Biological, soft-matter & statistical physics group
Biophysics
Proteins: The problem of determining the native ensemble of proteins is of formidable complexity, due to the high number and non trivial correlation of the involved degrees of freedom. Misfolded conformations may also be relevant, since they trigger pathological protein aggregation specifically related to a number of devastating degenerative diseases. In our approach we wish on the one hand to capture unifying emerging aspects in protein physics, typically by means of a statistical approach that employs simplified coarse-grained models. On the other hand, we focus on identifying which protein features can be understood in the context of standard polymer physics. A non exhaustive list of specific topics include the understanding of the origin of protein native folds based on geometry and symmetry, the development of statistical potentials to evaluate the quality of protein-like structures, the development of both sequence- and structure-based algorithms to predict a range of protein features, the investigation of the main mechanisms driving protein folding and aggregation.
Epigenetic and chromatin organization: The structural organization of the genome is emerging as a crucial regulator of the cell state, affecting gene transcription, DNA replication, and repair. Indeed, the control of gene expression relies on an interplay between transcription factors, epigenetic chromatin modifications, and genome spatial organization. We are interested in exploring how the 1D epigenetic changes reverberates on the 3D chromatin organization and hence on its genetic activity. We investigate the problem of chromatin folding vs epigenetic profile using coarse-grained co-polymer models where the chromatin is described as a linear chain of beads “colored” according to the epigenetic class they belong to. Then, by imposing attractive interactions between similar beads (i.e. epigenetic marks) the resulting folded polymer was shown to capture some features of experimental data.
Quorum sensing: Quorum sensing (QS) is a system of stimuli and responses correlated to population density. For instance, quorum sensing allows bacteria to restrict the expression of specific genes to the high cell densities at which the resulting phenotypes will be most beneficial. Examples of such behavior are biofilm formation, virulence and antibiotic resistance. In similar fashion, some social insects use quorum sensing to determine where to nest. Quorum sensing can function as a decision-making process in any decentralized system, as long as individual components have: (a) a means of assessing the number of other components they interact with and (b) a standard response once a threshold number of components is detected. A deep comprehension of the QS mechanism might allow the development of next generation drugs able to overcome the problems arising from antibiotic-resistant bacterial diseases.We are developing mathematical models to predict bacterial expression patterns and in particular we are focusing our work to study the dynamical activation of the process and the trade-off between cell density and population extension from boundaries.
Membraneless organelles In eukaryotic cells, numerous membraneless organelles assembled via liquid-liquid phase separation (LLPS), known as condensates, facilitate compartmentalization of cellular functions. We are interested in studying the emergence of such organelles by means of coarse-grain modelling and in characterizing the boundaries of the phase separation region from a thermodynamic perspective.
Active & Soft Matter
Network forming liquids
Polymer and biopolymer coarse-graining Polymers are macromolecules composed of many repeating units. From a computational perspective, simulating suspensions of large polymers with atomisitic or even monomeric resolution is still largely not achievable, even with modern hardware and state-of-the-art code. Coarse-graining techniques allow to decrease the number of degrees of freedom of the system while significantly maintaining structural and dynamical properties, via the introduction of suitable effective potentials. We are interested in developing coarse-grained models for polymers with complex architecture and in employing them to study the material properties of polymer suspensions.
Polymer dynamics: The translocation of a biopolymer through a membrane nanopore is an example of polymer dynamics with which living cells exchange information and energy. Other examples are the unwinding of the DNA double helix in the denaturation process and the supercoiling dynamics of the double stranded DNAs. Our research focus on developing and studying minimal models that capture the essential, universal, mechanisms underlying these dynamics and that allow a statistical characterization of these processes.
Conformational transitions in polymer systems: Polymers in solution can undergo different relevant conformational transitions depending on the properties of the surrounding medium, such as the solvent chemical composition, the temperature, the presence of geometrical constraints and external forces. One of the most studied conformational transition is the "Theta" collapse of polymers from an extended to a globular phase that, for example, can be triggered by the progressive deterioration of the quality of the solvent. Other examples of conformational transitions are the adsorption transition on attractive substrates and the thermal or mechanical denaturation of the double stranded DNA. Here we analytically and numerically the thermodynamic properties of these transitions, relying on coarse-grained models of polymers, on simulations of stochastic processes and on analytical approaches for the exact calculation of generating functions and partition functions.
Polymer topology: The topological entanglement of polymers and proteins, described in terms of knots and links, is a timely argument of research that span several scientific disciplines, such as mathematics, chemistry, biology and physics. Of particular interest is the understanding of how and to which extent the topological properties of polymers depend on either intrinsic properties (for instance, chain bending and torsional rigidity) and extrinsic factors, such as the quality of the solvent, the degree of confinement and external stresses. Since polymers in solution are flexible fluctuating objects, a natural approach to topological entanglement is statistical mechanics. In the last years this group has produced many results on this field. Recently we have introduced and developed methods to locate and measure the size of topological entanglements within a single polymer and between polymers or polypeptidic chains. These results have opened new, still unexplored, perpsectives on these issues, which we are currently exploring.
Liquid crystals: Liquid crystals (LC) are complex fluids made by anysotropic molecules that, under given conditions, give rise to orientationally ordered phases such as the nematic, cholesteric and blue phases. For these reasons they are structured fluids that respond to external stresses either as elastic or as viscous materials. They also display uncommon anisotropic optical and magnetic responses and for these reasons they are greatly implemented in optical devices. Moreover, models of LC can be properly extended to include non-equilibrium terms that mimics the physics of active fluids and gels such as the solution of actin and cystokeleton filaments in presence of molecular motors. A description of these systems in terms of Landau-de Gennes free energy and lattice Boltzmann equations have been one of the main achievment of this group and in the last years has allowed to obtain several results on the rheological, dynamical and optical properties of these systems. We now plan to extend these investigations to mixtures and emulsions of LC either passive or active.
Heteogeneously charged colloids: Heterogeneously charged colloids have emerged as a coarse-grained model systems for exploring the thermodynamics and the self-assembling properties of charged proteins and capsids, as well as for investigating a number of different systems in Material Science, for which the surface charge distribution is relevant. A plethora of different models have been developed to describe spherical particle with heterogeneous charge distribution: we are focusing on improving the Inverse Patchy Colloids (or IPC) model, going beyond the current model limitations. We are interested in studying the self-assembly of IPCs in bulk and under confinement.
Collective dynamics and patterning in systems of self-propelled particles: Bacterial suspensions, flocks of birds and swarms of insects are examples of self-propelled and interacting N-body systems that, under proper conditions, display collective motion, aggregation and patterning. If one neglects the details of these systems, each individual can be described as a particle that burns internal energy to move in the environment. Hence these systems are intrinsically out-of-equilibrium and their statistical behaviour is different from the one observed in their passive counterparts where equilibrium holds. Our research focuses on the design of simple models of self-propelled particles and the study of their statistical properties such as the aggregation phenomena and dynamical patterning that occurs as a result of spatial confinement, effective long-range interactions and mechanisms of communication between individuals. Our investigational approaches are either numerical or and analytical (Smoluchowski equations) on models in which individuals are described either as anisotropic Brownian particles with internal directional force or as point-like objects with a given position, direction of motion and constant speed. This activity has an experimental counterpart that is carried out in the laboratory of surfaces and interfaces of this Department (LaFSI:http://lafsi.fisica.unipd.it/).
Active polymers: Non-equilibrium phenomena in bio-polymer systems, driven by the action of chemically powered (active) molecular motors, have been observed in many biophysical systems, like actomyosin networks, microtubule arrays, the cytoskeleton and chromatin. We are interested in studying, from a computational perspective, coarse-grained polymer systems doped with active elements, with the goal of understanding the interplay between the polymeric properties (e.g. the filament flexibility, length and topology), the environment (e.g. confinment and crowding) and the activity.
Equilibrium and Non-equilibrium Statistical Mechanics
Diffusion in complex environments
Econophysics and Economic Complexity: The dynamics of markets shows robust stylized facts whose modelization stimulates since long time the use of statistical physics methods. Scaling and techniques inspired by the renormalization group are used as key ingredients for this modelization, for the pricing of derivative products, and for operative definitions of quantities like the time in finance. Another field of interest is that of growth processes realized in a context of global economic complexity. The networks of products and o producing countries play here a fundamental role in descriptions based on stochastic differential equations.
Statistical Physics & Machine Learning: There is a growing interest in problem solving via machine learning techniques. This is leading us to evaluate the new ideas and tools from the community studying machine learning, with the aim of applying these quite new methods in contexts such as complex systems, brain networks or polymer phases. One goal is to understand how machine learning performs in distinguishing polymeric, epigenetic and brain phases. A general plan is to apply machine learning techniques to infer emergent patterns in complex ecological, biological, social, and geophysical systems. Finally we would like to understand if and in which sense neural networks are critical, and how their performance can be understood through information theory and statistical physics.
Nonequilibrium Statistical Mechanics: A general statistical mechanical theory for nonequilibrium systems is under construction. We have in mind systems that maintain fluxes, e.g. of heat, or that are relaxing, or that are not based on the usual physical laws of systems that reach the thermodynamic equilibrium. Often these are small systems (at the micrometer scale), in which fluctuations are relevant. We deal with these systems, for example by generalizing the concept of energy equipartition and the virial theorem, or by developing a theory of linear response. We can thus discuss concepts such as the specific heat or the mobility of particles in systems subject to nonequilibrium forcings. Among the phenomena we study, there are unusual regimes of "negative response", such as particles that slow down if the force that pulls them is increased. Another goal is the identification of the fundamental constraints set by energy dissipation on the efficiency, accuracy and robustness of nonequilibrium processes.
Biochemical Reaction Networks Our aim is to understand the stochastic thermodynamics and response of open networks of biochemical reactions at small scales, such as those used by metabolism and signaling. There, the number of reacting molecules is large but finite, so that nonequilibrium fluctuations intermingle with the complex dynamics (e.g. multistability, limit cycles) that may be present in the thermodynamic limit. A natural questions is how this interplay shapes the statistics of reaction currents, the response of the system to external perturbations, the stability of reaction-diffusion patterns, and the efficiency of energy transduction.
