Fisica e Biologia Computazionale
Keywords: Glassy Systems, Molecular Dynamics, Complex Fluid Dynamics, HPC
At NANOTEC, experimental research in condensed matter, material science, advanced photonics, and
nano-biotechnology is supported by a well-established theoretical and numerical research
This focuses on:
- the theoretical and computational study of systems with many interacting constituents, from glasses and spin glasses to soft and biological matter; and the physics of random lasing materials;
- turbulent and complex flows;
- the quantitative and computational analysis of biological systems, from single-molecules to biological networks and system-level properties.
In addition, we are interested in the development of efficient algorithms for inference and big data analysis, to complement the High Performance Computing activity made of massive computational research on pre-exascale facilities (about 1017 calculations per second), such as the Leonardo supercomputer to be hosted at CINECA in a close future (2020-2022).
Main research lines of the Computational Physics and Biology activity are: :
Keywords: Collective Phenomena, Molecular Networks
People: Luca Leuzzi, Andrea De Martino
Random Lasing Materials. In recent years, random lasing materials (e.g. powders, porous media, precipitates in solution, or photonic crystals with impurities) have been extensively studied experimentally. Pumping energy into these systems causes them to re-emit multi-mode coherent light, with a spectrum displaying randomly arranged peaks in frequency. Starting from the structure and geometry of the atoms and molecules that scatter the light waves, one would like to formulate a theory that predicts the onset, the nature and the features of the light modes, and answering relevant questions, such as: what shape and size do light modes display in space? In which dimension and under which conditions do they localize because of disorder? On which frequencies do light modes emit in cavity-less media? Can there be a random laser pulse in time?
Glassy Random Laser and Experimental Measurement of Replica Symmetry Breaking. The investigation of the glassy behaviour of light in the framework of our theory is made possible by means of a newly introduced overlap parameter, the Intensity Fluctuation Overlap (IFO) measuring the correlation between intensity fluctuations of waves in random media. This order parameter allows to identify the laser transition in arbitrary physical regimes, with varying amount of disorder and non-linearity. In particular, in random media it allows for the identification of the glassy nature of some kind of random laser, in terms of emission spectra data, the only data so far accessible in random laser measurements. A generalised complex spherical spin-glass model solvable in the mean-field approximation by Replica Symmetry Breaking (RSB) theory can be applied. IFO measurements are possible in real experiments, recently leading to a validation of the RSB theory and a characterisation of lasers in terms of spectral intensity fluctuations.
Glassy and slowly relaxing systems. A glass can be viewed as a liquid in which a huge
slowing down of the
diffusive motion of the particles has destroyed its ability to flow on experimental timescales. Starting
from the liquid phase and by supercooling the system, the viscosity of the melt grows several orders of
magnitude in a relatively small temperature interval (i.e. the mobility of the atoms or molecules
decreases by many orders of magnitude). This loss of mobility makes it impossible to reach thermodynamic
equilibrium: the system is said to have fallen out of equilibrium.
We work on the theoretical representation of the behavior of viscous liquids, structural glasses and spin-glasses, on the critical slowing down occurring near-by the dynamic arrest, on the aging dynamics, on the extension of glass theories beyond the limit of validity of mean-field approximation.
Inverse problem in statistical mechanics. Given a data set and a model with some unknown parameters, the inverse problem aims to find the values of the model parameters that best fit the data. We focus on systems of interacting elements, in which the inverse problem concerns the statistical inference of the underling interaction network and of its coupling coefficients from observed data on the dynamics of the system. Versions of this problem are encountered in physics, biology, social sciences and finance, neuroscience (just to cite a few), and are becoming more and more important due to the increase in the amount of data available from these fields. We work on the analysis of the performances of the various inference methods, on their improvement and on their application to new problems. <
Disordered protein states. The ordered structure of proteins is one of the basic paradigms of classical biology, and it provides an explanation for many aspects of their functioning. Nevertheless, in many cases proteins operate in environments far from equilibrium, or possess labile conformations that convert towards order only under particular conditions. Examples include protein folding/unfolding in the presence of temperature and pressure variations, or configuration reorganizations induced by ligand binding in intrinsically disordered proteins. The statistical properties of these ensembles of structures can be studied with sampling techniques based on classical molecular dynamics simulations.
Molecular networks. We are interested in characterizing, via statistical mechanics approaches, the functional properties of large networks of interacting biological molecules like proteins or RNAs. Our goal is to understand what makes these networks optimal for their task, how the laws of physics limit their performance, and whether their physiological role is sustained by collective phenomena similar to those that characterize more traditional systems studied in statistical physics. Our hope is to gain insight into the evolution of the genetic and regulatory networks that govern cellular and multi-cellular activities, including disease (e.g. cancer) and development.
A sketch of a molecular network
Keywords: molecular dynamics, protein folding, Monte Carlo methods, cancer, bacterial growth, single-cell biology
Theoretical methods and mathematical modeling can be applied to investigate complex biological systems. Computer simulations are an especially important tool to analyze problems at different scales, from the interactions of individual biological molecules within cells to the large-scale organization of regulatory networks across different cell types and conditions (e.g. disease vs control). We are interested in developing efficient computational techniques for the study of inference and optimization problems from large, high-resolution biological datasets, most notably for (a) the analysis of gene expression at single cell resolution, (b) the study of kinetic and/or thermodynamical conservation laws in cellular metabolic networks, (c) the analysis of evolutionary variability in protein sequences, (d) the characterization of cell-to-cell variability in microbial populations (at both the physiological and the molecular level), and (e) the inference of complex interaction networks (protein-protein, protein-DNA, RNA-RNA) from genomic data.
Some relevant applications include:
Ligand binding. Interactions between macromolecules and ligands are responsible of molecular recognition, binding, transport and release of molecules that are important from a functional point of view. These mechanisms are fundamental in many processes that are vital for every living organism, and can be exploited in several applications of nanotechnology. We use Molecular dynamics simulations and docking calculations to determine a model of interaction between a variety of ligands and macromolecules belonging to different species. We can predict structural and dynamical properties about the functionality of a molecular complex, for instance between transport proteins or receptors, with small inorganic compounds that include molecules of physiological or pharmacological interest.
Protein folding & Regulatory RNA. Proteins are molecular ‘nanomachines’ that acquire their tridimensional structure through a spontaneous process of folding. Molecular dynamics simulations and other computational methods are able to reveal crucial details of the folding reaction. For intrinsically disordered proteins, these techniques give information on their counterintuitive non-structural properties.We are interested in quantifying the role and effectiveness of microRNA-mediated RNA cross-talk, with the goal of clarifying (i) under which conditions it outperforms other regulatory mechanisms, for instance in processing gene expression noise; (b) whether it can carry a significant systemic role; and (c) its relevance in specific biological cases of differentiation and disease.
Physiology vs gene expression in bacteria. Proteome organization in bacteria is actively regulated in response to growth conditions, as shown by the many quantitative “growth laws” that directly link gene expression parameters to physiological quantities like the growth rate. Several phenomenological models can explain growth laws at a qualitative level. By contrast, genome-scale approaches probing such relationships at the quantitative (molecular) level are far less developed. We aim at improving our understanding of the link between physiology and gene expression in microbes by devising efficient data-based mathematical and computational approaches.
Understanding cell-to-cell variability. Current experimental techniques (like the so-called ‘mother machine’) can probe physiological or gene expression variability in cell populations at single cell resolution. The resulting distributions reflect stochasticity at various levels, from the intracellular noise that characterizes gene expression to fluctuations in the extracellular medium. It is however known that, in many cases, cell-to-cell variability and population-level fitness are tightly linked. We are interested in identifying principles that drive the selection of observed growth states and shed light on the significance of the observed single-cell diversity.
Keywords: Turbulence, Complex flows, HPC
Our research addresses many aspects of turbulence, a ubiquitous phenomenon appearing in very different systems over an extremely wide range of scales, from microns, in quantum systems, to hundreds of kilometres in geophysical and astrophysical contexts.
Turbulence is not a single problem, but rather a huge field of interdisciplinary research with applications to different disciplines: we are interested in fundamental questions, motivated also by applications, linked to the statistical properties of the carrying fluid itself and to the advection/diffusion of particles and fields transported by the flow.
Applications: complex flows in industry and environment, dynamics of colloidal aggregates, evolution and transport of sediments or suspended matter in natural fluids.
In complex fluids, i.e., mixtures in which two or more phases coexists, the challenge is to export the knowledge and methodologies developed for the ideal case of Newtonian turbulence to the case of suspensions, or emulsions. We are interested in: rotating stratified flows, fragmentation dynamics of aggregates in turbulent and laminar flows, transitions from (D) to (D-1) dimensional dynamics.
Switching from classical to quantum word, we study two-dimensional quantum polariton fluids. Loosely speaking, these are superfluids – fluids with zero viscosity- emerging from the strong light-matter interaction in semiconductor microcavities. But unlike helium or other superfluids, that need very low temperature, polaritons can exist at room temperature also. In many applications, a mean field theoretical approach can be used, resulting in an effective non-linear Shroedinger equation for the wave function of the condensate.
DIrect Numerical Simulations of a Turbulent Rotating Flow (4096^3 grid points).
Among the different methodological assets, we cite the numerical methods based on Pseudo-Spectral algorithms complemented by tools for simulating complex particles (such as droplets, aggregates). Numerical tools are supported by a professional knowledge about the optimization of software for massive parallel machines hosted in the European supercomputing centers. We are engaged in the national High-Performace Computing community, with national grant allocations at CINECA, and have benefited of EU allocations within the PRACE framework.