Related Experiment Video
Updated: May 10, 2026
15:30
A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
Published on: August 5, 2020
Predictive modelling of complex agronomic and biological systems
Joost J B Keurentjes1, Jaap Molenaar, Bas J Zwaan
1Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands. joost.keurentjes@wur.nl
Plant, Cell & Environment
|June 20, 2013
Summary
Mathematical modeling helps unravel biological complexity. This review explores its evolution, current methods, and future challenges in biology.
Area of Science:
- Systems biology
- Computational biology
- Mathematical biology
Background:
- Biological systems exhibit immense complexity in function and regulation.
- Describing and understanding this complexity has long challenged scientists.
- Mathematics offers a powerful framework for simplifying and analyzing biological intricacies.
Purpose of the Study:
- To provide an overview of the historical progress in biological modeling.
- To discuss prevalent approaches for iterative modeling cycles in contemporary biology.
- To advocate for versatile modeling strategies.
Main Methods:
- Review of historical and current modeling techniques in biology.
- Discussion of parameter estimation, model reduction, and network reconstruction.
- Exploration of challenges in mathematical interpretation of in vivo biological complexity.
Main Results:
- Predictive modeling leverages existing knowledge to forecast system behavior under different scenarios.
- Increasingly detailed biological data offer opportunities for advanced modeling and experimental design.
- Versatility in modeling approaches is crucial for addressing biological complexity.
Conclusions:
- Mathematical modeling is essential for understanding complex biological systems.
- Iterative modeling cycles and diverse approaches are key to advancing biological insights.
- Overcoming challenges in translating in vivo complexity into mathematical models is vital for future research.
Related Concept Videos
01:25Multiple Regression
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
02:16Light Acquisition
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:28Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
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.