Building digital life systems for future biology and medicine

doi:10.15302/J-QB-023-0331

Quant. Biol.

2023, Vol. 11

Issue (3) : 207-213 https://doi.org/10.15302/J-QB-023-0331

PERSPECTIVE

Building digital life systems for future biology and medicine

Xuegong Zhang^1,^2,³(

), Lei Wei¹, Rui Jiang¹, Xiaowo Wang^1,², Jin Gu¹, Zhen Xie^1,², Hairong Lv¹

¹. MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
². Center of Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
³. School of Life Sciences and School of Medicine, Tsinghua University, Beijing 100084, China

Download: PDF(2269 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The rapid development of biological technology (BT) and information technology (IT) especially of genomics and artificial intelligence (AI) is bringing great potential for revolutionizing future medicine. We propose the concept and framework of Digital Life Systems or dLife as a new paradigm to unleash this potential. It includes the multi-scale and multi-granule measure and representation of life in the digital space, the mathematical and/or computational modeling of the biology behind physiological and pathological processes, and ultimately cyber twins of healthy or diseased human body in the virtual space that can be used to simulate complex biological processes and deduce effects of medical treatments. We advocate that dLife is the route toward future AI precision medicine and should be the new paradigm for future biological and medical research.

Keywords digital life systems digital twin aritificial intelligence precision medicine

Corresponding Author(s): Xuegong Zhang

Just Accepted Date: 19 June 2023 Online First Date: 13 July 2023 Issue Date: 08 October 2023

Cite this article:

Xuegong Zhang,Lei Wei,Rui Jiang, et al. Building digital life systems for future biology and medicine[J]. Quant. Biol., 2023, 11(3): 207-213.

URL:

https://academic.hep.com.cn/qb/EN/10.15302/J-QB-023-0331
https://academic.hep.com.cn/qb/EN/Y2023/V11/I3/207

Fig.1 The current medical paradigm. (Figure designed inspired by a figure in [24].)

Fig.2 A conceptual structure of dLife systems.

Fig.3 Showcases of in data drug experiment using dLife models.

Fig.4 A schematic diagram of digi-carbon experiments using dLife models.

1	M., Loukas, M., Hanna, N., Alsaiegh, M. M. Shoja, R. Tubbs, (2011). Clinical anatomy as practiced by ancient Egyptians. Clin. Anat., 24: 409–415 https://doi.org/10.1002/ca.21155
2	B. Ford, (1982). Bacteria and cells of human origin on van Leeuwenhoek’s sections of 1674. Trans. Am. Microsc. Soc., 101: 1–9 https://doi.org/10.2307/3225566
3	P. Kligfield, (2016). The Bicentennial of the Stethoscope: 1816 to 2016. Am. J. Cardiol., 118: 1601–1602 https://doi.org/10.1016/j.amjcard.2016.08.033
4	R. Mould, (1995). The early history of X-ray diagnosis with emphasis on the contributions of physics 1895–1915. Phys. Med. Biol., 40: 1741–1787 https://doi.org/10.1088/0031-9155/40/11/001
5	W. Fye, (1994). A history of the origin, evolution, and impact of electrocardiography. Am. J. Cardiol., 73: 937–949 https://doi.org/10.1016/0002-9149(94)90135-X
6	J. L. Stone, J. Hughes, (2013). Early history of electroencephalography and establishment of the American Clinical Neurophysiology Society. J. Clin. Neurophysiol., 30: 28–44 https://doi.org/10.1097/WNP.0b013e31827edb2d
7	I. Edler, C. Hertz, (2004). The use of ultrasonic reflectoscope for the continuous recording of the movements of heart walls. Clin. Physiol. Funct. Imaging, 24: 118–136 https://doi.org/10.1111/j.1475-097X.2004.00539.x
8	E. Beckmann, (2006). CT scanning the early days. Br. J. Radiol., 79: 5–8 https://doi.org/10.1259/bjr/29444122
9	T., Ai, J. N., Morelli, X., Hu, D., Hao, F. L., Goerner, B. Ager, V. Runge, (2012). A historical overview of magnetic resonance imaging, focusing on technological innovations. Invest. Radiol., 47: 725–741 https://doi.org/10.1097/RLI.0b013e318272d29f
10	J. Riley, (2005). Estimates of regional and global life expectancy, 1800–2001. Popul. Dev. Rev., 31: 537–543 https://doi.org/10.1111/j.1728-4457.2005.00083.x
11	T. H. N., Ellis, J. M. I., Hofer, G. M., Timmerman-Vaughan, C. J. Coyne, R. Hellens, (2011). Mendel, 150 years on. Trends Plant Sci., 16: 590–596 https://doi.org/10.1016/j.tplants.2011.06.006
12	R. Dahm, D. N. Discovering, (2008). Discovering DNA: Friedrich Miescher and the early years of nucleic acid research. Hum. Genet., 122: 565–581 https://doi.org/10.1007/s00439-007-0433-0
13	A., Thess, I., Hoerr, B. Y., Panah, G. Jung, (2021). Historic nucleic acids isolated by Friedrich Miescher contain RNA besides DNA. Biol. Chem., 402: 1179–1185 https://doi.org/10.1515/hsz-2021-0226
14	H. Hartley, (1951). Origin of the word “protein”. Nature, 168: 244 https://doi.org/10.1038/168244a0
15	J. D. Watson, F. H. Crick, (1953). Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature, 171: 737–738 https://doi.org/10.1038/171737a0
16	R. Wu, (1970). Nucleotide sequence analysis of DNA. I. Partial sequence of the cohesive ends of bacteriophage lambda and 186 DNA. J. Mol. Biol., 51: 501–521 https://doi.org/10.1016/0022-2836(70)90004-5
17	F. S., Collins, M. Morgan, (2003). The Human Genome Project: lessons from large-scale biology. Science, 300: 286–290 https://doi.org/10.1126/science.1084564
18	A., RegevS. A., TeichmannE. S., LanderI., AmitC., BenoistE., BirneyB., BodenmillerP., CampbellP., CarninciM., Clatworthy, (2017) The Human Cell Atlas. eLife, 6, e27041
19	Consortium HuBMAP. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature, 574,187–192
20	S., Chen, Y., Luo, H., Gao, F., Li, Y., Chen, J., Li, R., You, M., Hao, H., Bian, X. Xi, et al.. (2022). hECA: the cell-centric assembly of a cell atlas. iScience, 25: 104318 https://doi.org/10.1016/j.isci.2022.104318
21	M., Vaduganathan, G. A., Mensah, J. V., Turco, V. Fuster, G. Roth, (2022). The global burden of cardiovascular diseases and risk: a compass for future health. J. Am. Coll. Cardiol., 80: 2361–2371 https://doi.org/10.1016/j.jacc.2022.11.005
22	R. L., Siegel, K. D., Miller, H. E. Fuchs, (2022). Cancer statistics, 2022. CA Cancer J. Clin., 72: 7–33 https://doi.org/10.3322/caac.21708
23	M., Moor, O., Banerjee, Z. S. H., Abad, H. M., Krumholz, J., Leskovec, E. J. Topol, (2023). Foundation models for generalist medical artificial intelligence. Nature, 616: 259–265 https://doi.org/10.1038/s41586-023-05881-4
24	on the Learning Health Care System in America & Institute of Medicine Committee. (2013) Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington (DC): National Academies Press
25	J., Li, Z., Qiu, C., Zhang, S., Chen, M., Wang, Q., Meng, H., Lu, L., Wei, H., Lv, W. Zhong, et al.. (2022). ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features. Eur. Radiol., 33: 893–903 https://doi.org/10.1007/s00330-022-09055-0
26	Y., Yuan, B., Liu, P., Xie, M. Q., Zhang, Y., Li, Z. Xie, (2015). Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. Proc. Natl. Acad. Sci. USA, 112: 3158–3163 https://doi.org/10.1073/pnas.1413896112
27	L., Wei, Y., Yuan, T., Hu, S., Li, T., Cheng, J., Lei, Z., Xie, M. Q. Zhang, (2019). Regulation by competition: a hidden layer of gene regulatory network. Quant. Biol., 7: 110–121 https://doi.org/10.1007/s40484-018-0162-5
28	H., Huang, Y., Liu, W., Liao, Y., Cao, Q., Liu, Y., Guo, Y. Lu, (2019). Oncolytic adenovirus programmed by synthetic gene circuit for cancer immunotherapy. Nat. Commun., 10: 4801 https://doi.org/10.1038/s41467-019-12794-2
29	L., Wei, S., Li, P., Zhang, T., Hu, M. Q., Zhang, Z. Xie, (2021). Characterizing microRNA-mediated modulation of gene expression noise and its effect on synthetic gene circuits. Cell Rep., 36: 109573 https://doi.org/10.1016/j.celrep.2021.109573
30	H. O., Demirel, S. Ahmed, V. Duffy, (2022). Digital human modeling: a review and reappraisal of origins, present, and expected future methods for representing humans computationally. Int. J. Hum. Comput. Interact., 38: 897–937 https://doi.org/10.1080/10447318.2021.1976507
31	M. Campbell, (2022). Digital self: the next evolution of the digital human. Computer, 55: 82–86 https://doi.org/10.1109/MC.2022.3148642
32	R. W., Morris, C. A., Bean, G. K., Farber, D., Gallahan, E., Jakobsson, Y., Liu, P. M., Lyster, G. C. Y., Peng, F. S., Roberts, M. Twery, et al.. (2005). Digital biology: an emerging and promising discipline. Trends Biotechnol., 23: 113–117 https://doi.org/10.1016/j.tibtech.2005.01.005
33	J., Corral-Acero, F., Margara, M., Marciniak, C., Rodero, F., Loncaric, Y., Feng, A., Gilbert, J. F., Fernandes, H. A., Bukhari, A. Wajdan, et al.. (2020). The “digital twin” to enable the vision of precision cardiology. Eur. Heart J., 41: 4556–4564 https://doi.org/10.1093/eurheartj/ehaa159
34	K. Subramanian, (2020). Digital twin for drug discovery and development—the virtual liver. J. Indian Inst. Sci., 100: 653–662 https://doi.org/10.1007/s41745-020-00185-2
35	J., Masison, J., Beezley, Y., Mei, H., Ribeiro, A. C., Knapp, L., Sordo Vieira, B., Adhikari, Y., Scindia, M., Grauer, B. Helba, et al.. (2021). A modular computational framework for medical digital twins. Proc. Natl. Acad. Sci. USA, 118: e2024287118 https://doi.org/10.1073/pnas.2024287118
36	A., Mussomeli, A., Parrott, B. Umbenhauer, (2020). Deloitte. TechTrends, 2020: 70–71

[1]	Varshini Vasudevaraja, Jamie Renbarger, Ridhhi Girish Shah, Garrett Kinnebrew, Murray Korc, Limei Wang, Yang Huo, Enze Liu, Lang Li, Lijun Cheng. PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer[J]. Quant. Biol., 2017, 5(4): 380-394.
[2]	Lu Wang, Lipi Acharya, Changxin Bai, Dongxiao Zhu. Transcriptome assembly strategies for precision medicine[J]. Quant. Biol., 2017, 5(4): 280-290.
[3]	Zhen Wang, Zefeng Wang, Yixue Li. Strategic planning for national biomedical big data infrastructure in China[J]. Quant. Biol., 2017, 5(3): 272-275.
[4]	Amal Katrib, William Hsu, Alex Bui, Yi Xing. “RADIOTRANSCRIPTOMICS”: A synergy of imaging and transcriptomics in clinical assessment[J]. Quant. Biol., 2016, 4(1): 1-12.

Viewed

Full text

Abstract

Cited

Shared

Discussed