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Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci 2022; 23:4645. [PMID: 35563034 PMCID: PMC9104788 DOI: 10.3390/ijms23094645] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
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Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - Faryal Mehwish Awan
- Department of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan;
| | - Anam Naz
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Oscar Alvarez
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | - Ana Cernea
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | | | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43205, USA
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2
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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Schmucker R, Farina G, Faeder J, Fröhlich F, Saglam AS, Sandholm T. Combination treatment optimization using a pan-cancer pathway model. PLoS Comput Biol 2021; 17:e1009689. [PMID: 34962919 PMCID: PMC8747684 DOI: 10.1371/journal.pcbi.1009689] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 01/10/2022] [Accepted: 11/29/2021] [Indexed: 12/11/2022] Open
Abstract
The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available. Combination therapies are a promising approach to counter complex diseases such as cancers. Two key difficulties in the design of effective cancer combination therapies are the large number of available drugs and the heterogeneity of cancers which render exhaustive laboratory studies impractical. In recent years, sophisticated signaling pathway models that can predict responses to combination treatments at a molecular level have been developed. This motivates the question of how one can leverage mechanistic models to identify candidates for novel combination treatments. In this paper we propose a combination treatment optimization framework which employs a large-scale pan-cancer pathway model. We formulate treatment optimization problems for single cell lines and heterogeneous populations of cancer cells. We further investigate sequential treatment plans and combine an existing evolutionary algorithm with an efficient Hamiltonian Monte-Carlo based sampling scheme. During extensive simulation studies our approach identified combination therapies which are predicted to be more effective than conventional treatments. We hope that one day in silico experiments will be used to identify a small set of promising treatment candidates which can then form a starting point for laboratory studies, allowing for an efficient use of limited resources and accelerated discovery of effective therapies.
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Affiliation(s)
- Robin Schmucker
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Gabriele Farina
- Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - James Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ali Sinan Saglam
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tuomas Sandholm
- Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Strategy Robot, Inc., Pittsburgh, Pennsylvania, United States of America
- Optimized Markets, Inc., Pittsburgh, Pennsylvania, United States of America
- Strategic Machine, Inc., Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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4
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Rocca A, Kholodenko BN. Can Systems Biology Advance Clinical Precision Oncology? Cancers (Basel) 2021; 13:6312. [PMID: 34944932 PMCID: PMC8699328 DOI: 10.3390/cancers13246312] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/10/2021] [Indexed: 12/13/2022] Open
Abstract
Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.
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Affiliation(s)
- Andrea Rocca
- Hygiene and Public Health, Local Health Unit of Romagna, 47121 Forlì, Italy
| | - Boris N. Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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5
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Nwaokorie A, Fey D. Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models. Int J Mol Sci 2021; 22:9970. [PMID: 34576133 PMCID: PMC8467693 DOI: 10.3390/ijms22189970] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/01/2021] [Accepted: 09/10/2021] [Indexed: 11/17/2022] Open
Abstract
Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these models should focus on the critical STNs for each cancer, include prognostic genes and proteins, and correctly predict patient-specific differences in STN activity. Focussing on colorectal cancer and the WNT STN, we used mechanism-based machine learning models to identify genes and proteins with significant associations to event-free patient survival and predictive power for explaining patient-specific differences of STN activity. First, we identified the WNT pathway as the most significant pathway associated with event-free survival. Second, we built linear-regression models that incorporated both genes and proteins from established mechanistic models in the literature and novel genes with significant associations to event-free patient survival. Data from The Cancer Genome Atlas and Clinical Proteomic Tumour Analysis Consortium were used, and patient-specific STN activity scores were computed using PROGENy. Three linear regression models were built, based on; (1) the gene-set of a state-of-the-art mechanistic model in the literature, (2) novel genes identified, and (3) novel proteins identified. The novel genes and proteins were genes and proteins of the extant WNT pathway whose expression was significantly associated with event-free survival. The results show that the predictive power of a model that incorporated novel event-free associated genes is better compared to a model focussing on the genes of a current state-of-the-art mechanistic model. Several significant genes that should be integrated into future mechanistic models of the WNT pathway are DVL3, FZD5, RAC1, ROCK2, GSK3B, CTB2, CBT1, and PRKCA. Thus, the study demonstrates that using mechanistic information in combination with machine learning can identify novel features (genes and proteins) that are important for explaining the STN heterogeneity between patients and their association to clinical outcomes.
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Affiliation(s)
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland;
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6
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Han JZR, Hastings JF, Phimmachanh M, Fey D, Kolch W, Croucher DR. Personalized Medicine for Neuroblastoma: Moving from Static Genotypes to Dynamic Simulations of Drug Response. J Pers Med 2021; 11:395. [PMID: 34064704 PMCID: PMC8151552 DOI: 10.3390/jpm11050395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/19/2021] [Accepted: 04/30/2021] [Indexed: 12/21/2022] Open
Abstract
High-risk neuroblastoma is an aggressive childhood cancer that is characterized by high rates of chemoresistance and frequent metastatic relapse. A number of studies have characterized the genetic and epigenetic landscape of neuroblastoma, but due to a generally low mutational burden and paucity of actionable mutations, there are few options for applying a comprehensive personalized medicine approach through the use of targeted therapies. Therefore, the use of multi-agent chemotherapy remains the current standard of care for neuroblastoma, which also conceptually limits the opportunities for developing an effective and widely applicable personalized medicine approach for this disease. However, in this review we outline potential approaches for tailoring the use of chemotherapy agents to the specific molecular characteristics of individual tumours by performing patient-specific simulations of drug-induced apoptotic signalling. By incorporating multiple layers of information about tumour-specific aberrations, including expression as well as mutation data, these models have the potential to rationalize the selection of chemotherapeutics contained within multi-agent treatment regimens and ensure the optimum response is achieved for each individual patient.
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Affiliation(s)
- Jeremy Z. R. Han
- Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; (J.Z.R.H.); (J.F.H.); (M.P.)
| | - Jordan F. Hastings
- Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; (J.Z.R.H.); (J.F.H.); (M.P.)
| | - Monica Phimmachanh
- Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; (J.Z.R.H.); (J.F.H.); (M.P.)
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland; (D.F.); (W.K.)
- Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland; (D.F.); (W.K.)
- Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - David R. Croucher
- Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; (J.Z.R.H.); (J.F.H.); (M.P.)
- St Vincent’s Hospital Clinical School, UNSW Sydney, Sydney, NSW 2052, Australia
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7
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Salvucci M, Rahman A, Resler AJ, Udupi GM, McNamara DA, Kay EW, Laurent-Puig P, Longley DB, Johnston PG, Lawler M, Wilson R, Salto-Tellez M, Van Schaeybroeck S, Rafferty M, Gallagher WM, Rehm M, Prehn JHM. A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic. JCO Clin Cancer Inform 2020; 3:1-17. [PMID: 30995124 DOI: 10.1200/cci.18.00056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation. PATIENTS AND METHODS We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117). RESULTS Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs. CONCLUSION This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Mark Lawler
- Queen's University Belfast, Belfast, United Kingdom
| | | | | | | | | | | | - Markus Rehm
- Royal College of Surgeons in Ireland, Dublin, Ireland.,University of Stuttgart, Stuttgart, Germany
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8
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Salvucci M, Zakaria Z, Carberry S, Tivnan A, Seifert V, Kögel D, Murphy BM, Prehn JHM. System-based approaches as prognostic tools for glioblastoma. BMC Cancer 2019; 19:1092. [PMID: 31718568 PMCID: PMC6852738 DOI: 10.1186/s12885-019-6280-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 10/09/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The evasion of apoptosis is a hallmark of cancer. Understanding this process holistically and overcoming apoptosis resistance is a goal of many research teams in order to develop better treatment options for cancer patients. Efforts are also ongoing to personalize the treatment of patients. Strategies to confirm the therapeutic efficacy of current treatments or indeed to identify potential novel additional options would be extremely beneficial to both clinicians and patients. In the past few years, system medicine approaches have been developed that model the biochemical pathways of apoptosis. These systems tools incorporate and analyse the complex biological networks involved. For their successful integration into clinical practice, it is mandatory to integrate systems approaches with routine clinical and histopathological practice to deliver personalized care for patients. RESULTS We review here the development of system medicine approaches that model apoptosis for the treatment of cancer with a specific emphasis on the aggressive brain cancer, glioblastoma. CONCLUSIONS We discuss the current understanding in the field and present new approaches that highlight the potential of system medicine approaches to influence how glioblastoma is diagnosed and treated in the future.
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Affiliation(s)
- Manuela Salvucci
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Zaitun Zakaria
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Steven Carberry
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Amanda Tivnan
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Volker Seifert
- Department of Neurosurgery, Frankfurt University Hospital, Frankfurt am Main, Germany
| | - Donat Kögel
- Department of Neurosurgery, Frankfurt University Hospital, Frankfurt am Main, Germany
| | - Brona M. Murphy
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Jochen H. M. Prehn
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
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9
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Davis JD, Kumbale CM, Zhang Q, Voit EO. Dynamical systems approaches to personalized medicine. Curr Opin Biotechnol 2019; 58:168-174. [PMID: 30978644 DOI: 10.1016/j.copbio.2019.03.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 02/19/2019] [Accepted: 03/01/2019] [Indexed: 12/29/2022]
Abstract
The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.
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Affiliation(s)
- Jacob D Davis
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic Drive, Atlanta, GA 30332, United States
| | - Carla M Kumbale
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic Drive, Atlanta, GA 30332, United States; Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd, NE, Atlanta, GA 30322, United States
| | - Qiang Zhang
- Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd, NE, Atlanta, GA 30322, United States.
| | - Eberhard O Voit
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic Drive, Atlanta, GA 30332, United States.
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10
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Hastings JF, Skhinas JN, Fey D, Croucher DR, Cox TR. The extracellular matrix as a key regulator of intracellular signalling networks. Br J Pharmacol 2018; 176:82-92. [PMID: 29510460 DOI: 10.1111/bph.14195] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 02/06/2018] [Accepted: 02/13/2018] [Indexed: 12/11/2022] Open
Abstract
The extracellular matrix (ECM) is a salient feature of all solid tissues within the body. This complex, acellular entity is composed of hundreds of individual molecules whose assembly, architecture and biomechanical properties are critical to controlling the behaviour and phenotype of the different cell types residing within tissues. Cells are the basic unit of life and the core building block of tissues and organs. At their simplest, they follow a set of rules, governed by their genetic code and effected through the complex protein signalling networks that these genes encode. These signalling networks assimilate and process the information received by the cell to control cellular decisions that govern cell fate. The ECM is the biggest provider of external stimuli to cells and as such is responsible for influencing intracellular signalling dynamics. In this review, we discuss the inclusion of ECM as a central regulatory signalling sub-network in computational models of cellular decision making, with a focus on its role in diseases such as cancer. LINKED ARTICLES: This article is part of a themed section on Translating the Matrix. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.1/issuetoc.
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Affiliation(s)
- Jordan F Hastings
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Darlinghurst, NSW, 2010, Australia
| | - Joanna N Skhinas
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Darlinghurst, NSW, 2010, Australia
| | - Dirk Fey
- Systems Biology Ireland, University College Dublin, Dublin 4, Ireland
| | - David R Croucher
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Darlinghurst, NSW, 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Kensington, NSW, 2010, Australia.,School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland
| | - Thomas R Cox
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Darlinghurst, NSW, 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Kensington, NSW, 2010, Australia
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