BIOLOGICAL PHYSICS

Introduction: what is biophysics, top-down and bottom-up approaches.
The living cell: eukariotyc and prokaryotic cells, cell structure and function of its constituents, cell division.
The water: structure and chimical-physical properties, water-protein interaction, optical properties of water, pH and buffering systems, cell incubators.
Membranes and channels: conductance, cell equivalent circuit, Nernst potential, voltage-clamp technique, Hodgkin-Huxley model, neuronal action potential and its simulation, saltatory conduction and Schwann cell, patch-clamp, electrophysiology setup, derivation of cell electrical parameters, single channel current measurement, voltage-activated channel types and blockers, muscle and hair cell synapse, two-state channel model, three- and multi-state models, receptors, activation energies of a channel.
Diffusion: Fick’s laws, diffusion from a point source, random walk and Monte Carlo approach, particle interaction with boundaries, random walk on a grid, numerical simulations of the diffusion process, discretization of the diffusive Laplacian, hydration shells, Kramer equation, electrical mobility, Nernst-Planck equation.
Permeability: partition coefficient, Goldman-Hodgkin-Kats equations, deviation from Ohm’s law, ionic selectivity, single channel permeability, saturation, Eyring’s theory, sodium and potassium channel models.
Chemical reactions: enzymatic reactions, Michaelis-Menten equation, SERCA and PMCA pumps, fluorescent dyes, calcium (Ca2+) dyes, configuration of a fluorescence microscope, relationship between dye fluorescence and Ca2+ concentration, photobleaching, ratiometric dyes, non-equilibrium conditions between Ca2+ and the dye, numerical simulations of Ca2+ dynamics, generation of a reaction-diffusion model and comparison with experiments, Ca2+ dynamics in the inner ear and in cardiac cells, modeling a complex geometry using meshes.
Molecular dynamics: DNA, RNA and proteins, the central dogma of biology, amino acids, folding and protein structures, simulation of protein dynamics, potential energy formula, computational algorithms, boundary conditions and examples of models.
Neural networks: machine learning, learning approaches, artificial neuron and schemes of neural networks, error backpropagation, artificial vision and speech recognition, cerebral organoids, Boltzmann machines.