Using an adaptive gene network model for self-organizing multicellular behavior.
Using the transient interleukin (IL)-2 secretion of effector T helper (T(eff)) cells as an example, we show that self-organizing multicellular behavior can be modeled and predicted by an adaptive gene network model. Incorporating an adaptation algorithm we established previously, we construct a network model that has the parameter values iteratively updated to cope with environmental change governed by diffusion and cell-cell interactions. In contrast to non-adaptive models, we find that the proposed adaptive model for individual T(eff) cells can generate transient IL-2 secretory behavior that is observed experimentally at the population level. The proposed adaptive modeling approach can be a useful tool in the study of self-organizing behavior observed in other contexts in biology, including microbial pathogenesis, antibiotic resistance, embryonic development, tumor formation, etc.
School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA. yshin@cornell.edu
Yong-Jun
YJ
Shin
Ali H
AH
Sayed
Xiling
X
Shen
D000222:Adaptation, Physiological / Q000235:genetics*; D053263:Gene Regulatory Networks; D006801:Humans; D007376:Interleukin-2 / Q000378:metabolism; D008957:Models, Genetic; D053038:Quorum Sensing; D013601:T-Lymphocytes / Q000276:immunology
D007376:Interleukin-2