Scientific models of human disease traditionally involve single gene modifications in animals and 3D cell culture. In recent years, the menu of scientific models has expanded to include organ-on-a-chip technology, 3D printing, microfluidics, and computer modeling, enabling the interrogation of multiple genes and protein interactions.
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Disease Modeling: From Animals to Artificial Intelligence
By Nathan Ni, PhD
Designed by Emily Lavinskas
Rapid developments in technology change how researchers create and use disease models.
Transgenic Models
CRISPR/Cas gene editing makes it faster and easier to generate genetically modified animal models of disease. Today, researchers can manipulate multiple genes simultaneously and create new models in a single generation. CRISPR/ Cas gene editing enables both germline and somatic modulation, broadening the scope of animal disease modeling (1).
Microfluidics
3D multicellular models better recapitulate the cell-cell and cell-extracellular matrix interactions lacking in conventional 2D models, but cannot reproduce in vivo mechanical or physiological signals. Microfluidic technology replicates dynamic nutrient, oxygen, and waste supply/exchange, facilitating more physiologically relevant modeling (2).
3D Printing
3D bioprinting with live cells enables complex modeling using living tissues and microfluidics through one-step fabrication processes. Researchers use 3D printing biofabrication to model infectious diseases (including COVID-19), investigate organ architecture and dysfunction, and facilitate high-throughput drug screening (3).
Artificial Intelligence Guided Modeling
Artificial intelligence — including machine learning, deep learning, and natural language processing — is growing in popularity for collecting, processing, and analyzing large volumes of research and patient data. This facilitates a greater understanding of disease properties and the generation of in silico models for experimentation and simulation such as predicting drug responses or preclinical disease screening (4-6).
Digital Twins
In medicine, digital twins dynamically represent molecular, physiological, and lifestyle statuses across different treatments and times. Researchers train digital twin models using individual and population-level data to better understand complex biological networks such as the immune system (7). Digital twins help researchers tailor treatment strategies for diverse diseases such as cancer (8, 9).
References
1. Musunuru, K. CRISPR and cardiovascular diseases. Cardiovasc Res cvac048, (2022).
2. Kim, S.K. et al. Organoid engineering with microfluidics and biomaterials for liver, lung disease, and cancer modeling. Acta Biomater 132, 37-51 (2021).
3. Yi, H.G. et al. Application of 3D bioprinting in the prevention and the therapy for human diseases. Sig Transduct Target Ther 6(177) (2021).
4. Ahn, J.C. et al. Application of artificial intelligence for the diagnosis and treatment of liver diseases. Hepatology 73(6), 2546-63 (2021).
5. Ballester, P.J. et al. Artificial intelligence for drug response prediction in disease models. Brief Bioinform 23(1), bbab450 (2022).
6. Gaubert, S. et al. INSIGHT-preAD study group. A machine learning approach to screen for preclinical Alzheimer’s disease. Neurobiol Aging 105, 205-216 (2021).
7. Laubenbacher, R. et al. Building digital twins of the human immune system: toward a roadmap. npj Digit Med 5(64), (2022).
8. Hernandez-Boussard, T. et al. Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nat Med 27, 2065-66 (2021).
9. Björnsson, B. et al. Digital twins to personalize medicine. Genome Med 12(4), (2020).