Building SuperModels: Emerging patient avatars for use in precision and systems medicine

Frontiers in Physiology

Volumn 6, Issue NOV, 2015, Pages

  Brown, Sherry Ann ^a  

^a MAYO CLINIC   (United States)

Author keywords

Avatars; Computational; Discipulus; Electronic health record; Precision medicine; Supermodels; Systems medicine; Virtual patient

Indexed keywords

ALGORITHM; BRAIN; CANCER RESEARCH; COMPUTER MODEL; COMPUTER PROGRAM; ELECTRONIC MEDICAL RECORD; FEEDBACK SYSTEM; HEART; HUMAN; MACHINE LEARNING; MALIGNANT NEOPLASTIC DISEASE; MEDICAL LITERATURE; MEDICAL RESEARCH; NERVE CELL NETWORK; NONHUMAN; NOTE; PATIENT CARE; PERSONALIZED MEDICINE;

EID: 84949564084     PISSN: None     EISSN: 1664042X     Source Type: Journal    
DOI: 10.3389/fphys.2015.00318     Document Type: Note

References (45)

1. Agren, R., Mardinoglu, A., Asplund, A., Kampf, C., Uhlen, M., and Nielsen, J. (2014). Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol. Syst. Biol. 10, 721. doi: 10.1002/msb.145122.
2. American Cancer Society (2015). Cancer Facts and Figures 2015. Atlanta, GA: American Cancer Society.
3. Auffray, C., Chen, Z., and Hood, L. (2009). Systems medicine: the future of medical genomics and healthcare. Genome Med. 1:2. doi: 10.1186/gm2.
4. Barabási, A. L., and Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113. doi: 10.1038/nrg1272.
5. Bikson, M., Rahman, A., and Datta, A. (2012a). Computational models of transcranial direct current stimulation. Clin. EEG Neurosci. 43, 176-183. doi: 10.1177/1550059412445138.
6. Bikson, M., Rahman, A., Datta, A., Fregni, F., and Merabet, L. (2012b). High-resolution modeling assisted design of customized and individualized transcranial direct current stimulation protocols. Neuromodulation 15, 306-315. doi: 10.1111/j.1525-1403.2012.00481.x.
7. Brown, S. A., and Loew, L. M. (2012). Computational analysis of calcium signaling and membrane electrophysiology in cerebellar Purkinje neurons associated with ataxia. BMC Syst. Biol. 6:70. doi: 10.1186/1752-0509-6-70.
8. Brown, S. A., and Loew, L. M. (2014). Integration of modeling with experimental and clinical findings synthesizes and refines the central role of inositol 1,4,5-trisphosphate receptor 1 in spinocerebellar ataxia. Front. Neurosci. 8:453. doi: 10.3389/fnins.2014.00453.
9. Brown, S. A., McCullough, L. D., and Loew, L. M. (2015a). Computational neurobiology is a useful tool in translational neurology: the example of ataxia. Front. Neurosci. 9:1. doi: 10.3389/fnins.2015.00001.
10. Brown, S. A., Sandhu, N., and Herrmann, J. (2015b). Systems biology approaches to adverse drug effects: the example of cardio-oncology. Nat. Rev. Clin. Oncol. doi: 10.1038/nrclinonc.2015.168. [Epub ahead of print].
11. Capobianco, E. (2012). Ten challenges for systems medicine. Front. Genet. 3:193. doi: 10.3389/fgene.2012.00193.
12. Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y., Miriami, E., et al. (2012). Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293-1307. doi: 10.1016/j.cell.2012.02.009.
13. Chen, R., and Snyder, M. (2013). Promise of personalized omics to precision medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 5, 73-82. doi: 10.1002/wsbm.1198.
14. Collins, F., and Varmus, H. (2015). A new initiative on precision medicine. New Engl. J. Med. 372, 793-795. doi: 10.1056/NEJMp1500523.
15. El-Kareh, A. W., and Secomb, T. W. (2000). A mathematical model for comparison of bolus injection, continuous infusion, and liposomal delivery of doxorubicin to tumor cells. Neoplasia 2, 325-338. doi: 10.1038/sj.neo.7900096.
16. Emmert-Streib, F., and Dehmer, M. (2013). Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too. Front. Genet. 4:241. doi: 10.3389/fgene.2013.00241.
17. Fox, J., Bailenson, J., and Binney, J. (2009). Virtual experiences, physical behaviors: the effect of presence on imitation of an eating avatar. Presence 18, 294-303. doi: 10.1162/pres.18.4.294.
18. Fregni, F., Gimenes, R., Valle, A. C., Ferreira, M. J., Rocha, R. R., Natalle, L., et al. (2006). A randomized, sham-controlled, proof of principle study of transcranial direct current stimulation for the treatment of pain in fibromyalgia. Arthritis Rheum. 54, 3988-3998. doi: 10.1002/art.22195.
19. Garay, J. P., and Gray, J. W. (2012). Omics and therapy-a basis for precision medicine. Mol. Oncol. 6, 128-139. doi: 10.1016/j.molonc.2012.02.009.
20. Henderson, D., Ogilvie, L. A., Hoyle, N., Keilholz, U., Lange, B., Lehrach, H., et al. (2014). Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack. Biotechnol. J. 9, 1104-1114. doi: 10.1002/biot.201400109.
21. Highnam, G., and Mittelman, D. (2012). Personal genomes and precision medicine. Genome Biol. 13, 324. doi: 10.1186/gb-2012-13-12-324.
22. Hood, L., and Flores, M. (2012). A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N. Biotechnol. 29, 613-624. doi: 10.1016/j.nbt.2012.03.004.
23. Kullo, I. J., Jarvik, G. P., Manolio, T. A., Williams, M. S., and Roden, D. M. (2013). Leveraging the electronic health record to implement genomic medicine. Genet. Med. 15, 270-271. doi: 10.1038/gim.2012.131.
24. Lao, J., Madani, J., Puértolas, T., Alvarez, M., Hernández, A., Pazo-Cid, R., et al. (2013). Liposomal Doxorubicin in the treatment of breast cancer patients: a review. J. Drug Deliv. 2013, 456409. doi: 10.1155/2013/456409.
25. Lawrence Livermore National Security, L. (2015). High Performance Computing Innovation Center [Online]. Available online at: http://hpcinnovationcenter.llnl.gov/.
26. Melo, S. A., Luecke, L. B., Kahlert, C., Fernandez, A. F., Gammon, S. T., Kaye, J., et al. (2015). Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 523, 177-182. doi: 10.1038/nature14581.
27. Mirnezami, R., Nicholson, J., and Darzi, A. (2012). Preparing for precision medicine. N. Engl. J. Med. 366, 489-491. doi: 10.1056/NEJMp1114866.
28. Nature Biotechnology (2015). UK catapults precision medicine. Nat. Biotechnol. 33:119. doi: 10.1038/nbt0215-119c.
29. Ng, K., Sun, J., Hu, J., and Wang, F. (2015). Personalized predictive modeling and risk factor identification using patient similarity. AMIA Jt. Summits Transl. Sci. Proc. 2015, 132-136.
30. Noble, D. (2008). Claude Bernard, the first systems biologist, and the future of physiology. Exp. Physiol. 93, 16-26. doi: 10.1113/expphysiol.2007.038695.
31. Plotkin, S., Cashman, N., and Das, A. (2013). Computational prediction of ALS patient survival times from protein mechanical properties. Biophys. J. 104, 577a. doi: 10.1016/j.bpj.2012.11.3206.
32. Savage, S. (2014). MD Anderson and Watson Are Transforming Healthcare [Online]. IBM Systems Magazine. Available online at: http://www.ibmsystemsmag.com/power/issues/August-2014/.
33. Schuyler, A. D., Carlson, H. A., and Feldman, E. L. (2011). Computational methods for identifying a layered allosteric regulatory mechanism for ALS-causing mutations of Cu-Zn superoxide dismutase 1. Proteins 79, 417-427. doi: 10.1002/prot.22892.
34. Stamatakos, G., Dionysiou, D., Giatili, S., Kolokotroni, E., Georgiadi, E., Roniotis, A., et al. (2010). "The contracancrum oncosimulator: integrating biomechanisms across scales in the clinical context," in 4th International Advanced Research Workshop on In Silico Oncology and Cancer Investigation (Athens).
35. Steinhubl, S. R., Muse, E. D., and Topol, E. J. (2015). The emerging field of mobile health. Sci. Transl. Med. 7, 283rv283. doi: 10.1126/scitranslmed.aaa3487.
36. The Discipulus Project, V. (2013). Discipulus: Roadmap for the Digital Patient [Online]. Available online at: http://www.vph-institute.org/upload/discipulus-digital-patient-research-roadmap_5270f44c03856.pdf.
37. Tortella, G., Casati, R., Aparicio, L. V., Mantovani, A., Senço, N., D'Urso, G., et al. (2015). Transcranial direct current stimulation in psychiatric disorders. World J. Psychiatry 5, 88-102. doi: 10.5498/wjp.v5.i1.88.
38. Tortolina, L., Castagnino, N., De Ambrosi, C., Moran, E., Patrone, F., Ballestrero, A., et al. (2012). A multi-scale approach to colorectal cancer: from a biochemical-interaction signaling-network level, to multi-cellular dynamics of malignant transformation. Interplay with mutations and onco-protein inhibitor drugs. Curr. Cancer Drug Targets 12, 339-355. doi: 10.2174/156800912800190910.
39. Truong, D. Q., Magerowski, G., Blackburn, G. L., Bikson, M., and Alonso-Alonso, M. (2013). Computational modeling of transcranial direct current stimulation (tDCS) in obesity: impact of head fat and dose guidelines. Neuroimage Clin. 2, 759-766. doi: 10.1016/j.nicl.2013.05.011.
40. Utsler, J. (2015). How IBM Research Is Using Advanced Computing to Lessen Sudden Heart Failure [Online]. IBM Systems Magazine. Available online at: http://www.ibmsystemsmag.com/power/trends/ibmresearch/model-heart/.
41. Vejpongsa, P., and Yeh, E. T. (2014). Prevention of anthracycline-induced cardiotoxicity: challenges and opportunities. J. Am. Coll. Cardiol. 64, 938-945. doi: 10.1016/j.jacc.2014.06.1167.
42. Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., et al. (2014). Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333-337. doi: 10.1038/nmeth.2810.
43. Wolkenhauer, O. (2013). The role of theory and modeling in medical research. Front. Physiol. 4:377. doi: 10.3389/fphys.2013.00377.
44. Yizhak, K., Chaneton, B., Gottlieb, E., and Ruppin, E. (2015). Modeling cancer metabolism on a genome scale. Mol. Syst. Biol. 11, 817. doi: 10.15252/msb.20145307.
45. Zhang, P., Wang, F., Hu, J., and Sorrentino, R. (2014). Towards personalized medicine: leveraging patient similarity and drug similarity analytics. AMIA Jt Summits Transl. Sci. Proc. 2014, 132-136.