Computational modeling of the immune system requires practical and efficient data analytical approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. Computational systems biology approaches can be used to represent and study molecular mechanisms of cell differentiation. However, such systematic modeling efforts require the building of complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. It also requires the integration of complex processes that occur at different scales of spatiotemporal magnitude. Application of supervised learning methods, such as artificial neural network (ANN), can reduce the complexity of ordinary differential equation (ODE)-based models of intracellular networks by focusing on the input and output cytokines. In addition, this modeling framework can be efficiently integrated into multiscale tissue-level models of the immune system.
|Original language||English (US)|
|Title of host publication||Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology|
|Subtitle of host publication||Algorithms and Software Tools|
|Number of pages||18|
|State||Published - Aug 7 2015|
All Science Journal Classification (ASJC) codes
- Computer Science(all)