Computational Modeling
Computational Modeling - Related Videos
11:19
Dorsal Column Steerability with Dual Parallel Leads using Dedicated Power Sources: A Computational Model
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Cited by 16 •
2011
Using a mathematical model of spinal cord stimulation, we found that a multi-source system with independent power sources for each contact can target more central points of stimulation on the dorsal column (100 vs 3) and has 50-fold more field steering resolution (0.02mm vs 1mm) than a single-source system.
10:23
A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment
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Cited by 3 •
2023
The article describes a protocol to simulate the transient temperature profiles and the coupled spatiotemporal variation of the interstitial fluid pressure following the heating delivered by a dipolar radiofrequency hyperthermia system. The protocol can be used to assess the response of biophysical parameters characterizing the tumor microenvironment to interventional hyperthermia techniques.
13:09
Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
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Cited by 26 •
2012
Fat content analysis is routinely conducted in studies utilizing murine obesity models. Emerging methods in small animal CT imaging and analysis are providing for longitudinal detail rich fat content analysis. Here we detail step by step procedures for performing small animal CT imaging, analysis, and visualization.
12:39
Computational Fluid Dynamics Simulations of Blood Flow in a Cerebral Aneurysm
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2023
Source: Joseph C. Muskat, Vitaliy L. Rayz, and Craig J. Goergen, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana The objective of this video is to describe recent advancements of computational fluid dynamic (CFD) simulations based on patient- or animal-specific vasculature. Here, subject-based vessel segmentations were created, and, using a combination of open-source and commercial tools, a high-resolution numerical solution was determined within a flow model.
09:57
How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
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Cited by 168 •
2012
Measuring gyrification (cortical folding) at any age represents a window into early brain development. Hence, we previously developed an algorithm to measure local gyrification at thousands of points over the hemisphere1. In this paper, we detail the computation of this local gyrification index.
FAQs
Assumptions determine which biological relationships the model represents and which factors it leaves out. They therefore shape the simulated behavior and the kinds of conclusions the model can support. Researchers must compare model results with experimental or observational data to evaluate whether those assumptions provide a reasonable representation of the biological system under study.
Parameters translate biological processes into adjustable quantities within the model. Researchers can modify these values and compare the resulting simulations with experimental or observational data. This process helps identify parameter settings that are consistent with observed behavior and shows how different biological conditions may alter the system’s predicted response.
By examining how simulated outcomes change when model variables are adjusted, researchers can determine which factors have the strongest effects on system behavior. This approach is useful when many biological processes interact or when some variables are difficult to measure directly. The results can help prioritize experiments and focus attention on potentially important relationships.
Simulations can expose relationships among biological components that may not be accessible through direct measurement alone. They can also test hypotheses and explore predicted responses under different conditions. In this way, modeling extends experimental or observational work by providing a structured way to examine system behavior beyond the conditions that have already been measured.
A typical workflow begins by encoding a biological process into a mathematical or algorithmic representation. Researchers then adjust relevant parameters, run simulations, and compare the outcomes with experimental or observational data. They use the comparison to assess the model, refine its representation when appropriate, and examine predictions for conditions that may be difficult to test directly.
Computational modeling applies across molecular, cellular, ecological, and evolutionary biology. At these different scales, it can represent processes such as gene regulation, cellular signaling, or population change. This broad scope allows researchers to use related modeling principles for questions involving individual biological components, interacting cells, populations, or changes across biological systems.
Reliability depends on the suitability of the model’s assumptions, the quality of the data used with it, and careful validation against real biological systems. Agreement between simulations and experimental or observational data strengthens confidence, but it does not remove the need to consider omitted factors and the conditions under which the model was evaluated.