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Alessia Annibale

Publications and source records attributed to Alessia Annibale.

At least 37 records · Page 2Linked to original sources
arXiv2020

Maximal modularity and the optimal size of parliaments

An important question in representative democracies is how to determine the optimal parliament size of a given country. According to an old conjecture, known as the cubic root law, there is a fairly universal power-law relation, with an exponent close to 1/3, between the size of an elected parliament and the country's population. Empirical data in modern European countries support such universality but are consistent with a larger exponent. In this work, we analyze this intriguing regularity using tools from complex networks theory. We model the population of a democratic country as a random network, drawn from a growth model, where each node is assigned a constituency membership sampled from an available set of size $D$. We calculate analytically the modularity of the population and find that its functional relation with the number of constituencies is strongly non-monotonic, exhibiting a maximum that depends on the population size. The criterion of maximal modularity allows us to predict that the number of representatives should scale as a power-law in the size of the population, a finding that is qualitatively confirmed by the empirical analysis of real-world data.

physics.soc-ph↗
arXiv2020

Assessing Position-Dependent Diffusion from Biased Simulations and Markov State Model Analysis

A variety of enhanced statistical and numerical methods are now routinely used to extract comprehensible and relevant thermodynamic information from the vast amount of complex, high-dimensional data obtained from intensive molecular simulations. The characterization of kinetic properties, such as diffusion coefficients, of molecular systems with significantly high energy barriers, on the other hand, has received less attention. Among others, Markov state models, in which the long-time statistical dynamics of a system is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years, with the aim of tackling these fundamental issues. Here we propose a general, automatic method to assess multidimensional position-dependent diffusion coefficients within the framework of Markovian stochastic processes and Kramers-Moyal expansion. We apply the formalism to one- and two-dimensional analytic potentials and data from explicit solvent molecular dynamics simulations, including the water-mediated conformations of alanine dipeptide. Importantly, the developed algortihm presents significant improvement compared to standard methods when the transport of solute across three-dimensional heterogeneous porous media is studied, for example, the prediction of membrane permeation of drug molecules.

cond-mat.soft↗
arXiv2020

Percolation on the gene regulatory network

We consider a simplified model for gene regulation, where gene expression is regulated by transcription factors (TFs), which are single proteins or protein complexes. Proteins are in turn synthesised from expressed genes, creating a feedback loop of regulation. This leads to a directed bipartite network in which a link from a gene to a TF exists if the gene codes for a protein contributing to the TF, and a link from a TF to a gene exists if the TF regulates the expression of the gene. Both genes and TFs are modelled as binary variables, which indicate, respectively, whether a gene is expressed or not, and a TF is synthesised or not. We consider the scenario where for a TF to be synthesised, all of its contributing genes must be expressed. This results in an ``AND'' gate logic for the dynamics of TFs. By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the necessary conditions, in the network parameter space, under which bipartite networks can support a multiplicity of stable gene expression patterns, under noisy conditions, as required in stable cell types. In particular, the analysis reveals the possibility of a bi-stability region, where the extensive percolating cluster is or is not resilient to perturbations. This is remarkably different from the transition observed in standard percolation theory. Finally, we consider perturbations involving single node removal that mimic gene knockout experiments. Results reveal the strong dependence of the gene knockout cascade on the logic implemented in the underlying network dynamics, highlighting in particular that avalanche sizes cannot be easily related to gene-gene interaction networks.

q-bio.MN↗
arXiv2019

Correlation Functions, Mean First Passage Times and the Kemeny Constant

Markov processes are widely used models for investigating kinetic networks. Here we collate and present a variety of results pertaining to kinetic network models, in a unified framework. The aim is to lay out explicit links between several important quantities commonly studied in the field, including mean first passage times (MFPTs), correlation functions and the Kemeny constant, and highlight some of the subtleties which are often overlooked in the literature, while providing new insights. Results include (i) a simple physical interpretation of the Kemeny constant, (ii) a recipe to infer equilibrium distributions and rate matrices from measurements of MFPTs, potentially useful in applications, including milestoning in molecular dynamics, and (iii) a protocol to reduce the dimensionality of kinetic networks, based on specific requirements that the MFPTs in the coarse-grained system should satisfy. It is proven that this protocol coincides with the one proposed by Hummer and Szabo in [1] and it leads to a variational principle for the Kemeny constant. We hope that this study will serve as a useful reference for readers interested in theoretical aspects of kinetic networks, some of which underpin useful applications, including milestoning and coarse-graining.

physics.chem-ph↗
arXiv2018

The role of T-helper/T-suppressor ratio in the adaptive immune response: a dynamical model

Recent experimental studies have suggested the ratio between T-helper and T-suppressor lymphocytes as an index of immunosuppression in HIV, cancer, immunosenescence and inflammatory and auto-immune diseases. However, a quantitative understanding of the impact of this ratio on the immune response has lagged behind data and its validity as a tool for prognostic monitoring or therapeutic target remains an open question. In this work, we use statistical physics and dynamical systems approaches to analyze the time-dependent response to an antigen, of a simplified model of the adaptive immune system, which comprises B, T-helper and T-suppressor lymphocytes. The model is remarkably robust against changes in the noise level and kinetic parameters, but it is very sensitive to changes in the ratio between T-helper and T-suppressor lymphocytes, exhibiting, in particular, a transition from a responsive to an immuno-suppressed phase, as the ratio is lowered below a critical value, which is in line with experiments. This result supports the validity of the T-helper/T-suppressor ratio as an index of immunosuppression and may provide a useful theoretical benchmark to interpret and compare experiments.

q-bio.CB↗
arXiv2017

Retrieving Infinite Numbers of Patterns in a Spin-Glass Model of Immune Networks

The similarity between neural and immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies {\em in parallel}. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with `coordinator branches' (T-cells) and `effector branches' (B-cells), and show how the finite connectivity enables the system to manage an extensive number of immune clones simultaneously, even above the percolation threshold. The system exhibits only weak ergodicity breaking, so that both multiple antigen defense and homeostasis can be accomplished.

cond-mat.dis-nn↗
arXiv2016

The role of idiotypic interactions in the adaptive immune system: a belief-propagation approach

In this work we use belief-propagation techniques to study the equilibrium behaviour of a minimal model for the immune system comprising interacting T and B clones. We investigate the effect of the so-called idiotypic interactions among complementary B clones on the system's activation. Our result shows that B-B interactions increase the system's resilience to noise, making clonal activation more stable, while increasing the cross-talk between different clones. We derive analytically the noise level at which a B clone gets activated, in the absence of cross-talk, and find that this increases with the strength of idiotypic interactions and with the number of T cells signalling the B clone. We also derive, analytically and numerically, via population dynamics, the critical line where clonal cross-talk arises. Our approach allows us to derive the B clone size distribution, which can be experimentally measured and gives important information about the adaptive immune system response to antigens and vaccination.

cond-mat.dis-nn↗
arXiv2015

A dynamical model of the adaptive immune system: effects of cells promiscuity, antigens and B-B interactions

We analyse a minimal model for the primary response in the adaptive immune system comprising three different players: antigens, T and B cells. We assume B-T interactions to be diluted and sampled locally from heterogeneous degree distributions, which mimic B cells receptors' promiscuity. We derive dynamical equations for the order parameters quantifying the B cells activation and study the nature and stability of the stationary solutions using linear stability analysis and Monte Carlo simulations.The system's behaviour is studied in different scaling regimes of the number of B cells, dilution in the interactions and number of antigens. Our analysis shows that: (i) B cells activation depends on the number of receptors in such a way that cells with an insufficient number of triggered receptors cannot be activated; (ii) idiotypic (i.e. B-B) interactions enhance parallel activation of multiple clones, improving the system's ability to fight different pathogens in parallel; (iii) the higher the fraction of antigens within the host the harder is for the system to sustain parallel signalling to B cells, crucial for the homeostatic control of cell numbers.

q-bio.CB↗
arXiv2015

The two-star model: exact solution in the sparse regime and condensation transition

The $2$-star model is the simplest exponential random graph model that displays complex behavior, such as degeneracy and phase transition. Despite its importance, this model has been solved only in the regime of dense connectivity. In this work we solve the model in the finite connectivity regime, far more prevalent in real world networks. We show that the model undergoes a condensation transition from a liquid to a condensate phase along the critical line corresponding, in the ensemble parameters space, to the Erdös-Rényi graphs. In the fluid phase the model can produce graphs with a narrow degree statistics, ranging from regular to Erdös-Rényi graphs, while in the condensed phase, the "excess" degree heterogeneity condenses on a single site with degree $\sim\sqrt{N}$. This shows the unsuitability of the two-star model, in its standard definition, to produce arbitrary finitely connected graphs with degree heterogeneity higher than Erdös-Rényi graphs and suggests that non-pathological variants of this model may be attained by softening the global constraint on the two-stars, while keeping the number of links hardly constrained.

cond-mat.dis-nn↗
arXiv2015

Protein interaction networks and biology: towards the connection

Protein interaction networks (PIN) are popular means to visualize the proteome. However, PIN datasets are known to be noisy, incomplete and biased by the experimental protocols used to detect protein interactions. This paper aims at understanding the connection between true protein interactions and the protein interaction datasets that have been obtained using the most popular experimental techniques, i.e. mass spectronomy (MS) and yeast two-hybrid (Y2H). We show that the most natural adjacency matrix of protein interaction networks has a separable form, and this induces precise relations between moments of the degree distribution and the number of short loops. These relations provide powerful tools to test the reliability of datasets and hint at the underlying biological mechanism with which proteins and complexes recruit each other.

cond-mat.dis-nn↗
arXiv2014

Associative networks with diluted patterns: dynamical analysis at low and medium load

In this work we solve the dynamics of pattern diluted associative networks, evolving via sequential Glauber update. We derive dynamical equations for the order parameters, that quantify the simultaneous pattern recall of the system, and analyse the nature and stability of the stationary solutions by means of linear stability analysis as well as Monte Carlo simulations. We investigate the parallel retrieval capabilities of the system in different regions of the phase space, in particular in the low and medium storage regimes and for finite and extreme pattern dilution. Results show that in the absence of patterns cross-talk, all patterns are recalled symmetrically for any temperature below criticality, while in the presence of pattern cross-talk, symmetric retrieval becomes unstable as temperature is lowered and a hierarchical retrieval takes over. The shape of the hierarchical retrieval occurring at zero temperature is provided. The parallel retrieval capabilities of the network are seen to degrade gracefully in the regime of strong interference, but they are not destroyed.

cond-mat.dis-nn↗
arXiv2014

Extensive load in multitasking associative networks

We use belief-propagation techniques to study the equilibrium behavior of a bipartite spin-glass, with interactions between two sets of $N$ and $P = αN$ spins. Each spin has a finite degree, i.e.\ number of interaction partners in the opposite set; an equivalent view is then of a system of $N$ neurons storing $P$ diluted patterns. We show that in a large part of the parameter space of noise, dilution and storage load, delimited by a critical surface, the network behaves as an extensive parallel processor, retrieving all $P$ patterns {\it in parallel} without falling into spurious states due to pattern cross-talk and typical of the structural glassiness built into the network. Our approach allows us to consider effects beyond those studied in replica theory so far, including pattern asymmetry and heterogeneous dilution. Parallel extensive retrieval is more robust for homogeneous degree distributions, and is not disrupted by biases in the distributions of the spin-glass links.

cond-mat.dis-nn↗
arXiv2013

Immune networks: multi-tasking capabilities near saturation

Pattern-diluted associative networks were introduced recently as models for the immune system, with nodes representing T-lymphocytes and stored patterns representing signalling protocols between T- and B-lymphocytes. It was shown earlier that in the regime of extreme pattern dilution, a system with $N_T$ T-lymphocytes can manage a number $N_B!=!\order(N_T^δ)$ of B-lymphocytes simultaneously, with $δ!<!1$. Here we study this model in the extensive load regime $N_B!=!αN_T$, with also a high degree of pattern dilution, in agreement with immunological findings. We use graph theory and statistical mechanical analysis based on replica methods to show that in the finite-connectivity regime, where each T-lymphocyte interacts with a finite number of B-lymphocytes as $N_T\to\infty$, the T-lymphocytes can coordinate effective immune responses to an extensive number of distinct antigen invasions in parallel. As $α$ increases, the system eventually undergoes a second order transition to a phase with clonal cross-talk interference, where the system's performance degrades gracefully. Mathematically, the model is equivalent to a spin system on a finitely connected graph with many short loops, so one would expect the available analytical methods, which all assume locally tree-like graphs, to fail. Yet it turns out to be solvable. Our results are supported by numerical simulations.

cond-mat.dis-nn↗
arXiv2013

Immune networks: multi-tasking capabilities at medium load

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low load regime, where the number $P$ of simultaneously retrievable patterns scales with the number $N$ of nodes as $P\sim \log N$. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium load regime, $P \sim N^δ$ with $δ\in (0,1]$. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for $δ<1$ the existence of large regions in the phase diagram where the network can retrieve all stored patterns simultaneously. Finally, in the high load regime $δ=1$ we find that the system behaves as a spin glass, suggesting that finite-connectivity frameworks are required to achieve effective retrieval.

cond-mat.dis-nn↗
arXiv2009

Dynamic heterogeneities in critical coarsening: Exact results for correlation and response fluctuations in finite-sized spherical models

We study dynamic heterogeneities in the out-of-equilibrium coarsening dynamics of the spherical ferromagnet after a quench from infinite temperature to its critical point. A standard way of probing such heterogeneities is by monitoring the fluctuations of correlation and susceptibility, coarse-grained over mesoscopic regions. We discuss how to define fluctuating coarse-grained correlations (C) and susceptibilities (Chi) in models where no quenched disorder is present. Our focus for the spherical model is on coarse-graining over the whole volume of $N$ spins, which requires accounting for N^{-1/2} non-Gaussian fluctuations of the spin. The latter are treated as a perturbation about the leading order Gaussian statistics. We obtain exact results for these quantities, which enable us to characterise the joint distribution of C and Chi fluctuations. We find that this distribution is qualitatively different, even for equilibrium above criticality, from the spin-glass scenario where C and Chi fluctuations are linked in a manner akin to the fluctuation-dissipation relation between the average C and Chi. Our results show that coarsening at criticality is clearly heterogeneous for d>4 and suggest that, as in other glassy systems, there is a well-defined timescale on which fluctuations across thermal histories are largest. Surprisingly, however, neither this timescale nor the amplitude of the heterogeneities increase with the age of the system, as would be expected from the growing correlation length. For d<4, the strength of the fluctuations varies on a timescale proportional to the age of the system; the corresponding amplitude also grows with age, but does not scale with the correlation volume as might have been expected naively.

cond-mat.dis-nn↗
arXiv2008

Fluctuation-dissipation relations in critical coarsening: crossover from unmagnetized to magnetized initial states

We study the non-equilibrium dynamics of the spherical ferromagnet quenched to its critical temperature, as a function of the magnetization of the initial state. The two limits of unmagnetized and fully magnetized initial conditions can be understood as corresponding to times that are respectively much shorter and much longer than a magnetization timescale, as in a recent field theoretical analysis of the $n$-vector model. We calculate exactly the crossover functions interpolating between these two limits, for the magnetization correlator and response and the resulting fluctuation-dissipation ratio (FDR). For $d>4$ our results match those obtained recently from a Gaussian field theory. For $d<4$, non-Gaussian fuctuations arising from the spherical constraint need to be accounted for. We extend our framework from the fully magnetized case to achieve this, providing an exact solution for the relevant integral kernel. The resulting crossover behaviour is very rich, with the asymptotic FDR $X^\infty$ depending non-monotonically on the scaled age of the system. This is traced back to non-monotonicities of the two-time correlator, themselves the consequence of large magnetization fluctuations on the crossover timescale. We correct a trivial error in our earlier calculation for fully magnetized initial states; the corrected FDR is {\em consistent} with renormalization group expansions to first order in $4-d$ for the longitudinal fluctuations of the O(n) model in the limit $n\to\infty$.

cond-mat.dis-nn↗
arXiv2004

Coexistence of supersymmetric and supersymmetry-breaking states in spherical spin-glasses

The structure of states of the perturbed p-spin spherical spin-glass is analyzed. At low enough free energy metastable states have a supersymmetric structure, while at higher free energies the supersymmetry is broken. The transition between the supersymmetric and the supersymmetry-breaking phase is triggered by a change in the stability of states.

cond-mat.dis-nn↗