Characterizing chaos and multifractality in noise-assisted tumor-immune interplay

We propose a noise-assisted tumor-immune system based on the Wiener process. Stochastic sensitivity and chaos both are studied with the variations of noise strengths. The sensitivity analysis is done by confidence ellipsoid fit technique. It indicates that the sensitivity is enhanced with the increase of noise strengths. On finding chaos, the existence of multi-periodicity is investigated by counting periods under the variation of the same noise strengths. Further, noise-induced chaos is quantified by measuring the asymptotic growth of the phase space trajectories. With the same analysis, chaotic and non-chaotic regimes are classified. Moreover, deterministic and its noise-induced chaotic dynamics are compared using wavelet leader-based multifractal analysis. Numerical results assure that the noise-assisted tumor-immune system illustrates the rich as well as fine structure compared to its non-noisy dynamics.

Duke Scholars

Author Sayan Mukherjee Statistical Science