The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Abstraction Methods for Analysis of Gene Regulatory Networks
Abstract
With advances in high throughput methods of data collection for gene regulatory networks, we are now in a position to face the challenge of elucidating how these genes coupled with environmental stimuli orchestrate the regulation of cell-level behaviors. Understanding the behavior of such complex systems is likely impossible to achieve with wet-lab experiments alone due to the amount and complexity of the data being collected. Therefore, it is essential to integrate the experimental work with efficient and accurate computational methods for analysis. Unfortunately, such analysis is complicated not only by the sheer size of the models of interest but also by the fact that gene regulatory networks often involve small molecular counts making discrete and stochastic analysis necessary. To address this problem, this chapter presents a model abstraction methodology which systematically performs various model abstractions to reduce the complexity of computational biochemical models resulting in substantial improvements in analysis time with limited loss in accuracy.
Related Content
|
S. Karthigai Selvi, Sharmistha Dey, Siva Shankar Ramasamy, Krishan Veer Singh.
© 2025.
16 pages.
|
|
S. Sheeba Rani, M. Mohammed Yassen, Srivignesh Sadhasivam, Sharath Kumar Jaganathan.
© 2025.
22 pages.
|
|
U. Vignesh, K. Gokul Ram, Abdulkareem Sh. Mahdi Al-Obaidi.
© 2025.
22 pages.
|
|
Monica Bhutani, Monica Gupta, Ayushi Jain, Nishant Rajoriya, Gitika Singh.
© 2025.
24 pages.
|
|
U. Vignesh, Arpan Singh Parihar.
© 2025.
34 pages.
|
|
Sharmistha Dey, Krishan Veer Singh.
© 2025.
20 pages.
|
|
Kalpana Devi.
© 2025.
26 pages.
|
|
|