Scientific Interest and Activity
Fabio Cecconi
Statistical mechanics of complex systems
The statistical mechanics of complex systems is a branch of physics that
deals with the study of systems composed of a large number of interacting
components, exhibiting emergent properties and nontrivial behavior.
Complex systems are characterized by nonlinear dynamics and the presence of
multiple scales.
The general protocol of statistical mechanics is a description of the
macroscopic behavior of complex systems
from the properties of the microscopic constituents and their interactions.
In this line, it provides a conceptual framework for connecting the
collective behavior of the system to the microscopic dynamics of its
elementary components.
The statistical mechanics of complex systems finds applications in a wide
range of fields, including condensed matter
physics, materials science, biophysics, computer science, and
socio-economic systems.
It provides insights into the behavior of complex systems, their critical
properties, collective phenomena, autorganization, and phase transitions.
Dynamical system theory and simulations
Dynamical system theory is a branch of mathematics and physics dealing with
the study of system evolutions.
It provides, through modeling, data analysis and simulations, a framework for
understanding the behavior and properties of complex systems,
including biological systems, physical phenomena, and
social and economical behaviors.
An interesting property shown by certain nonlinear dynamical systems is the
instability termed deterministic chaos, such that small perturbations
or disturbances generated
on initial states grow exponentially, thus producing unexpected behaviours in the system's evolution.
Therefore, deterministic chaos (shortly chaos) is synonym of "strong sensitivity to initial conditions"
and is responsible for complex and unpredictable behaviour of dynamical
systems.
- In this context my activity focuses on
understanding and characterizing the role of microscopic chaos in determining
the transport properties (i.e. diffusivity) of dynamical systems Refs. (13;
14; 20; 31; 37).
Since chaos amplifies perturbations and external inputs its is expected to play a crucial
role in defining the transport properties of a system. Actually chaos certainly affects
the values of the transport coefficients, however it is non necessary to transport because
even non chaotic systems chan show well-defined transport properties.
Identification of minimal requirements for the onset of
robust statistical behaviour and transport in dynamical systems.
Theory and simulations of granular systems
The term "granular matter" refers to collections of macroscopic solid
particles such as sand, grains, powders, and other bulk materials.
The behavior of granular materials is of interest in various scientific and engineering fields,
including physics, geology, materials science, and engineering.
Theoretical models and simulations play a crucial role in understanding and predicting the complex behavior of granular matter.
Molecular Dynamics simulations are commonly used in the study of materials at the atomic or molecular scale. However, they can also be extended to granular materials by treating particles as larger units with simplified interaction potentials. MD simulations track the motion of individual particles and solve Newton's equations of motion to study the dynamics of granular assemblies. MD can provide detailed information about particle-level interactions, such as energy transfer, compaction, and jamming phenomena.
Kinetic theory provides a statistical description of granular materials by
treating particles as units with certain probabilities of interaction.
It uses concepts from statistical mechanics to derive equations that describe the evolution of particle distribution functions. Kinetic theory provides insights into phenomena such as particle velocity distribution, clustering, and heat conduction in granular materials.
Biological systems:
Studying the physics of biological systems is crucial for understanding how
life organizes, adapts and responds to the constraints imposed by the
environment. In this context my activity includes: protein folding,
normal mode analysis of biomolecules, biopolymer transport across
nanochannels.
- Protein folding
Protein folding is the process by which a newly synthesized or denatured protein adopts its three-dimensional structure, known as its native conformation. The native conformation is critical for the protein to carry out its specific biological functions effectively.
Proteins are linear chains of amino acids, and their folding is guided by
the sequence of amino acids and the interactions between them.
The primary structure of a protein, which is the specific sequence of amino
acids, determines its folding pathway and final structure.
- Normal mode analysis of biomolecules
Normal mode analysis is a computational technique used to study the collective motions and vibrations of biomolecules, such as proteins and nucleic acids. It provides insights into the dynamics and flexibility of these molecules, which are crucial for understanding their functions and interactions.
In normal mode analysis, the biomolecule is approximated as a set of interconnected atoms or beads, and their motions are described as harmonic vibrations around their equilibrium positions. The analysis focuses on the low-frequency vibrational modes, known as normal modes, which represent the most significant collective motions of the molecule.
The normal modes can be analyzed and visualized to gain insights into the collective motions of the molecule. Common analyses include examining the displacements of atoms in each mode, investigating the contribution of different residues or domains to specific modes, and identifying key functional motions.
- Biopolymer transport across nanochannels
Nanopore-based devices employ nanoscale pores to detect and analyze proteins at the single-molecule level. When a biomolecule crosses a nanopore, generates
characteristic electrical signals (resistive pulses) that can be recorded
and decoded to determine the molecular properties.
Nanofluidics, on the other hand, focuses on manipulating and controlling the flow of fluids at the nanoscale, enabling precise sample delivery and interaction with nanopores.
- Active Matter
Active particles, also known as active matter, refer to a class of systems composed of individual or particles that can convert stored or ambient energy into directed motion or work. These particles are capable of autonomous movement, unlike traditional passive particles that only undergo random Brownian motion.
Active particles can be found in various biological and non-biological systems, including living organisms, synthetic nanomachines, and self-propelled colloidal particles. Examples of active particles in nature include bacteria, swimming microorganisms, and cells that exhibit motility through the contraction of their cytoskeleton.
The behavior of active particles is often governed by local interactions and the energy conversion processes that enable their self-propulsion. These interactions can lead to collective behavior and emergent phenomena, where the collective dynamics of active particles are different from the individual behaviors.
M.Cencini, F.Cecconi, A.Vulpiani:
Chaos: form simple models to complex systems: 17
A.Vulpiani, F.Cecconi, M.Cencini, A.Puglisi,
D.Vergni: Large Deviations in Physics
(The Legacy of the Law of Large Numbers)