Related Experiment Video
Updated: Apr 26, 2026
09:20
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
6.1K
Mathematical modeling of physiological systems: an essential tool for discovery
Patric Glynn1, Sathya D Unudurthi1, Thomas J Hund2
1The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA; Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA.
Life Sciences
|July 28, 2014
Summary
Mathematical models are crucial for understanding complex biological systems, from DNA interactions to disease prediction. This review highlights their value, particularly in studying excitable cells.
Area of Science:
- Systems Biology
- Computational Biology
- Biophysics
Background:
- Mathematical models are essential for dissecting complex biological systems.
- They elucidate interrelations between components like DNA, proteins, and signaling molecules.
- Modeling aids in predicting system behavior under altered conditions, such as disease states.
Purpose of the Study:
- To provide a historical overview of mathematical modeling in biology.
- To highlight key applications of modeling, with a focus on excitable cells.
- To address the value and utility of mathematical models to experimental scientists.
Main Methods:
- Review of historical developments in biological modeling.
- Compilation of significant applications of mathematical models in diverse biological processes.
- Emphasis on modeling techniques applied to the study of excitable cells.
Main Results:
- Mathematical modeling has significantly advanced understanding of enzyme kinetics, metabolic networks, signal transduction, gene regulation, and electrophysiology.
- Recent advancements in high-throughput data and computational methods have increased the centrality of modeling in biological research.
- The review demonstrates the tangible benefits and predictive power of mathematical models in biological systems.
Conclusions:
- Mathematical modeling is an indispensable tool for modern biological research, offering deep insights and predictive capabilities.
- The application of modeling, especially in excitable cells, continues to expand.
- Addressing skepticism requires clearly demonstrating the value and practical contributions of theoretical models to experimental biology.
Related Concept Videos
01:26Pharmacokinetic Models: Comparison and Selection Criterion
485
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
485
01:21Mechanistic Models: Overview of Compartment Models
585
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
585
01:15Model Approaches for Pharmacokinetic Data: Physiological Models
371
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
371
01:20Pharmacokinetic Models: Overview
2.6K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.6K
00:57Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models
500
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
500
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
333
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
333