Computational Modeling Methods for Neuroscientists. Computational Neuroscience. Edited by Erik De Schutter. Cambridge (Massachusetts): MIT Press. $50.00. xii + 419 p.; ill.; index. ISBN: 978-0-262-01327-7. 2010.

2012 ◽  
Vol 87 (3) ◽  
pp. 261-262
Author(s):  
Giancarlo La Camera
Keyword(s):  
Computational Modeling ◽  
Computational Neuroscience ◽  
Modeling Methods

scholarly journals Anatomy of a Psychological Theory: Integrating Construct-Validation and Computational-Modeling Methods to Advance Theorizing

2021 ◽  
pp. 174569162096679
Author(s):  
Ivan Grahek ◽  
Mark Schaller ◽  
Jennifer L. Tackett
Keyword(s):  
Computational Modeling ◽  
Construct Validation ◽  
Theory Development ◽  
Psychological Theory ◽  
Modeling Methods ◽  
Psychological Theories ◽  
Development Processes ◽  
Convergence And Divergence

Discussions about the replicability of psychological studies have primarily focused on improving research methods and practices, with less attention paid to the role of well-specified theories in facilitating the production of reliable empirical results. The field is currently in need of clearly articulated steps to theory specification and development, particularly regarding frameworks that may generalize across different fields of psychology. Here we focus on two approaches to theory specification and development that are typically associated with distinct research traditions: computational modeling and construct validation. We outline the points of convergence and divergence between them to illuminate the anatomy of a scientific theory in psychology—what a well-specified theory should contain and how it should be interrogated and revised through iterative theory-development processes. We propose how these two approaches can be used in complementary ways to increase the quality of explanations and the precision of predictions offered by psychological theories.

scholarly journals Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

2021 ◽  
Vol 12 ◽  
Author(s):  
Renee Dale ◽  
Scott Oswald ◽  
Amogh Jalihal ◽  
Mary-Francis LaPorte ◽  
Daniel M. Fletcher ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Computational Modeling ◽  
Quantitative Methods ◽  
Plant Biology ◽  
Experimental Plant ◽  
Modeling Methods ◽  
Modeling Techniques ◽  
Research Questions ◽  
Biology Research

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

Computational modeling methods for simulating obstructive human lung diseases

2016 ◽  
Author(s):  
Stavros Nousias ◽  
Aris Lalos ◽  
Konstantinos Moustakas ◽  
Antonios Lalas ◽  
Dimitrios Kikidis ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Lung Diseases ◽  
Human Lung ◽  
Modeling Methods

Computational Modeling Methods for 3D Structure Prediction of Ribozymes

Ribozymes ◽  
2021 ◽  
pp. 861-881
Author(s):  
Pritha Ghosh ◽  
Chandran Nithin ◽  
Astha Joshi ◽  
Filip Stefaniak ◽  
Tomasz K. Wirecki ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Structure Prediction ◽  
3D Structure ◽  
Modeling Methods ◽  
3D Structure Prediction

scholarly journals Anatomy of a Psychological Theory: Integrating Construct Validation and Computational Modeling Methods to Advance Theorizing

2021 ◽  
Author(s):  
Ivan Grahek ◽  
Mark Schaller ◽  
Jennifer L Tackett
Keyword(s):  
Computational Modeling ◽  
Construct Validation ◽  
Theory Development ◽  
Psychological Theory ◽  
Modeling Methods ◽  
Psychological Theories ◽  
Development Processes ◽  
Convergence And Divergence

Discussions about replicability of psychological studies have primarily focused on improving research methods and practices, with less attention paid to the role of well-specified theories in facilitating the production of reliable empirical results. The field is currently in need of clearly articulated steps to theory specification and development, particularly regarding frameworks that may generalize across different fields of psychology. Here we focus on two approaches to theory specification and development which are typically associated with distinct research traditions: computational modeling and construct validation. We outline the points of convergence and divergence between them to illuminate the anatomy of a scientific theory in psychology - what a well specified theory should contain and how it should be interrogated and revised through iterative theory development processes. We propose how these two approaches can be used in complementary ways to increase the quality of explanations and the precision of predictions offered by psychological theories.

scholarly journals Challenges of the Modeling Methods for Investigating the Interaction between the CNT and the Surrounding Polymer

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Roham Rafiee ◽  
Timon Rabczuk ◽  
Reza Pourazizi ◽  
Junhua Zhao ◽  
Yancheng Zhang
Keyword(s):  
Carbon Nanotubes ◽  
Electrical Properties ◽  
Computational Modeling ◽  
Polymer Nanocomposite ◽  
Modeling Methods ◽  
Scale Models ◽  
Future Challenges ◽  
Key Factor ◽  
Bonding Properties ◽  
Thermal And Electrical Properties

The interaction between the carbon nanotubes (CNT) and the polymer is a key factor for determining the mechanical, thermal, and electrical properties of the CNT/polymer nanocomposite. However, it is difficult to measure experimentally the interfacial bonding properties between the CNT and the surrounding polymer. Therefore, computational modeling is used to predict the interaction properties. Different scale models, from atomistic to continuum, are critically reviewed addressing the advantages, the disadvantages, and the future challenges. Various methods of improvement for measuring the interaction properties are described. Finally, it is concluded that the semicontinuum modeling may be the best candidate for modeling the interaction between the CNT and the polymer.

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