ABCDEFGHIJKLMNOPQRSTUVWXYZ
1
talk length00:30
2
DayHourNameemailAffiliationTitleAbstract
3
11/09/202415:00Raffaele Marinoraffaele.marino@unifi.itUniversità di FirenzeLearning in Wilson-Cowan model for metapopulationThe Wilson-Cowan model for metapopulation, also known as Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity between these regions. Each region comprises interacting populations of excitatory and inhibitory cells, consistent with the standard Wilson-Cowan model.
By incorporating stable attractors into such a metapopulation model’s dynamics, we transform it into a learning algorithm capable of achieving high image and text classification accuracy. We test it on MNIST and Fashion MNIST, in combination with convolutional neural networks also on CIFAR-10 and TF-FLAWERS, and in combination with a transformer architecture (BERT) on IMDB, always showing high classification accuracy. This numerical evaluation illustrates that minimal modifications to the Wilson-Cowan model for metapopulation can reveal unique and previously unobserved dynamics, helping to describe brain complex behaviors.
4
11/09/202415:30Sebastiano Stramaglia
sebastiano.stramaglia@ba.infn.it
uniba & infnDisentangling high order effects in the transfer entropyTransfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias - either in deficiency or excess - during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. Here, we propose a novel approach. Instead of conditioning TE on all network processes except the driver and target, as in its fully conditioned version, or not conditioning at all, as in the pairwise approach, our method searches for both the multiplets of variables that maximize information flow and those that minimize it. This provides a decomposition of TE into unique, redundant, and synergistic atoms. Our approach enables the quantification of the relative importance of high-order effects compared to pure two-body effects in information transfer between two processes, while also highlighting the processes that contribute to building these high-order effects alongside the driver. We demonstrate the application of our approach in climatology by analyzing data from El Niño and the Southern Oscillation.
5
11/09/202416:00Vera Pecorinopecov1800@gmail.comDFAGranger dependencies in the precipitation network across Sicily islandRainfall constitutes an important climatic variable as its lack
can lead to severe droughts, while its excess can trigger catastrophic
events. Recently Sicily island, for its location in the middle of different
climatic systems, displayed unusual rainfall behavior and unexpected
extreme precipitation events during the last decades. In this paper, we
analyse the Granger dependencies in the precipitation network across
Sicily island from 2002 to 2023. The aim is to study, at different spatial
resolution, the causal relation between different areas of the island across
22 years. We generate weighted and directed networks (1 per year) for
each season and investigate the F-statistics distribution. Then we explore
of the westward and eastward directed connections above the 75th per-
centile across years, focusing on interior-coastline (medium range) and
coast-to-coast (long range) relations. We observe that after year 2012,
Sicily experience a higher precipitation seasonal variability on the coast-
line, in parallel to a decreasing connection of rainfall behavior between
coast and interior. In addition, we note a growth of the strength of per-
turbations, especially in the westward direction. These results represent
a description of Sicily rainfall complementary to the ones performed us-
ing monotonic statistical trends or clustering algorithms, since here the
complex nature of pairwise interaction is taken into account. This study
is a further contribution to the wide literature, which testifies the ongo-
ing deep climate change and that new strategies for managing resources
and damages are urgently needed.
6
11/09/202416:30Giovanni La Pennagiovanni.lapenna@cnr.itCNR e INFNModelling membrane plasticityThere is mounting evidence for different functions of divalent cations, like Mg, Ca, Zn, Cu, Fe, in the modulation of signal transmission at synaptic level. This effect arises primarily from changes of fluidity [1], but since the synaptic space is tiny, membrane fusion and vesiculation are affected. We proposed a possible role of amyloid peptides in the regulation of such effects [2,3]: these peptides and divalent cations are all present at particularly high concentration in the synaptic cleft.
Developing models that capture the combination of electrostatics and coordination chemistry of multivalent cations moving around lipid membranes, the latter with the particular composition of synapse, receptors and signaling molecules is, in our opinion, mandatory to understand synapse and its aging.
In this contribution I shall present our ideas and experience on this subject.
References
1) X. Jiang et al., Anomalous behavior of membrane fluidity caused by copper-copper bond coupled phospholipids, Sci. Rep. 2018, 8(1), 14093.
2) P.D.Q. Huy et al., Computational model to unravel the function of amyloid-beta peptides in contact with a phospholipid membrane, J. Phys. Chem. B, 2020, 124(16), 3300.
3) P. Krupa et al., Amyloid-beta tetramers and divalent cations at the membrane/water interface: Simple models support a functional role, Int. J. Mol. Sci., 2023, 24(16), 12698.
7
8
12/09/20249.30Simona Olmisimona.olmi@fi.isc.cnr.itIstituto dei Sistemi Complessi - CNRPopulation spiking and bursting in next-generation neural masses with spike-frequency adaptationSpike-frequency adaptation (SFA) is a fundamental neuronal mechanism taking into account the fatigue due to spike emissions and the consequent reduction of the firing activity. We have studied the effect of this adaptation mechanism on the macroscopic dynamics of excitatory and inhibitory networks of quadratic integrate-and-fire (QIF) neurons coupled via exponentially decaying post-synaptic potentials. In particular, we have studied the
population activities by employing an exact mean-field reduction, which gives rise to next-generation neural mass models. This low-dimensional reduction allows for the derivation of bifurcation diagrams and the identification of the possible macroscopic regimes emerging both in a single and in two identically coupled neural masses. In single populations SFA favors the emergence of population bursts in excitatory networks, while it hinders tonic
population spiking for inhibitory ones. The symmetric coupling of two neural masses, in absence of adaptation, leads to the emergence of macroscopic solutions with broken symmetry, namely, chimera-like solutions in the inhibitory case and antiphase population spikes in the excitatory one. The addition of SFA leads to new collective dynamical regimes exhibiting cross-frequency coupling (CFC) among the fast synaptic timescale and the slow
adaptation one, ranging from antiphase slow-fast nested oscillations to symmetric and asymmetric bursting phenomena. The analysis of these CFC rhythms in the θ-γ range has revealed that a reduction of SFA leads to an increase of the θ frequency joined to a decrease of the γ one. This is analogous to what has been reported experimentally for the hippocampus and the olfactory cortex of rodents under cholinergic modulation, which is
known to reduce SFA
9
12/09/202410.00Valentina Buonfigliovalentina.buonfiglio@unifi.itUniversità di FirenzeA Small Ensemble of Myosin Motors at Work: Fitting Experimental Data with a StochasticMyosin II is the molecular motor of the striated (skeletal and cardiac) muscle that converts the chemical energy into steady force and shortening by cyclic ATP-driven interactions with the actin filaments in the sarcomere. Different isoforms of the myosin motor account for distinct functional requirements of the slow muscles (primarily responsible for the posture) and fast muscles (responsible for voluntary movements). Single molecule experiments cannot be invoked to shed light on the peculiar microscopic traits from which the observed macroscopic differences eventually originate. These are in fact presumably stemming from the collective dynamics of the examined array of motors. To bridge this gap, we elaborated on a self- consistent procedure to recover the relevant parameters underpinning the force exerted by an ensemble made of slow and fast skeletal muscles. The force is acquired experimentally by measuring the isometric performance of a unidimensional sarcomere-like synthetic nanomachine, powered by myosin isoforms purified from either slow or fast rabbit skeletal muscle. In this talk I will describe the stochastic model to be fitted against available experimental time series. The proposed procedure yields estimates of all the mechano-kinetic properties of the motor ensemble, including the motor force, the fraction of actin- attached motors and the rate of transition through the attachment-detachment cycle. The procedure adopted set the stage for future studies on the emergent mechanokinetic properties of an ensemble of myosin molecules either engineered or purified from mutant animal models or human biopsies.
10
12/09/202410:30coffee break
11
12/09/202411:00Lorenzo Di Mecolorenzo.dimeco3@unibo.itUniversity of Bologna
Congestion transition on random walks on graphs
The formation of congestion on an urban road network is a key issue for the development of sustainable mobility in future smart cities.
We propose a reductionist approach by studying the stationary states of a simple transport model using a random process on a graph, where each node represents a location and the link weights give the transition rates to move from one node to another, representing the mobility demand.
Each node has a maximum flow rate and a maximum load capacity, and we assume that the average incoming flow equals the outgoing flow.
In the approximation of the single-step process, where at each time step only one node can move an agent, we are able to analytically characterize the traffic load distribution on the single nodes using a local maximum entropy principle.
Our results explain how congested nodes emerge as the total traffic load increases, analogously to a percolation transition where the appearance of a congested node is an independent random event.
However, using numerical simulations, we show that in the more realistic case of synchronous dynamics for the nodes, where at each time step every non-empty node can move an agent, entropic forces introduce correlations among the node states and favor the clustering of empty and congested nodes.
Our aim is to highlight the universal properties of congestion formation and, in particular, to understand the role of traffic load fluctuations as a possible precursor of congestion in a transport network.
A major challenge in this context is the identification of ”universal patterns” which do not depend on the particular individual but are common to all tested brains. Despite the huge amount of gene expression data both at the bulk and single-cell levels which
are now available, this remains a difficult task, mainly due to the lack of suitable data mining tools.
In this paper, we propose a new approach to address this issue based on a hierarchical version of Stochastic Block Modeling which, thanks to the particular choice of priors, is particularly effective in identifying these universal features. We use as a laboratory to test our algorithm a dataset obtained from six independent human brains from the Allen Human Brain Atlas. We show that our algorithm is indeed able to identify universal patterns much better than other more standard algorithms, like LDA or WGCNA, and that the (probabilistic) association between genes and samples that we find well represents the known
anatomical and functional brain organization. Leveraging the peculiar ”fuzzy” structure of the gene sets that we obtain with our method, we also identify a few transcriptional and post-transcriptional pathways which are precisely associated with specific brain regions, a result which highlights the potential of our approach.
12
12/09/202411.30Giulio Colombinigiulio.colombini2@unibo.itUniversity of Bologna ItaltA simple model for delay stabilisation in nonlinear dissipative systems
In the study of synchronization phenomena and non-equilibrium steady states in directed networks of nonlinear systems, Delay Differential Equations (DDEs) serve as an effective model for analyzing collective self-consistent states. Building on previous work involving directed loops of nonlinear neurons, where a traveling wave state is shown to correspond to a limit cycle emerging from a cycle saddle node bifurcation in an effective DDE, we propose a simplified, normal form-like model to better characterize this transition. In the limit of low base dissipation, this model can be analytically approximated, revealing that the bifurcation is actually a delay-induced saddle node bifurcation of limit cycles that does not destabilize the origin—similar to behavior observed in the FitzHugh-Nagumo neuron. Furthermore, a perturbative approach suggests that, despite the infinite dimensionality of the delayed system, the bifurcation can be understood at its onset as a fundamentally planar phenomenon. This insight indicates that such bifurcations could be a useful framework for studying systems that spontaneously reduce the dimensionality of their dynamics.
13
12/09/202412.00lunchModel
14
12/09/202414.30Michele Casellecaselle@to.infn.itPhysics Dep. Torino UniversityGene duplication as a driver of complex regulatory motifs.The goal of this talk is to present a brief overview of the activity of
the Torino group. In particular, I will mainly focus on a few works
concerning the role of gene duplication in tuning the gene regulatory
network in higher eukaryotes. At the end, if time permits, I will also briefly
discuss a few other projects that we are pursuing on the implications at
the transcriptional level of these regulatory patterns.
15
12/09/202415.00Francesca Vercellone
francesca.vercellone@na.infn.it
Università degli Studi di Napoli Federico II & INFNModeling chromatin 3D organization from sparse contact data using a polymer-physics based approachThe rapid advancement of comprehensive genome mapping techniques, such as Hi-C, for investigating the three-dimensional configuration of the genome within the nucleus has uncovered complex chromatin architectures at multiple scales, including A/B compartments, topologically associating domains (TADs), and chromatin loops. These structural elements of the 3D genome are linked to crucial genomic functions, such as gene transcription, although the variability of 3D genome structures and their functional implications at the single-cell level remain largely elusive. Emerging single-cell Hi-C (scHi-C) technologies now facilitate the genomic mapping of 3D chromatin configurations in individual cells, offering the potential to elucidate fundamental connections between genome structure and function at single-cell resolution across diverse biological contexts. Nevertheless, there is a significant deficiency in computational methodologies capable of physically characterizing the sparse scHi-C data. Here, we employ a polymer-physics based approach, which relies on phase-separation mechanisms, combined with machine learning, to impute contact maps from the sparse scHi-C data and to analyze the cell-to-cell variability of three-dimensional (3D) chromatin organization through their polymer models.
16
12/09/202415.30Filippo Vallefilippo.valle@unito.itUniversity of Turin and INFNExploring the latent space of transcriptomic data with topic modelingThe availability of high-dimensional transcriptomic datasets is increasing at a tremendous pace, together with the need
for suitable computational tools. Clustering and dimensionality reduction methods are popular go-to methods to identify
basic structures in these datasets. At the same time, different topic modeling techniques have been developed to organize
the deluge of available data of natural language using their latent topical structure.
This paper leverages the statistical analogies between text and transcriptomic datasets to compare different topic modeling
methods when applied to gene expression data. Specifically, we test their accuracy in the specific task of discovering and
reconstructing the tissue structure of the human transcriptome and distinguishing healthy from cancerous tissues. We
examine the properties of the latent space recovered by different methods, highlight their differences, and the pros and
cons of the methods across different tasks. Finally, we show that the latent topic space can be a useful embedding space,
where a basic neural network classifier can annotate transcriptomic profiles with high accuracy
17
12/09/202416.00Alessio Bartoccialessio.bartocci@unitn.itUniversity of Trento, Department of Physics; INFN-TIFPA, Trento Institute for Fundamental Physics and ApplicationsSUPRAMOLECULAR PROTEIN-LIGAND SYSTEMS: INSIGHTS FROM MOLECULAR DYNAMICS SIMULATIONSBindings of small ligands to proteins are ruled by interactions between the ligand and residues in the protein's binding site, which can be located in a small binding pocket or on the protein surface.
Such interactions can directly modulate protein functions and conformations, with possible
implications ranging from biophysical processes, such as protein co-crystallization [1-3] , to the understanding of molecular mechanisms behind drug binding [4] (selective drug design).
Computational ways to investigate such systems are molecular dynamics simulations (MDs) which, spanning from atomistic to coarse-grained resolution, can give insights into the mechanisms, kinetics, and thermodynamics underpinning the protein-ligand complex formation. Here, it will be discussed how supramolecular complexes, formed by proteins interacting with small molecules, can be described by MDs approaches, proposing characteristic fingerprints and key patterns for the
interactions.

[1] McGovern, R.E., Fernandes, H., Khan, A.R., Power, N.P., & Crowley, P.B. Nat. Chem. 4, 527-533 (2012)
[2] Bartocci, A., Pereira, G.P., Cecchini, M., & Dumont, E. JCIM 62, 6739-6748 (2022)
[3] Bartocci, A., & Dumont, E. J. Chem. Phys. 160, 105101 (2024)
[4] Heinzelmann, G., Henriksen, N.M., & Gilson, M.K. JCTC 13, 3260-3275 (2017)
18
12/09/202420:00Social dinner
19
20
13/09/20249.30Clemens Franz Vorsmann
clemensfranz.vorsmann@studenti.unipd.it Switch accounts
Universita di PadovaAdsorption of Colloidal Particles by Polymer BrushesNanoporous membranes coated with polymer chains are increasingly popular for the usage in water filtration devices, among other reasons, due to their non-fouling properties that extend the devices' lifespan. Employing a simplified model of a polymer brush, we first investigated the conformational changes of a planar brush due to colloidal adsorption by a mixed approach of scaling arguments and coarse grained molecular dynamics simulations. We show that the adsorption of the colloidal particle effectively changes the size of the polymeric brush by a direct mapping to a good solvent chain with a different “effective” degree of polymerisation $M$. We find a master curve which is in sound agreement with the curve of a non-adsorbing brush even for various interaction strengths between colloid and chain.

In our more recent work we introduced a slightly different model with anisotropic attractive spots attached to the monomeric units. To compare the anisotropic and isotropic system for equal amounts of enthalpic gain of adsorption, we calculated the second Virial coefficients as is commonly done for patchy particles, recasting them in the form of the Baxter temperature. We find a less pronounced transition between the collapsed and non-collapsed brushes, since the amount of cooperating neighbours is reduced.
21
13/09/202410.00Alessandro Pluchino
alessandro.pluchino@ct.infn.it
Dipartimento di Fisica e Astronomia, Università di Catania, e Sezione INFN di Catania, LINCOLNPairwise and high‐order dependencies in the cryptocurrency trading networkIn this work we analyse the effects of information flows in cryptocurrency markets. We first define a cryptocurrency trading network, i.e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as links, later we analyse its evolution over time. In particular, with reference to years 2020 and 2021, we study the logarithmic US dollar price returns of the cryptocurrency trading network using both pairwise and high‐order statistical dependencies, quantified by Granger causality and O‐information, respectively. With reference to the former, we find that it shows peaks in correspondence of important events, like e.g., Covid‐19 pandemic turbulence or occasional sudden prices rise. The corresponding network structure is rather stable, across weekly time windows in the period considered and the coins are the most influential nodes in the network. In the pairwise description of the network, stable coins seem to play a marginal role whereas, turning high‐order dependencies, they appear in the highest number of synergistic information circuits, thus proving that they play a major role for high order effects. With reference to redundancy and synergy with the time evolution of the total transactions in US dollars, we find that their large volume in the first semester of 2021 seems to have triggered a transition in the cryptocurrency network toward a more complex dynamical landscape. Our results show that pairwise and high‐order descriptions of complex financial systems provide complementary information for cryptocurrency analysis and could be fruitfully extended to other contexts.
22
13/09/202410.30Marin Vatinvatinmarin@gmail.com
23
13/09/202411.00Tommaso Matteuzzitommaso.matteuzzi@unifi.itUniversità di Firenze
24
12/09/202411.30Giuliano Migliorinigiuliano.migliorini@unifi.itUniversità degli studi di Firenze; Université d'Orléans; Centre de Biophysique Moléculaire (CNRS)Diffusion and enzymatic reactions in crowded environmentsThe extracellular matrix (ECM) is the non-cellular component that builds up all tissues and organs. It is made of water and macromolecules, such as elastin [1]. Fundamental to an organism's health, ECM is continuously remodelled, mainly through the action of enzymes. So densely populated by macromolecules, ECM is an example of crowded environment, a complex system whose properties are hardly reproducible in vitro. Shedding light on how crowding affects enzymatic activity in the ECM is the objective of X-CROWD, a 4-year, ANR-funded project the authors of this work are part of. X-CROWD focuses on investigating the kinetics of key ECM enzymes under controlled substrate concentrations and crowding conditions [2]. By performing enzyme kinetics and diffusion experiments, the goal is to understand (i) how crowding affects the system dynamics, and (ii) what are the main factors responsible for it, such as excluded-volume effects and substrate-environment interactions.

Among the experiments performed by the X-CROWD project in CBM, fluorescence intensity kinetic assays play a major role. Focusing on two enzymes, Human Neutrophil Elastase (HNE) and MMP1 Collagenase, kinetics involving substrates and crowders are followed in time, with two main goals: (i) to apply existing reaction models [3][4] or develop new ones, and (ii) to assess the role of crowding. Typically, these experiments involve several chemical species whose contribution to absorbance cannot be neglected and are often led under conditions that are not even close to the dilution regime where the relationship between fluorescence and concentrations is linear. To experimentally control the physical mechanisms involved in the measurement at high concentrations in solution, we applied the radiative transfer equation [5] to the front-face geometry typical of fluorometric assays. A law has been derived that, in a compact and accessible form, sheds light on the relation between measured fluorescence intensity, the concentrations and the optical properties of chemical species in solution, accounting for inner filter effects and quenching mechanisms altogether, and which might be helpful beyond the aim of the project itself to deal with these, usually undesired, experimental effects.

The relevance of this approach for enzymatic assays at high concentrations of substrates and crowders has been confirmed through its application to the kinetic experiments conducted in CBM. The results of these experiments and their interpretation in terms of kinetic modelling and crowding effects will be discussed, together with some of the other research lines in which I have been involved as part of the X-CROWD project.

References:

[1] C. Frantz, K. M. Stewart, V. M. Weaver . The extracellular matrix at a glance. J. Cell. Sci. 2010, 123, 4195-4200.
[2] R. Nehmé, R. Nasreddine, L. Orlic, C. Lopin-Bon, J. Hamacek, F. Piazza. Kinetic theory of hyaluronan cleavage by bovine testicular hyaluronidase in standard and crowded environments. Biochim Biophys Acta. 2021, 1865, 3.
[3] L.Hedstrom. Serine Protease Mechanism and Specificity. Chem. Rev. 2002, 102, 4501−4523.
[4] R. L. Stein. Catalysis by Human Leukocyte Elastase. 4.1 Role of Secondary-Subsite Interactions. J. Am. Chem. Soc. 1985, 107, 5767-5775.
[5] F. Martelli, S. D. Bianco, A. Ismaelli, and G. Zaccanti, “Light propagation through biological tissue and other diffusive media: Theory,solutions, and software,” 2009.
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100