Patient Specific Disease Modeling
Patient Specific Disease Modeling - Related Videos
12:09
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Cited by 14 •
2013
A methodology to estimate ventricular fiber orientations from in vivo images of patient heart geometries for personalized modeling is described. Validation of the methodology performed using normal and failing canine hearts demonstrate that that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.
08:50
Assessment of Vascular Function in Patients With Chronic Kidney Disease
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Cited by 25 •
2014
The degree of vascular dysfunction and contributing physiological mechanisms can be assessed in patients with chronic kidney disease by measuring brachial artery flow-mediated dilation, aortic pulse-wave velocity, and vascular endothelial cell protein expression.
11:26
Isolation and Immortalization of Patient-derived Cell Lines from Muscle Biopsy for Disease Modeling
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Cited by 19 •
2015
This protocol describes techniques for live cell isolation and primary culture of myogenic and fibroblast cell lines from muscle or skin tissue. A technique for the immortalization of these cell lines is also described. Altogether, these protocols provide a reliable tool to generate and preserve patient-derived cells for downstream applications.
07:30
3D Printing Model of a Patient's Specific Lumbar Vertebra
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Cited by 4 •
2023
This study aims to create a 3D-printed model of a patient-specific lumbar vertebra, which contains both the vertebra and spinal nerve models fused from high-resolution computed tomography (HRCT) and MRI-Dixon data.
11:01
Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
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2011
The methodology for collecting high-density event-related potential data while patients with Alzheimer's disease perform a recognition memory task is reviewed. This protocol will include subject preparation, quality assurance, data acquisition, and data analysis.
FAQs
Patient-specific disease modeling preserves biological variation by building experiments around an individual’s genetic and cellular characteristics. Patient-derived cells, induced pluripotent stem cells, organoids, tissue-engineered systems, and digital models can represent disease at different levels. This allows investigators to examine how molecular changes are associated with tissue-level outcomes instead of relying only on generalized disease behavior.
The key comparison is between disease phenotypes under controlled conditions. By holding experimental conditions consistent while examining models from different patients, researchers can distinguish features associated with an individual disease state from effects caused by the test environment. That comparison supports investigation of disease mechanisms and helps reveal why treatment responses may differ across genetic and cellular backgrounds.
Unlike one-size-fits-all models, these systems are intended to preserve patient-linked characteristics that standard models may miss. The distinction matters because a model can be evaluated not only for whether it reproduces a disease feature, but also for whether it reflects the specific molecular and cellular context relevant to one patient. This provides a more individualized basis for studying therapeutic strategies.
Model choice determines which disease features can be examined. Patient-derived cells retain a direct connection to the individual, while induced pluripotent stem cells, organoids, tissue-engineered systems, and digital models provide different laboratory or computational representations. Selecting among these formats helps align the model with the biological scale of the question, from molecular changes to tissue-level outcomes.
A general workflow begins by creating a laboratory or computational model from patient-linked biological characteristics, then examining disease phenotypes under controlled conditions. Investigators can compare the observed features and connect molecular changes with tissue-level outcomes. The resulting comparisons provide a basis for testing treatment responses, evaluating biomarkers, or assessing therapeutic strategies without assuming that every patient will behave identically.
Drug screening is one major application: candidate treatments can be assessed in a model that reflects a patient’s genetic and cellular characteristics. The same framework can support biomarker evaluation and assessment of therapeutic strategies. Its value lies in generating evidence about disease and response in a patient-relevant context, rather than depending exclusively on results from generalized models.
In bioengineering, the approach connects biological material, engineered tissues, and computational representations with questions about disease and treatment. It can link molecular changes to tissue-level consequences while supporting controlled comparisons among disease phenotypes. This combination makes the models useful for precision medicine, where the objective is to account for individual variation when investigating drugs, biomarkers, and therapeutic options.