Computational Modeling: How It is Supporting Scientific Learning Process ! Apply Your Brain to Master Computational Modeling!!

Abstract: 

Computational modeling is the process of mathematically and logically representing a problem in order to simulate and experimentally verify different properties of intelligence.

Computational models predict how the composition, and motion of structures interact with the underlying surfaces to afect what happens during an earthquake. Computational models are used to simulate and study complex biological systems.

Similarly, Computational modeling is the use of computers to study and simulate complex systems through mathematics, physics, and computer science. It involves using a mathematical model and adjusting its variables to simulate different outcomes. Here are some ways computational modeling is used: 

 

Biology

Computational models can be used to study the development of disease, including how it affects tissues and organs. For example, computational models can be used to study the spread of infectious diseases like influenza. 

 

Medicine

Computational models can be used to identify evolving variants of viruses, which can help inform public health responses. They can also help medical professionals provide personalized care to patients. 

 

Other scientific fields

Computational modeling can be used to study enzymatic mechanisms, binding energies, and gene annotation. 

 

Some other things to know about computational modeling include: 

 

Computational models can be used to identify the most likely laboratory experiments to solve a problem. 

 

Computational modeling can involve symbolic modeling, which focuses on abstract mental functions, or subsymbolic modeling, which follows the neural properties of the human brain. 

 

When developing computational models, it's important to consider the frame of reference, as a model developed for one frame may not be suitable for another. 

After engaging with this  article you will be able equipped with in-depth knowledge to understand about Computational Modeling .

Keywords:

Computational Modeling,  Variables, Simulate the System's Behavior, Simulation, Human Brain

Learning Outcomes

After engaging with this article, you will be empowered with in-depth knowledge of the following essential topics on Computational Modeling  :

1. Introduction of Computational Modeling

2. What's Computational Modeling?

3. Why Computational Modeling is important?

4. Elements of Computational Modeling

5. Characteristics of Computational Modeling

6. Types of Computational Modeling

7. Steps of Computational Modeling

8. Computational Modeling  Tools and Techniques

9. Computational Modeling Simulation 

10. Benefits of Computational Modeling

11. Limitations of Computational Modeling

12. Strategies for  Computational Modeling

13. Conclusions

14. FAQs

References 

1. Introduction of 

Computational Modeling

Have you ever wondered about computational Modeling. What if it has not invented. Why is this so useful in simulation and modeling?  Here is the answer.

Computational modeling is a technique that uses computers to study complex systems by simulating them with mathematics, physics, and computer science. It can be used to make predictions and simulations in many fields, including science, engineering, economics, psychology, and computer science. 

 

Here are some things to know about computational modeling: 

 

How it works

Computational models use variables to represent a system, and the variables are adjusted to simulate the system's behavior. The results of the simulation are then observed. 

 

How it's used

Computational modeling can be used to study systems at multiple levels, which is known as multiscale modeling. For example, models of disease development can include molecular processes, cell interactions, and how those changes affect tissues and organs. 

 

How it's useful

Computational modeling can help scientists identify which laboratory experiments are most likely to solve a problem. It can also help medical professionals provide personalized care by leveraging data-led insights and predictive analytics. 

 

Some common models

Some examples of computational models include weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, and neural network models. 

2. What's Computational Modeling?

Modeling is the use of computers to simulate and study complex systems by applying mathematics, physics, and computer science. It involves creating models that contain variables that represent the system being studied, and then adjusting those variables to observe the outcomes. 

 

Computational modeling has many uses, including: 

 

NASA

NASA uses computational modeling to help solve problems that are expensive, time consuming, or have safety hazards. 

 

Healthcare

Computational modeling can help medical professionals provide personalized care to patients by leveraging data-led insights and predictive analytics. 

 

Science

Computational modeling can help scientists predict enzymatic mechanisms, binding energies, and more. 

 

Computational modeling can be used in two approaches:

Symbolic modeling: Uses symbols to focus on abstract mental functions.

Subsymbolic modeling: Follows the neural and associative properties of the human brain. 

 

3. Why Computational Modeling is important?

Computational modeling is important because it helps us understand, design, and predict complex systems and processes. It is used in many fields, including science, engineering, economics, psychology, and computer science. Here are some reasons why computational modeling is important: 

 

Simulate experiments

Computational modeling allows scientists to run thousands of simulated experiments on a computer. This helps them identify which laboratory experiments are most likely to solve a problem. 

 

Study complex systems

Computational models are used to study complex systems that are nonlinear and don't have simple analytical solutions. 

 

Multiscale modeling

Computational models can study systems at multiple levels, such as molecular processes, cell interactions, and how those changes affect organs and tissues. 

 

Stem cell research

Computational modeling can help researchers understand the mechanisms of stem cell dysfunction and develop regenerative medicine strategies. 

 

Decision making

Computational models can help people ask the right questions and make decisions. 

 

4. Elements of Computational Modeling

Some elements of computational modeling include: 

 

Finite element method

A computational method that breaks down a CAD model into small, simple geometric shapes. This collection of shapes is called a finite-element mesh. The finite element method is often used in biomechanical studies to analyze stress. 

 

Computer simulation

A tool for mathematical modeling that uses computer simulations to examine how a system behaves over time. Computer simulations can be used to model natural systems in physics, chemistry, biology, and more. 

 

Data visualization

A tool for understanding and communicating large amounts of data generated by computational simulations. 

 

Computational materials science

A field that uses modeling, simulation, theory, and informatics to understand materials. The goals of this field include discovering new materials, determining material behavior, and explaining experiments. 

 

Numerical analysis

A pillar of computational algorithms that supports accurate results for machine learning algorithms. 

5. Characteristics of Computational Modeling

Computational modeling uses computers to simulate and study complex systems using mathematics, physics, and computer science. Some characteristics of computational modeling include: 

 

Simulation

Computational models use variables to represent a system and simulate it by adjusting the variables and observing the outcomes. 

 

Validation

Computational models can replicate physical experiments, and the results can be used to validate the model. 

 

Experimentation

Computational models can perform experiments that are not feasible in a physical setting, such as running an arbitrary number of experiments from the same starting configuration. 

 

Explanation

Computational models can provide a human-understandable explanation of a real-world phenomenon. 

 

Prediction

Computational models can make testable predictions about new data. 

 

Probing

Computational models can be modified, and the results of those modifications can be used to make further predictions. 

 

Collaboration

Computational modeling allows scientists to collaborate closely with experimentalists. 

 

Applications

Computational modeling is used in many fields, including medicine, physics, engineering, and natural sciences. 

 

6. Types of Computational Modeling

There are several types of computational modeling, including: 

 

Cellular automata (CA)

An abstract model of dynamic systems that change over time and space. CA are used by composers to model music, which is the organization of sounds in space and time. 

 

Simulation modeling

A type of computer modeling that uses system analysis to design a generalized model of a simulated system. 

 

Agent-based modeling

A model that gives a collection of autonomous agents simple rules and lets them interact with each other and their environment. 

 

Computational cognition

A research approach that focuses on developing computational models that fit empirical evidence and consider human experience. 

 

Computer aided drug design (CADD)

A computational method used in drug design and discovery. 

 

System dynamics

A computational modeling method that helps understand the dynamic interactions that influence behavior in complex systems. 

 

Multiscale modeling

A technique that uses multiple models at different scales to describe the behavior of materials. 

 

Bayesian models

A computational model of human performance that explains performance by modeling human behavior as if they were optimal observers. 

 

7. Steps of Computational Modeling

Some steps of computational modeling include: 

 

Problem identification: Clearly define the problem to be solved 

 

Decomposition: Break the problem down into smaller, more manageable subproblems 

 

Algorithm selection: Choose an appropriate algorithm to execute on the training data to create the model 

 

Simulation setup: Reconstruct the experimental setup in silico to simulate the mathematical model 

 

Communication: Communicate the model results through a user interface or visualization 

 

Abstraction is an important principle in computational thinking, and is the process of removing excessive details to arrive at a representation of a problem that consists of only the key features. 

 

8. Computational Modeling Tools and Techniques

Some computational modeling tools include: 

 

Boolean models

A powerful tool for modeling and concepting that allows users to quickly iterate through shapes, combine objects, and make cuts. 

 

Molecular modeling

A group of computational methods that simulate physicochemical systems to generate, manipulate, and analyze realistic representations of molecular structures. 

 

Agent-based models

A type of computational model used to model complex systems by creating, analyzing, and testing the model. 

 

Physiologically-based pharmacokinetic (PBPK) modeling

A computational approach that simulates the absorption, distribution, metabolism, and elimination (ADME) of chemical substances in organisms. 

 

Quantitative models

A computational model that quantitatively analyzes the behavior of a biochemical reaction over time. 

 

Bayesian modeling and data analysis

A powerful tool for computational research that involves writing a probability model and fitting it with observed data. 

 

Codon optimization

A computational design tool that uses a dynamic programming algorithm to optimize codon usage for heterologously expressing non-endogenous genes in host organisms. 

 

Computational biochemistry

A computational model of an enzyme that explains microscopic details of the catalytic process and predicts macroscopic experimental findings. 

 

Computer simulation

A computational model of real or hypothesized situations or natural phenomena that allows users to explore the implications by manipulating or changing parameters within them. 

 

9. Computational Modeling Simulation 

Computational modeling and simulation is a scientific method that uses computers to study and simulate complex systems. It involves creating mathematical, graphical, or algorithmic representations of real-world systems, and then running simulations to study, predict, or optimize their behavior. 

 

Computational modeling and simulation is used in many scientific and engineering fields, including: Fluid dynamics, Materials science, Transportation systems, and Biological systems. 

 

Here are some benefits of computational modeling and simulation: 

 

Multiscale modeling

Computational models can study biological systems at multiple levels, including molecular processes, cell interactions, and how these changes affect tissues and organs. 

 

Collaboration

Computational modeling allows scientists to work closely with experimentalists. 

 

Drug discovery

Computational techniques have been used to discover anticancer drugs and to study diseases ranging from cancer to neurodegenerative diseases. 

 

Cost and labor reduction

Computational techniques are expected to reduce the cost and labor of experimental work. 

 

10. Benefits of Computational Modeling

Computational modeling is a standard tool in many fields, and has many benefits, including: 

 

Understanding biological systems: Computational models can help test hypotheses, interpret experiments, and trace chains of causation. They can also help avoid experimental difficulties and permit experiments that aren't currently feasible. 

 

Predicting disease development: Computational models can help predict the likelihood of disease development and progression. They can also help identify evolving variants of viruses, which is important for informing public health responses. 

 

Optimizing cancer treatment: Computational models can help optimize cancer treatment. 

 

Diagnosing Alzheimer's and breast cancer: Computational models can help diagnose Alzheimer's and breast cancer. 

 

Analyzing complex data: Computational models can help analyze complex data and interpret mathematical models. 

 

Investigating geologic carbon storage: Computational models can help investigate the safety and permanence of geologic carbon storage. 

 

Describing microbial distribution: Computational models can help describe the dynamics of microbial communities in lakes. 

 

11. Limitations of Computational Modeling

Computational models have several limitations, including: 

 

Complexity: Some systems are too complex to be accurately represented by mathematical equations. For example, the human brain is a complex system that we don't fully understand. 

 

Validation: Models need to be validated against real-world data, but this data may not always be available. 

 

Assumptions: Models always involve assumptions. 

 

Interpretation: Models should be interpreted appropriately, and not used as the sole source of decision-making. 

 

Context: Models need to be used within a context of knowledge and experience. 

 

Specification: It's difficult to decide which factors to include in a model. 

 

Reliability: The reliability of outputs depends on the data used. 

 

Statistical uncertainty: Statistical uncertainty can be a limitation. 

 

Knowledge gaps: Knowledge gaps can be a limitation. 

12. Tips, Tricks and Strategies for  Computational Modeling

To start learning computational modeling, I would recommend the following steps:

1. Gain a strong foundation in computer science and programming. Familiarize yourself with data structures, algorithms, and programming languages like Python, MATLAB, or R.
2. Explore the different domains of computational modeling, such as numerical simulations, agent-based modeling, system dynamics, and machine learning. Determine which area(s) are of most interest to you.
3. Learn the mathematical and statistical concepts that underpin computational modeling, such as differential equations, linear algebra, probability, and statistics. These form the theoretical basis for many modeling techniques.
4. Identify relevant computational modeling tools and software, such as MATLAB, R, NetLogo, or Wolfram Mathematica. Invest time in learning how to use these tools effectively.
5. Work through tutorials, examples, and case studies to gain practical experience in applying computational modeling techniques to real-world problems in fields like biology, economics, engineering, or social sciences.
6. Consider taking online courses or enrolling in university programs that offer coursework or degrees in computational modeling, scientific computing, or a related discipline.
7. Engage with the computational modeling community by attending conferences, workshops, or joining online forums to learn from experienced practitioners and collaborate on projects.

The key is to build a solid foundation in both the theoretical and practical aspects of computational modeling, while also gaining domain-specific knowledge relevant to your areas of interest. 

13. Conclusions

Computational modeling has many applications and can be used in a variety of fields, including biology, medicine, and engineering. Here are some conclusions about computational modeling: 

 

Scientific discovery

Computational modeling can be considered a third paradigm of scientific discovery, along with theory and experiment. It can help researchers gain new insights and develop technological innovations. 

 

Medical care

Computational modeling can help improve medical care in a number of ways, including: 

 

Tracking infectious diseases: Computational models can help identify effective interventions to reduce the spread of disease. 

 

Clinical decision support: Computational models can help doctors provide informed care by analyzing patient data and recommending treatments. 

 

Drug design: Computational modeling can help researchers design safer drugs with fewer side effects. 

 

Ethical considerations

As computational models are used to inform decisions in clinical, industrial, and environmental settings, it's important to balance scientific advancement with ethical considerations. 

 

Documentation

Models should be well documented to explain what they do and how they do it. Documentation should be available for developers, analysts, and users. 

 

14. FAQs

Q. How can computational modeling improve medical care and research?

Ans.

Tracking infectious diseases. Computational models are being used to track infectious diseases in populations, identify the most effective interventions, and monitor and adjust interventions to reduce the spread of disease. Identifying and implementing interventions that curb the spread of disease are critical for saving lives and reducing stress on the healthcare system during infectious disease pandemics.

Clinical decision support. Computational models intelligently gather, filter, analyze and present health information to provide guidance to doctors for disease treatment based on detailed characteristics of each patient. The systems help to provide informed and consistent care of a patient as they transfer to appropriate hospital facilities and departments and receive various tests during their course of treatment.

Predicting drug side effects. Researchers use computational modeling to help design drugs that will be the safest for patients and least likely to have side effects. The approach can reduce the many years needed to develop a safe and effective medication.

Q. How Computational Modeling support teachers in teaching learning process ?

Ans.

Teachers will know students are understanding systems with computational models because they may observe the following student actions:

Using and Modifying Computational Models to Explore a Driving Question

• Identifying a question to explore using a computational model
• Setting up multiple and different scenarios to collect data from a computational model
• Making predictions about how the model will behave with different inputs

Creating Computational Models

• Identifying different parts of the system that the model is representing
• Defining relationships between different parts of a system
• Automating relationships between parts of the system with a flowchart or programming/modeling software

Assessing Computational Models

• Considering how the model represents the real-world system
• Considering bias in the outputs of a computational model

References 

https://digitalcommons.odu.edu/msve_books

Here are some articles and resources on computational modeling: 

 

A planning support system using interactive optimization

This article from Urban Energy Systems for Low-Carbon Cities discusses how computational models can help with urban planning challenges. 

 

Computational modeling in hyperthermia

This article from International Journal of Hyperthermia covers topics such as SAR-based optimization cost function, MR compatible hyperthermia applicator, and magnetic hyperthermia. 

 

Computational modeling for decision-making

This article from Royal Society Publishing discusses the importance of documentation for computational models, including documentation for developers, analysts, and users. 

 

Computational and Mathematical Organization Theory

This journal provides a forum for interdisciplinary research that combines computation, organizations, and society. 

 

Computational models 

- Latest research and news - Nature

This article from Nature Portfolio discusses computational models, which are mathematical models that are simulated using computation to study complex systems.