The interface between physical and life sciences has emerged as a key area in both academia and industry and is fundamentally changing how we investigate living matter. New technologies such DNA-sequencing, super-resolution microscopy and microfluidics are making it possible to probe the inner workings of cells and generate previously unimaginable data. The quantitative information emerging from these new techniques is enabling a new understanding of biological systems, grounded in quantitative models, with profound links to established areas of physics, such as statistical mechanics. In this course we will focus on model building based on physical concepts and laws. We will draw examples from different areas of modern biology including molecular and cell biology, microbiology, systems biology, developmental biology and neuroscience.

Introduction: an overview of different lengthscales and timescales for processes at the level of a biological cell; a focus on the "central dogma" of information processing in cell biology; consideration of important physical quantities at the cellular scale.

Linking with previous knowledge: how do statistical physics, soft matter, classical dynamics, condensed matter and other "modules" of physics help understand aspects of biological systems, particularly at the level of cell biology.

Elements of Networks: intro to basic concepts, random graphs, small motifs, percolation.

Statistical Physics of Living systems: A physical description of living systems; Chemical Forces; Macromolecules as two-state systems; Monod-Wyman-Changeux (MWC) Model of Cooperative binding; Applications of the MWC: Ligand-gated ion channels; Modeling Hemoglobin binding; Diffusion in the cell.

Molecular Motors: Free energy transductions in the cell; Single Molecule Techniques; Molecular Motors as Brownian Ratchets; Smolunchowski Equation; Models of motion; Polymerization ratchets; The Flagellar Rotary Motor in Bacteria; Models of Rotary Motion; Adaptation in Chemotaxis; ATP Synthase.

Neuro-physics: Neuronal Anatomy; Membrane Potential; Nernst Equation; Action Potentials; Cable Equation; Voltage gating Hypothesis; The cell Membrane as a bistable switch; Patch Clamp Experiments; Hodgkin-Huxley Model; Integrate and Fire Models; Opto-genetics.

Biological Pattern Formation: Morphogen Gradients; Reaction-Diffusion Systems; The Turing instability;

Modeling Protein Production with ODE: systems of differential equations to describe unregulated and autorepressed gene expression; dynamics and fluctuations.

Biochemical Noise: Small number stochasticity; Intrinsic and extrinsic noise; modelling noise in protein production; two-step model for protein production.

Regulation of Gene Expression: Statistical Physics description of RNA polymerase binding to promoters; cases of no regulation, activation and repression; behaviour of real promoters, example of the lac operon; the Gillespie approach to numerical modelling of stochastic dynamics.

General elements of dynamical systems: fixed points, phase space, control variables; description of dynamics from an effective potential; nullclines.

Genetic circuits: examples of synthetic and natural circuits exhibiting switching or oscillatory behaviour; application of dynamical systems concepts to model behaviour of genetic circuits; circuits and biological components designed to control gene expression noise.

The course includes guest lectures on biological pattern formation and genetic/proteomic networks and an outlook on the most active research areas of biological physics.

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