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Many real world systems contain a multiplicity of interactions, however, this complexity is usually difficult to capture using statistical physics. In this talk the "All Particles are Different" (APD) model is introduced, where the energy with which any pair of particles in an ensemble interact is some quenched random variable. We first present results of simulations of APD systems and then discuss some more theoretical aspects using simplified lattice APD models.

Dynamics of biological systems are inherently stochastic from the level of protein fluctuations, to cellular transport, and all the way to sensorimotor responses of whole organisms. In many cases the observed stochastic dynamics exhibit exotic properties such as memory, correlations and non-Gaussian propagators, which cannot be explained simply due to thermal noise, pointing at complex underlying physics. I present an approach seeded in the statistical physics analysis of stochastic trajectories [1], to relate such observed complex characteristics to minimal models of the underlying physics.
I utilise this framework to investigate the high-dimensional subdiffusive dynamics of protein fluctuations, characterising the structure of the rough energy landscape and revealing the coexistence of distinct origins of subdiffusion [2]. The multiple analogies between protein dynamics and glassy systems hint that this approach may also shed light on the latter.
I then use a similar approach to tackle information processing mechanisms in biological systems, where I analyse the response trajectories of cell chemotaxis to known stimuli [3]. I present a minimal model which represents stochastic processing via a memory kernel, and predicts a coupling between the fast membrane polarization (sensing), and the slow cytoskeletal polarization (movement). The model successfully recovers experimental observations including directional memory.

Small molecules are invaluable tools for the investigation of biology. However, discovering new molecules to specifically modulate a target protein is still one of the biggest challenges of chemical biology. Molecules that are able to form a covalent bond with their target often show enhanced selectivity, potency and utility for biological studies, but are yet harder to discover, as they are typically expunged from high throughput screening libraries. Computational methods can help bridge this gap. We developed a covalent docking method for the discovery of covalent probes. Applying this method prospectively to several protein targets we were able to discover potent covalent inhibitors (typically with

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