Computational Modeling

Extracted Attributes

• Applications

  Drug development, weather forecasting, flight simulators, clinical decision support, and infectious disease tracking

• Primary Fields

  Computer Science, Computability Theory, Computational Complexity Theory, Biomedical Engineering

• Core Components

  Computation units, memory, communication protocols, and mathematical variables

• Primary Benefit

  Allows performance measurement independent of specific technology implementations

• Regulatory Status

  Proposed replacement for animal studies in FDA drug approval processes

• Research Institution

  Institute of Computational Modeling SB RAS (Krasnoyarsk, Russia)

Timeline

• The National Institute of Biomedical Imaging and Bioengineering (NIBIB) publishes a fact sheet detailing the use of computational modeling in tracking infectious diseases and clinical decision support. (Source: NIBIB Fact Sheet)

  2020-05-01

• FDA Commissioner Marty Makary discusses the integration of computational modeling into FDA reforms at the JP Morgan Healthcare Conference in San Francisco. (Source: Document 065d2e96-4d40-49bd-8511-d8d35f8b01f4)

  2025-01-13

Model of computation

In computer science, and more specifically in computability theory and computational complexity theory, a model of computation is a model that describes how an output of a mathematical function is computed given an input. A model of computation describes how units of computations, memories, and communications are organized. The computational complexity of an algorithm can be measured given a model of computation. Using a model allows studying the performance of algorithms independently of the variations that are specific to particular implementations and specific technology.

Web Search Results

• Computational Modeling - Sage Research Methods

  # Computational Modeling ## Summary ## Contents Computational modelling allows researchers to combine the rich detail of qualitative research with the rigour of quantitative and formal research, as well as to represent complex structures and processes within a theoretical model. After an introduction to modelling, the authors discuss the role of computational methods in the social sciences. They treat computational methods, including dynamic simulation, knowledge-based models and machine learning, as a single broad class of research tools and develop a framework for incorporating them within established traditions of social science research. They provide a concise description of each method and a variety of social science illustrations, including four detailed examples.

• Computational Modeling

  ## What is computational modeling? Computational modeling is the use of computers to simulate and study complex systems using mathematics, physics, and computer science. [...] 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 augment the use of animal models and potentially reduce the many years needed to develop safe and effective medications. [...] Complex systems are characterized by numerous variables (factors) that can affect how the system functions, which can ultimately influence its outcomes. Using computational modeling, complex systems are studied in a virtual environment using variables that define each system. The computer model then simulates the system under different conditions, creating simulated outputs or predictions. For example, weather forecasting models make predictions based on numerous atmospheric variables, and flight simulators use complex equations that govern how aircraft fly and react to variables such as turbulence, air density, and precipitation.

• [PDF] Computational Modeling

  National Institute of Biomedical Imaging and Bioengineering Computational Modeling MAY 2020 National Institute of Biomedical Imaging and Bioengineering What is Computational Modeling? Computational modeling is the use of computers to simulate and study complex systems using mathe-matics, physics and computer science. A computational model contains numerous variables that char-acterize the system being studied. Simulation is done by adjusting the variables alone or in combination and observing the outcomes. Computer modeling allows scientists to conduct thousands of simulated experiments by computer. The thousands of computer experiments identify the handful of laboratory experiments that are most likely to solve the problem being studied. Today’s computational models can study a biological [...] Computational models are used to simulate and study complex biological systems. Image Courtesy ISB. How can computational modeling improve medical care and research? Tracking infectious diseases Computational models are being used to track infectious diseases in populations, identify the most efective interventions, and monitor and adjust interventions to reduce the spread of disease. Identifying and implementing interventions that curb 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 [...] Today’s computational models can study a biological system at multiple levels. Models of how disease develops include molecular processes, cell to cell interactions, and how those changes afect tissues and organs. Studying systems at multiple levels is known as multiscale modeling (MSM). How is computational modeling used to study complex systems? f Weather forecasting models make predictions based on numerous atmospheric factors. Accurate weather predictions can protect life and property and help utility companies plan for power increases that occur with extreme climate shifs.

• AM Simulation Software - Computational Modeling

  Search this site Embedded Files AM Simulation Software Computational Modeling: A Smart Approach to Smarter Experiments Computational modeling has become an invaluable tool in modern science, offering the ability to simulate complex systems and predict outcomes with remarkable precision. One of its most significant advantages lies in its ability to analyze data before committing to real-world experiments, saving valuable time, money, and resources. By utilizing computational models, researchers can gain critical insights into the behavior of a system, identify potential problems, and design more effective experiments—all before setting foot in a lab. It's like testing your outfit in front of the mirror before heading out—minus the judgmental stares. [...] In addition to enhancing the design and efficiency of experiments, computational models also help identify potential risks and limitations in experimental setups before they are conducted. By simulating different conditions and tweaking variables within the model, researchers can anticipate potential failures or challenges in their experiments. This proactive approach helps ensure that experiments are better prepared and that any potential issues are addressed early on—ultimately saving both time and money. In fields such as environmental science, where resources are often limited, computational modeling can provide a realistic preview of how experimental processes will play out, enabling scientists to avoid costly mistakes and focus their efforts on the most promising methods. It’s like [...] Moreover, computational modeling plays a crucial role in optimizing experiments before they take place. By testing different scenarios within the model, researchers can simulate outcomes, identify key variables, and refine hypotheses, all without the need for immediate physical experimentation. This reduces the risk of wasted resources and ensures that only the most viable experiments are pursued. In industries like pharmaceuticals, where experimentation can be costly and time-consuming, models can narrow down the search for effective solutions before the first trial is even conducted. The cost savings alone make it an indispensable tool in the research and development process. It's like ordering a pizza with extra cheese: you don’t want to end up with a crust and no toppings after all

• An introduction to Computational Modelling, Foundations of ...

  realize this whole process of uh making the assumption, formulating the problem, solving the problem and then going back to the real world, making some additional experiments and realizing what's needed. Okay. So now let's look into computational modeling. Uh so actually computational modeling is the use of computer programs to simulate a complex system. Uh this is um usually used when analytical solutions are not uh readily available and there are lot of lots of examples in weather forecasting, earth simulators, uh biomedical engineering and as we said the focus today will be on healthcare. So we'll see a few of these applications. Uh computational models same way as um mathematical models can provide insights into the model but they also allow us to run many many many simulations. So we [...] that are extending some on medical imaging and we actually recently had a training event on that on the segment anything model which is on our website uh if you're interested to have a look at that. Um okay so we've shown a lot of applications in health care. Of course there is a growing need for more experiments and more data and we can see that uh computational modeling is gaining ground as a tool for accelerating research and translation. Of course, there are still a lot of model uncertainties and we can always say that a model is only as good as the knowledge data and assumptions that underpin it or some people can be less optimistic and say that all models are wrong but some are useful. uh future directions uh of course there will be increased computational complexity increasing [...] and uh stepby-step uh manner. Okay. So now let's focus on computational modeling in healthcare. Uh there are various examples. So for example uh there are pharmaccoinetic models for drug uh development where we you can simulate the drug absorption distribution and uh how that will work and this is important because it can reduce the need for animal testing and expedite uh drug development. Uh there are also soft tissue biomechanics for understanding function and optimizing treatment. Uh there's a lot going on on epidemiological modeling. So for example during COVID time computational models of this kind were very important in understanding how the virus would spread. Um sorry uh there's also models of surgical simulation which is basically creating a virtual environment to assess the