### Computational Physics and Biology

**Contact Persons:**Alessandra S. Lanotte, Luca Leuzzi, Bruno Rizzuti, Andrea De Martino

**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
activity.**

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: :

**Contact Persons:**Luca Leuzzi, Andrea De Martino

**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.
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**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**

**Contact Persons:**Andrea De Martino, Bruno Rizzuti

**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.

**Contact Persons:**Alessandra S. Lanotte

**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.