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The Hippocampus: from Memory into Space and Back40m
I will contrast the spatial and memory narratives that have
dominated these last few decades of hippocampal research, leading to the
somersault caused by the discovery of grid cells. Besides yielding a Nobel
prize for Edvard and May-Brit Moser, grid cells have paradoxically refocused
attention on the dentate gyrus, as one of two key innovations introduced
in the mammalian nervous system some 250 million years ago.
Correlations in brain activity20m
Prof.Lucilla de Arcangelis
(Seconda Università di Napoli)
Chaos and correlated avalanches in excitatory neural networks with synaptic plasticity20m
Irregular firing patterns characterized by power-law distribtion in avalanches size are observed to be fundamental dynamical features of neural networks. Here we show how such dynamical regimes
naturally emerge in a disordered mean field model of purely excitatory leaky integrate-and-fire neurons with dynamical synapses modeling short-term plasticity. We compute the phase diagram of
the model as a function of the coupling strength and of the synaptic time-scales and we show that it exhibits two transitions, from quasi-synchronous and asynchronous regimes to a nontrivial bursty
collective activity. In the homogeneous case without disorder, the bursty behavior is reflected into a doubling-period transition to chaos for a two dimensional discrete map. Numerical simulations
confirm that the introduction of disorder preserves the main chaotic features of the dynamics and it gives rise, through a dynamical mechanism, to power-law scaling of activity events.
Critical phenomena at a first-order phase transition in a lattice of glow lamps: Experimental findings and analogy to neural activity20m
Recent work has shown that networks of non-linear electronic oscillators can recapitulate diverse aspects of neural dynamics, such as formation of complex synchronization patterns, offering opportunities to draw parallels between emergence in biological and engineered systems. However, two pervasive properties found across brains and in-vitro neuronal cultures have not been extensively addressed in circuit models: criticality, intended as operation at the boundary between ordered and disordered dynamical phases, and metastability, that is, the ability to maintain and switch between states having finite lifetime. Here, we investigated a network of glow lamps, a common type of neon-argon discharge tube, coupled in a two-dimensional square lattice by capacitors. We find that the system can be metastable with respect to the transition between two dynamical phases having different degrees of spatiotemporal order. Close to the spinodal points, which denote the limits of existence of the metastable states, fractal temporal structure emerges and activity avalanches are generated, whose size and duration follow power-law distributions having exponents ≈3/2 and ≈2. Despite differences in system scale, topology, and nature, these critical exponents overlap neural recordings; hence, this setup deserves consideration as building block to realize corresponding physical electronic models. The circuit is also of interest as a physical system wherein critical phenomena are observed close to the spinodal in a first-order phase transition.
Lucilla De Arcangelis
The Brain Hierarchical Atlas40m
Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional
magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects.
Our novel approach allows us to find a common
skeleton shared by structure and function from which a new Brain Hierarchical Atlas can be described. The use of this atlas in different pathologies underlines the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.
Prof.Jesus M Cortes
(Biocruces Bilbao Spain)
Entropic effects in chromatin folding20m
How DNA is organized in three dimensions inside the cell nucleus and how this organization affects the ways in which cells, access, read and interpret genetic information are among the most challenging questions in cell biology. Thanks to the recent development in molecular, genomic and computational approaches based on chromosome conformation capture technology (such as 3C, 4C, 5C and Hi-C) we are able to tackle a question regarding whether or not entropic forces are driving the relative positioning of chromatin fibers (represented as self-avoiding polymers) in the cell nucleus.
The idea that nonspecific forces (like “entropic centrifuge”) can be a major driver of chromatin organization has been well formalized but a direct verification from real data is still missing.
Since a high GC content in the sequence is known to correlate with increased flexibility and decompaction of the chromatin fiber we suggest that integrating the information about contact probability and GC content will enable us to gain a better insight on the role of entropic forces in the self organization of chromosomes within nuclei.
Partition of a graph using the Fiedler's theory: some properties20m
According to the Fiedler's theory, a bipartition of a graph can be obtained by means of the second eigenvector of its Laplacian matrix. A possible refinement of this method holds true by considering a weighted Laplacian matrix. However in order to have a robust partition of a given graph, it is necessary to consider the effect of a perturbation on a weighted Laplacian matrix. In particular, we show that different partitions of a graph are obtained by specifying the size of its parts or by requiring which nodes are in a section of the partition rather then into another one.
Furthermore, by requiring the coalescence between the second and the third eigenvalues of the perturbed Laplacian, a suitable robustness measure of the Fiedler partition can be defined and we study the network partition determined by the two eigenvectors near the eigenvalue collapse.
Some applications of this method to small graphs are investigated.
Further applications of the method so far put forward can be addressed to biological systems.
(University of L'Aquila)
Cellular differentiation occurs during the development of multicellular organisms and leads to the formation of many different tissues where gene expression is modulated without modification of the genetic information. These modulations are in part encoded by chromatin-associated proteins or biochemical tags that are set down at the chromatin level directly on DNA or on histone tails, the so-called epigenome. These markers are directly or indirectly involved in the local organization and structure of the chromatin fiber, and therefore may modulate the accessibility of DNA to transcription factors or enzymatic complexes, playing a fundamental role in the transcriptional regulation of gene expression. Statistical analysis of the repartition of this epigenomic information along the chromosomes have shown that genomes of higher eukaryotes are linearly partitioned into domains of functionally distinct chromatin states. In particular, experimental evidence has shown that the pattern of chromatin markers along chromosomes is strongly correlated with the 3D chromatin organization inside the nucleus. This suggests a coupling between epigenomic information and large-scale chromatin structure. In this presentation, I will review our efforts to understand the physical and biological mechanisms behind this coupling. Using polymer and statistical physics, we develop models of chromatin organization and epigenomic regulation in close collaboration with experimentalists. Based on original physical concepts, these models allow us to explore different aspects of the 3D-1D coupling of chromatin from drosophila to human.
Detection of gene communities in multi-networks reveals cancer drivers20m
Multi-Networks represent the most effective way to study functional regulatory patterns originating from complex interactions across multiple layers of biological relationships. Such a multi-network approach is mandatory when complex pathologies like cancer are addressed. In this talk we
discuss a new, original, multi-network-based strategy, which we recently published in Scientific Reports (2015) 5-17386, to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To test our proposal we applied it to a set of expression data for gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.
The 3-D organizaton of chromosomes: polymer physics models and applications20m
DrAndrea Maria Chiariello
(Università di Napoli Federico II)
Physics in bacteria: chromosome compaction, gene expression, growth of individual cells.40m
t has become possible recently to resolve important biological properties of cells in vivo, on large numbers of cells. This allows then models and approaches typical of statistical physics to be developed and deployed. In an effort to understand quantitatively aspects of bacterial life, we measure and model chromosome fluctuations, and rates of gene expression and cell growth. We consider the distributions in clonal populations, and search for correlations between these properties.
Dynamics of macromolecules with Theoretical Physics tools at Trento20m
In this contribution I will provide an overview of the progress of the Trento BIOPHYS group in development and apply theoretical physics methods (path integral formalism, quantum field theory, renormalisation group) to investigate the non-equilibrium dynamics of biomolecules.
In particular, I will focus on a unified "bottom-up" formalism we have develop which enables to describe both structural dynamics (conformational reactions and folding of proteins) as well as electronic excitations (linear and non-linear spectroscopy).
(Trento University and TIFPA)
Polymer modeling to unveil the complexity of DNA loci folding20m
(Università di Napoli Federico II)
Lunch (nel Dipartimento)
Understanding dynamical and statistical properties of urban mobility20m
The Complex Systems Science looks for power law distributions for relevant observables as a fingerprint of the complexity character.
However the identification of an empirical power law behavior of a complex system is rarely scientifically useful by itself, but needs to be model-informed. Since multiple competing models can explain the same pattern, it even risks swamping future research with years of replicating the same, and possibly wrong, pattern analysis.
Understanding individual mobility has important implications for traffic forecasting, epidemics spreading or the evolution of cities. Remarkably enough, what appears to be under-evaluated in the study of human mobility is the relevance of travel dynamics. Human traveling behavior is usually described as a sequence of rest
times and jumps in space. These two processes need to be separated, since costs are in general associated to trips while a positive utility can be associated to activities performed during stops. However, the proposed models usually neglect the role of travel time and the moving velocity and assume instantaneous jumps. We show that the observed truncated power laws in the jump size distribution can be the consequence of simple processes such as random walks with random velocities. The model is validated over a large GPS database describing the mobility of 780,000 private vehicles in Italy, where travels and stops can be easily separated, as the transition is identified by the moment when the engine is turned on or off. This allows us to evaluate accurately not only the displacements, but also travel times, speeds and rest times, and to propose a random walk acceleration model for human mobility based on simple, reasonable assumptions. Our random acceleration model leads to predictions in excellent agreement with data, and brings evidence that the long-standing interpretation with L\'evy flights is incorrect.
Trap competition in the Penney Model and in Markov processes20m
The Penney’s game [1,2] is a game that can be simply played with coins, betting on sequences of heads and tails (so it is a binary game). The players agree on the length of the sequence, at list of three bits, and then choose orderly which sequence they bet on. Then a coin is tossed several times, generating the binary sequence. The player whose sequence appears first wins. As odd as it may seem at first, The second player has always the possibility of winning over the first, statistically. Indeed, the Penney’s game is a well-known example of non-transitive games , so that for any given sequence of length three or more, one can always find another sequence that has a higher probability of occurring first. This game can be mapped onto a more general problem: given a Markov chain with a trap (the first sequence), where is more convenient to place a second trap in order to "shade" at most the first one? In this language, we are interested in interference among traps (intransitivity), and we developed some techniques to compute the degree of intransitivity . For the Penney's game, we show that there is a sort of phase transition: if the coins are sufficiently biased, the game the becomes more fair, in the sense that the first player choice may not always be defeated by the second one.
 Walter Penney, Journal of Recreational Mathematics, October 1969, p. 241.
 M. Gardner, On the paradoxical situations that arise from nontransitive relations., Sci. Am. 231 (1974) 120.
 Giulia Cencetti, Franco Bagnoli, Francesca Di Patti, Duccio Fanelli, The second will be first: competition on directed networks, Scientific Reports 6, Article number: 27116 (2016) doi:10.1038/srep27116
Relation between Zipf ’s law and the distribution of shared components in complex component systems.20m
Several complex systems of diverse nature consist of realizations which can be broken into their elementary constitutive components, for example, books into words, genomes into genes, and technological systems into building blocks.
The statistics of the components (e.g., word) across realizations (e.g., books) shows several quantitative laws, such ad the well-known example of the power-law distribution of component abundances, known as Zipf’s law in the context of natural languages.
Central to the current debate in evolutionary genomics is a different law, the ”gene-frequency distribution”, or ”occurrence distribution”, where a component
occurrence is defined as the fraction of realizations in which the component is present.
In genomes, the occurrence distribution shows a peculiar ?U-shape? due to a large number of rare (i.e. belonging to very few species) and common genes (present in almost all the species), compared to genes at intermediate occurrences.
While several possible theoretical explanations of the U-shaped gene occurrence distribution have been proposed, its causes are still under debate.
Here, we consider occurrence distributions in three datasets from genomics, linguistics (literary texts), and technology (LEGO toy constructions), showing that the U-shape is linked to the component frequency (i.e., the Zipf’s law).
By means of a theoretical model based on sampling, we establish an analytical relationship between these two laws, which allows us to identify the crucial parameters affecting the occurrence distribution power law decay and the size of the common component peak.
The null model captures some relevant empirical features, as well as highlighting deviations that carry important information about the specificity of each system.
(Physics Department and INFN, University of Turin)
Dimensional Reduction of Markov State Models from Renormalization Group Theory20m
Renormalization Group (RG) theory provides the theoretical framework to define Effective Theories, i.e. systematic low-resolution approximations of arbitrary microscopic models. Markov State Models (MSMs) can be shown to be rigorous Effective Theories for Molecular Dynamics (MD). Based on this fact, we use Real Space RG to vary the resolution of a MSM and define an algorithm for clustering microstates into macrostates. The result is a lower dimensional stochastic model which, by construction, provides the optimal coarse-grained Markovian representation of the system’s relaxation kinetics. To illustrate and validate our theory, we analyze a number of test systems of increasing complexity, ranging from synthetic toy models to two realistic applications, built form all-atom MD simulations.
Molecular simulations at bio/organic/inorganic interfaces20m
(Università di Trento)
Dynamics and rheology of cell and vesicles in shear flow20m
A deep understanding of the dynamics and rheology of suspensions of vesicles,
cells, and capsules is relevant for different applications, ranging from soft
glasses to blood ﬂow .
I will present the study of suspensions of fluid vesicles by a combination of
molecular dynamics and mesoscale hydrodynamics simulations
(multi-particle collision dynamics)
in two dimensions , pointing out the big potential of the numerical
method to address problems in soft matter. The flow behavior is studied
as a function of the shear rate,
the volume fraction of vesicles, and the viscosity ratio between inside
and outside fluids. Results are obtained for the interactions of two vesicles,
the intrinsic viscosity of the suspension, and the cell-free layer near
the walls [3-5].
 D. Barthes-Biesel, Annu. Rev. Fluid Mech. 48, 25 (2016)
 R. Finken, A. Lamura, U. Seifert, and G. Gompper, Eur. Phys. J. E 25,
 A. Lamura and G. Gompper, EPL 102, 28004 (2013)
 A. Lamura and G. Gompper, Procedia IUTAM 16, 3 (2015)
 E. Afik, A. Lamura, and V. Steinberg, EPL 113, 38003 (2016)
Cell Division Control in E. coli20m
The coordination of cell growth and division is a long-standing problem in biology. In particular, the mechanisms that ensure size homeostasis and, at the same time, adapt cell growth and size to the environment have been the subject of intense research. However, the answers were traditionally hindered by limited statistics on single cells. Contemporary experimental techniques overcome this problem, but this progress must be combined with new theoretical tools to approach the data. Focusing on E. coli, we introduced a quantitative method for estimating the variables controlling the division rate from dynamic data, and used it to build a minimal stochastic model of cell growth and division. Combining this method with large-scale microscopy experiments, classic quantitative laws relating cell size, doubling time and growth rate of bacterial populations in different nutrient conditions can be revisited at the single cell level. The main result is the emergence of a combination of universality and individuality in the growth-division laws of single E. coli cells. These two apparently contrasting behaviors emerge naturally from the condition-dependent modulation of the division control mechanism, thus actually representing two sides of the same coin. Finally, the simultaneous observation of cell growth and DNA replication dynamics allowed us to pinpoint replication initiation and cell division as the two main "checkpoints" for size control. This opens the way to more detailed models of the process, and to rigorous tests of molecular cell-cycle descriptions.
(University of Torino)
Properties of low–dimensional collective variables in the molecular dynamics of biopolymers20m
The description of the dynamics of a complex, high-dimensional system in terms of a low-dimensional set of collective variables (CVs) Ys can be fruitful if the low dimensional representation satisfies a Langevin equation, where drift and diffusion coefficients depend only on the value of Y, but not on the microscopic conformation on which Y has been calculated.
We present here a computational scheme to evaluate whether a given CV Y possess such a property or, on the contrary, whether the drift and diffusion coefficients take different values at distinct conformations, despite Y being equal.
The algorithm is based on the framework of finite-difference Langevin-equation, similar to that used for molecular-dynamics simulations. It allows one to calculate the associated drift and diffusion coefficients in any points of the full-dimensional system. In this way, one can calculate the distribution of drift and diffusion coefficients in an ensemble of microscopic conformations at the same value of Y. The width of this distribution indicates to which extent the dynamics of Y is described by a simple Langevin equation.
Using a simple protein model, we show that collective variables often used to describe biopolymers display a non-negligible width, both in the drift and in the diffusion coefficients.
Stochastic Model of Supercoiling-Dependent Transcription20m
We propose a stochastic model for gene transcription coupled to DNA supercoiling, where we incorporate the experimental observation that polymerases create supercoiling as they unwind the DNA helix and that these enzymes bind more favorably to regions where the genome is unwound. Within this model, we show that when the transcriptionally induced flux of supercoiling increases, there is a sharp
crossover from a regime where torsional stresses relax quickly and gene transcription is random, to one where gene expression is highly correlated and tightly regulated by supercoiling. In the latter regime, the model displays transcriptional bursts, waves of supercoiling, and up regulation of divergent or bidirectional genes.
(Università degli Studi di Bari)
Evolution of transcription factor families along the human lineage.20m
Transcription factors (TFs) exert their regulatory action by binding to DNA with specific sequence preferences.
However, different TFs can partially share their binding sequences. This ``redundancy'' of binding defines a way of organizing TFs in ``motif families'' that goes beyond the usual classification based on protein structural similarities. Since the TF binding preferences ultimately define the target genes, the motif family organization entails information about the structure of transcriptional regulation as it has been shaped by evolution. Focusing on the human lineage, we show that a one-parameter evolutionary model of the Birth-Death-Innovation type can explain the empirical repartition of TFs in motif families, thus identifying the relevant evolutionary forces at its origin.
More importantly, the model allows to pinpoint few deviations in human from the neutral scenario it assumes: three over-expanded families corresponding to HOX and FOX type genes, a set of ''singleton'' TFs for which duplication seems to be selected against, and an higher-than-average rate of diversification of the binding preferences of TFs with a Zinc Finger DNA binding domain.
Finally, a comparison of the TF motif family organization in different eukaryotic species suggests an increase of redundancy of binding with organism complexity.
Modeling Financial Markets by Self-Organized Criticality20m
We present a self-organized criticality (SOC) model to study herding and avalanche dynamics in financial markets. We first consider a community of interacting investors, distributed in a small-world network, who bet on the bullish (increasing) or bearish (decreasing) behavior of an exogenous real market. Then we modify the model in order to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts of financial markets. In both the models, we introduce in the community a variable number of random traders in order to study their possible beneficial role in stabilizing the market.
Self-organized criticality elucidates the conditions under which systems tune themselves to the edge of a second-order phase transition, with scale invariance. Motivated by the empirical observation of bimodal activity-burst distributions in neuroscience and other fields, we propose and analyze a theory for the self-organization to the point of phase-coexistence in systems exhibiting a first-order phase transition. It explains the emergence of regular avalanches with attributes of scale-invariance which coexist with huge anomalous ones, with possible realizations in many fields.
MrsSerena Di Santo
(Università di Parma)
Stochastic model of CPEB3 oligomerization for synaptic facilitation and LTM formation20m
CPEB3 is a mammalian prion-like protein of the CPEB family, which has been shown to mediate local protein synthesis in the synaptic facilitation
and Long Term Memory (LTM) formation both in vitro and in vivo.
Moreover, it has been observed that SUMOYlayion of CPEB3 regulates its oligomerization and activity. In the basal state, SUMOylated CPEB3
is the most abundant among CPEB3 forms, while deSUMOylation occurs when there is a synaptic activity.
From these information we built a simple stochastic model for LTM formation under external stimulus, assuming that CPEB3 oligomers represent the active form of the protein.
Our model considers a closed three-state chemical process, named SC (SUMOy-lated CPEB3 monomers),C_deol (pure CPEB3 monomers) and C_ol (oligomerized CPEB3 monomers). A synapse with no LTM will have mainly SC monomers, whilst the presence of LTM is correlated with a relevant number of oligomerized (C_ol) monomers. The transition from one state to another is regulated by the presence of an external stimulus, which increases of several orders of magnitude the transition probability from state SC to C_deol.
The fluctuations are relevant due to the small number of protein involved at synaptic scale.
We discuss the dynamics of this system analyzing the Laplacian Matrix properties of the Master Equation.
Since oligomers tend to aggregate more easily in presence an aggregation seed, we model the nonlinear character of the transition rates from C_deol to C_ol by an exponential law depending on a threshold parameter n_th, which represents the dimension of the aggregation seed. According to empirical observations, We consider a small number of total Monomers (form 8 to 15) and search for bistability behavior and transitions from an initial state SC and C_deol, as n_th varies in presence of an eternal stimulus.