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Research on nonlinear dynamics and evolution
Research on nonlinear dynamics and evolution
Our research seeks to understand nonlinear phenomena in biological processes across multiple scales, from prebiotic replicators to complex ecosystems and disease dynamics, using the qualitative theory of dynamical systems and approaches from statistical physics and nonlinear science. We investigate the dynamical evolution of nonlinear systems with a focus on bifurcations, aiming to identify useful dynamical scenarios such as ecosystem recovery, biodiversity loss prevention, virus clearance, and tumor extinction. In theoretical ecology, we study functional shifts in species, semiarid ecosystems, and early-warning signals of ecological collapse. In the dynamics and evolution of RNA viruses, we explore quasispecies theory. In cancer dynamics, we examine tumor heterogeneity, phenotypic quasispecies, genomic instability, epigenetic regulation, cancer-free attractors, and dynamics of cancer cells-oncolytic viruses. In brain dynamical complexity, we analyze EEG time series to understand decision-making, physiological responses during mountaineering, brain-computer interfaces, and even in arcade machine experiments.
Our research seeks to understand nonlinear phenomena in biological processes across multiple scales, from prebiotic replicators to complex ecosystems and disease dynamics, using the qualitative theory of dynamical systems and approaches from statistical physics and nonlinear science. We investigate the dynamical evolution of nonlinear systems with a focus on bifurcations, aiming to identify useful dynamical scenarios such as ecosystem recovery, biodiversity loss prevention, virus clearance, and tumor extinction. In theoretical ecology, we study functional shifts in species, semiarid ecosystems, and early-warning signals of ecological collapse. In the dynamics and evolution of RNA viruses, we explore quasispecies theory. In cancer dynamics, we examine tumor heterogeneity, phenotypic quasispecies, genomic instability, epigenetic regulation, cancer-free attractors, and dynamics of cancer cells-oncolytic viruses. In brain dynamical complexity, we analyze EEG time series to understand decision-making, physiological responses during mountaineering, brain-computer interfaces, and even in arcade machine experiments.
Systems Biology and nonlinearity
Systems Biology and nonlinearity
Theoretical ecology: Functional shifts in species; semiarid ecosystems; dynamics of cooperation and facilitation; tipping points (bifurcations); biodiversity loss; warning signals.
Dynamics and evolution of RNA viruses: Quasispecies theory; dynamics of subviral agents; defective viral genomes; virus evolution; multiscale virus-population models.
Cancer dynamics and evolution: Cancer heterogeneity and phenotypic quasispecies; Cancer-free attractors; Tumour evolution and genomic instability; Modelling targeted cancer therapies; Tumour immunogenicity; Epigenetic regulation.
Brain dynamical complexity: Brain dynamics; Brain-computer interface (BCI); arcade machines; decision-making; EEGs, mountaineering; physiological responses; nonlinearity; attractors; time series analysis; entropy; multifractal analysis.
Image below: multiscroll attractor visualised in an oscilloscope
Image below: multiscroll attractor from electronic circuit equations
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