signatures (SFB1371) and Molecular Mechanisms in Plants (SFB924), and his VILLUM Young Investigator grant (nr. 13154). Publisher Copyright: © 2020 Wiley Periodicals LLC.
PY - 2020/11
Y1 - 2020/11
N2 - Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein–protein interaction networks, correlation‐based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in ideNetwork Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein–protein interaction networks, correlation‐based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseasesntifying key genes within genetic association regions, and limited applications to human diseases
AB - Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein–protein interaction networks, correlation‐based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in ideNetwork Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein–protein interaction networks, correlation‐based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseasesntifying key genes within genetic association regions, and limited applications to human diseases
KW - big data
KW - molecular networks
KW - network medicine
KW - PROTEIN-INTERACTION NETWORKS
KW - GENE REGULATORY NETWORKS
KW - SYSTEMS BIOLOGY
KW - EXPRESSION DATA
KW - CELL-CULTURE
KW - EPIGENETIC REGULATION
KW - INTEGRATED ANALYSIS
KW - HUMAN GENOME
KW - STEM-CELL
KW - VISUALIZATION
U2 - 10.1002/wsbm.1489
DO - 10.1002/wsbm.1489
M3 - (Systematic) Review article
C2 - 32307915
SN - 1939-5094
VL - 12
JO - Wiley Interdisciplinary Reviews-Systems Biology and Medicine
JF - Wiley Interdisciplinary Reviews-Systems Biology and Medicine
IS - 6
M1 - e1489
ER -