Random walks with strongly correlated memory and applications to biology

• Grant number: 18/15308-4
• Support Opportunities: Regular Research Grants
• Start date: December 01, 2018
• End date: November 30, 2022
• Field of knowledge: Physical Sciences and Mathematics - Physics
• Agreement: University of Manchester
• Mobility Program: SPRINT - Projetos de pesquisa - Mobilidade
• Principal Investigator: Marco Antonio Alves da Silva
• Grantee: Marco Antonio Alves da Silva
• Principal researcher abroad: Sergei Fedotov
• Institution abroad: University of Manchester, England
• Host Institution: Faculdade de Ciências Farmacêuticas de Ribeirão Preto (FCFRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Associated research grant: 16/03918-7 - Characterization and memory effects in stochastic processes, AP.R

Abstract

The main purposes of this project are the development of new models of non-linear non-Markovian discrete and continuous time random walks with strong memory dependence in terms of long range time-memory correlations and their application to biological problems. Specifically, we will focus on systems with full and damaged memory, the latter being those in which the recent memory has been erased. We will apply newly developed random walk models to explain observed phenomena of anomalous sub- and super-diffusive transport inside living cells. The question of mapping non-Markovian processes in Markovian processes will be explored both in 1D and 2D. We will tackle the emergence of Lévy distributions from random walk processes with strong memory dependence and explore non-linear cooperative effects and emergence behaviour arising from interactions of random walkers with memory. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

FEDOTOV, SERGEI; HAN, DANIEL; IVANOV, ALEXEY O.; DA SILVA, MARCO A. A.. uperdiffusion in self-reinforcing run-and-tumble model with rest. PHYSICAL REVIEW A, v. 105, n. 1, JAN 27 2022. (18/15308-4)