Meeting BioPhys & PlexNet 2019

24 Sept 2019, 13:55 → 26 Sept 2019, 13:05 Europe/Rome

Conference room (Stazione zoologica Anton Dohrn)

Meeting BioPhys & PlexNet 2019 
Physics, Statistics, Computational Biology and Complex Systems

The meeting aims at developing the methods of theoretical physics, computer science and bioinformatics to understand living systems at three different, interlinked scales: the characterization of biomolecules and their interactions; the 3D organization and regulation of genomes via genomics data; regulatory networks of molecules, cells and neurons.

Tuesday 24 September

14:00 → 14:20 Welcome

14:20 → 14:45 Synergy as a precursor of transitions: the case of the 2D Ising model

We consider the formalism of information decomposition of target effects from multi-source interactions, i.e. the problem of defining redundant and synergistic components of the information that a set of source variables provides about a target, and apply it to the two-dimensional Ising model as a paradigm of a critically transitioning system. Intuitively, synergy is the information about the target variable that is uniquely obtained taking the sources together, but not considering them alone; redundancy is the information which is shared by the sources. To disentangle the components of the information both at the static level and at the dynamical one, the decomposition is applied respectively to the mutual information and to the transfer entropy between a given spin, the target, and a pair of neighbouring spins (taken as the drivers). We show that a key signature of an impending phase transition (approached from the disordered size) is the fact that the synergy peaks in the disordered phase, both in the static and in the dynamic case: the synergy can thus be considered a precursor of the transition. The redundancy, instead, reaches its maximum at the critical temperature. The peak of the synergy of the transfer entropy is far more pronounced than those of the static mutual information. We show that these results are robust w.r.t. the details of the information decomposition approach, as we find the same results using two different methods; moreover, w.r.t. previous literature rooted on the notion of Global Transfer Entropy, our results demonstrate that considering as few as three variables is sufficient to construct a precursor of the transition, and provide a paradigm for the investigation of a variety of systems prone to crisis, like financial markets, social media, or epileptic seizures.

Speaker: Prof. Sebastiano Stramaglia (Università di Bari)

14:45 → 15:10 Chromatin architecture code inferred by machine learning and polymer physics

Speaker: Andrea Esposito (Università di Napoli)

15:10 → 15:35 Neural inhibition and the statistical mechanics of asynchronous states

Neural networks are shaped by the combined action of excitatory and inhibitory interactions. Here we show that inhibition solves the problem of the all-or-none type of response that comes about in purely excitatory networks, allowing the network to operate at low activity, between quiescent and saturated regimes. In particular, the low activity phase is generated by a noise-induced effect that we call “Jensen’s force” –stemming from the combined effect of excitation/inhibition balance and network sparsity .The phase reproduces relevant features of asynchronous states in cortical networks, which are characterized by low, uncorrelated and highly-irregular activity. Our approach provides a simple understanding of asynchronous states and of the phase transitions to other phases, putting asynchronous-state and critical-state hypotheses, within a unified framework. We argue that Jensen’s forces could be measurable experimentally and might be relevant in contexts beyond neuroscience.

Speaker: Prof. Alessandro Vezzani ( Università di Parma)

15:35 → 16:00 Predicting genome 3D architecture in development and disease

Speaker: Dr Simona Bianco (Università di Napoli)

16:00 → 16:30 Coffee Break

16:30 → 16:55 Synchronization and amplification in long-range interacting neuromorphic networks

Neuromorphic networks are the basic ingredients to let intelligent abilities emerge from an artificial hardware composed by connected spiking neurons. These systems can be described in terms of coarse-grained interacting units, and still some interesting behaviors spontaneously arise. It has been recently found that a directed linear chain of connected patch of neurons amplifies an input signal, also tuning its characteristic frequency. Here we study a generalization of such a simple model, introducing heterogeneity and variability in the parameter space and long-range interactions, breaking, in turn, the preferential direction of information transmission of a directed chain. On one hand, enlarging the region of parameters leads to more complicated conditions to let the amplification settled in; moreover, it becomes possible to have a given frequency distribution tuning the non-local interactions of each node. On the other hand, adding long-range interactions can cause the onset of novel phenomena, as coherent and synchronous oscillations among all the interacting units, which can also coexist with the amplification of the signal. The interplay between these two cooperative features lay the foundations for the understanding of the core ingredients needed to have a robust and coherent transmission of an input signal. In the light of the observations in biological neural networks on the role of amplification, synchronization and phase-locking, reproducing similar behaviors on neuromorphic systems could help in engineering hardwares which perform more complex computational tasks.

Speaker: Ilenia Apicella (Università di Padova)

16:55 → 17:20 Study of Brownian yet non Gaussian diffusion of silica microspheres in a stochastic optical field

Speaker: Antonio Ciarlo (Università di Napoli)

17:20 → 17:45 Modelling pedestrian mobility on a road network: the Venezia project

The tourist flows in the historical centre of Venice have been continuously increasing in the recent years and the local authorities have to cope with the problems of liveability level for the resident population and frailty of the cultural heritage. To face these problems and to propose new governance tools, the Venice municipality has proposed the project of a Smart Control Room (SCR) which constitutes a central hub for the collection and analysis, through dynamical models, of multi-source mobility data. The Information Communication Technologies (ICT) that provide dynamical data on the individual mobility, will be integrated by many flow counting and people counting sensors distributed on the road network. These sensors will allow to collect real time data of local flows, the pedestrian velocity field and the crowd density. These data have to be integrated into dynamical models that simulates the pedestrian microscopic dynamics and water mobility taking advantage from the knowledge of the use of the road network provided by the ICT data and the public transportation data. The models have to perform a nowcasting of the mobility state on the whole road network in the historical centre, to forecast the short-term evolution and to point out the criticalities. Moreover the models have to be able to analyze different possible scenarios to perform a real time governance of the tourist flows, to provide the stakeholders with a useful tool to plan a sustainable mobility during large tourist events. The Laboratory of Complex Systems Physics of Bologna University is in charge to develop the dynamical models for pedestrian mobility in Venice. We illustrate the main features of the models, the results of the performed tests to measure the flow and people counting using video-cameras equipped by a neural networks and the results of an analysis performed using GNSS data from mobile devices (collaboration with TIM) to reconstruct the dynamical features of individuals moving on the Venice road network.

Speaker: Dr Alessandro Fabbri (Università di Bologna)

Wednesday 25 September

09:15 → 09:40 Interplay between lipid lateral diffusion, dye concentration and membrane permeability in a model lipid bilayer

Lipid lateral diffusion in membrane bilayers is a fundamental process exploited by cells to enable complex protein structural and dynamic reorganizations. For its importance, lipid mobility in both cellular and model bilayers has been extensively investigated in recent years, especially through
the application of time-resolved, fluorescence-based, optical microscopy techniques. However, one caveat of fluorescence techniques is the need to use dye-labeled variants of the lipid of interest, thus potentially perturbing the structural and dynamic properties of the native species. Generally, the effect of the dye/tracer molecule is implicitly assumed to be negligible. Nevertheless, in view of the widespread use of optically modified lipids for studying lipid bilayer dynamics, it is highly desirable to well assess this point. Here, fluorescence correlation spectroscopy (FCS) and molecular dynamics (MD) simulations have been combined together to uncover subtle structural and dynamic effects in DOPC planar membranes enriched with a standard Rhodamine-labeled lipid. Our findings support a non- neutral role of the dye-labeled lipids in diffusion experiments, quantitatively estimating a decrease
in lipid mobility of up to 20% with respect to the unlabeled species. Moreover, results highlight the existing interplay between dye concentration, lipid lateral diffusion and membrane permeability, thus suggesting possible implications for future optical microscopy studies of biophysical processes occurring at the membrane level.

Speaker: Prof. Giuseppe Brancato (Scuola Normale Superiore di Pisa)

09:40 → 10:05 All-Atom Simulation of Protein Folding and Direct Validation Against Time-Resolved Spectroscopy Experiments.

The theoretical characterization of a protein folding process requires to overcome three main formidable challenges. First, the transition path ensemble for the folding reaction has to be accurately sampled. Next, the reactive trajectories generated by computer simulations have to be post-processed, in order to identify the kinetically relevant meta-stable states. Finally, this structural information needs to be translated into predictions for experimental observables, to enable experimental validation.

In this talk, I discuss how these three major steps can be tackled by means of a specific combination of path integral-based enhanced sampling algorithms and approximation schemes [1], Renormalization Group-based statistical analyses [2], and excited-state quantum chemical calculations (required to connect molecular dynamics with time-resolved spectroscopy experiment) [3].

For illustration purposes, we report on applications of this scheme to predict the time-resolved single-molecule FRET and ensemble near-UV CD signals for the folding of several proteins consisting of several hundreds of amino-acids, using a realistic all-atom force field. Depending on time availability, we will also report about the application on the same scheme to simulate the interconversion between the cellular prion protein PrP-C into aggregates of the pathogenic scrapie isoform PrP-Sc [4].

References
[1] S. A Beccara, T. Skrbic, R. Covino and P. Faccioli, Proc. Natl. Acad. Sci. USA 109 2330 (2012) S. a Beccara, L. Fant and P. Faccioli, Phys. Rev. Lett. 114, 098103 (2015). S. Orioli, S. a Beccara and P. Faccioli, J. Chem. Phys. 147, 064108 (2017);
[2] S. Orioli and P. Faccioli, J. Chem. Phys. 145, 124120 (2016).
[3] A. Ianeselli, S. Orioli, G. Spagnolli, P.Faccioli, L. Cupellini, S. Jurinovich, B.Mennucci, J. Am. Chem. Soc.140, 3674 (2018)
[4] G. Spagnolli et al, “Full Atomistic Model of Prion Structure and Conversion”, to appear in PLoS Pathogens (2019)

Speaker: Prof. Pietro Faccioli (TIFP)

10:05 → 10:30 Storage capacity and criticality in a spiking network

Several experimental results [1, 2, 3, 4, 5] concerning information coding, processing and memory in the brain, show that, combining spatial population coding with temporal coding into a dual code, provides signi?cant gains of information. Some evidence of independent rate and temporal coding also supports the idea that phase timing can code for an independent information, and population rate for another additional information.
However the mechanisms leading to such spatio-temporal coding in the brain and the possibility to use such dual coded patterns in an associative memory framework is not yet clear. Some attemps to include time and define dynamical patterns instead of static one has been done, such as sequence processing for Hopfiled-like models [6], or synfire chains spiking models [7].
In this work [8] we introduce a new paradigm: a nested coding where a population (spatial) distribution of active neurons is combined with phase-of-firing (temporal) label of active neurons, and we show that patterns coded in this way can be stored and retrieved in a associative memory framework.
Notably the learning of such patterns is not achieved with a manual design, but with a biologically plausible learning rule, composed of global inhibition term which play a crucial role, and a STDP-plasticity term, which gives a LTP only when presynaptic spike arrive few ms before post-synaptic spike.
We define an order parameter to measure the success of the cue-induced retrieval of such dynamical patterns, and measure the \information capacity", defined as the number of patterns encoded times the number of bits carried by each pattern, as a function of the learning parameters, network size, and fraction M=N of active neurons in each pattern.
Notably such capacity in dual coded patterns is higher of both the case of phase-coded alone patterns, and the case of Hopfield model with population (spatial) coding alone, while it is close than the maximum capacity found for sequence processing binary networks [6]. It's interesting that there seems to be a unique maximum information capacity for storing dynamical patterns, in which time has been introduced in two different ways in
two very different models, our spiking model with dual coded spatiotemporal patterns and STDP-plasticity, and sequence of binary patterns in a Hopfiled-like model with discrete time and binary Ising-like units.
Therefore, we introduce a new way to look at dynamical spiking patterns, based on a biological-motivated learning rule that enable the system to work as an associative memory, showing that the information capacity of such spiking patterns with nested phase and spatial coding is enhanced with respect to the population coding alone and the phase coding alone[8].
We then link togheter the memory function with another intriguing phenomena characterize cortical dynamics both in-vitro
and in-vivo: criticality and scale-free neuronal avalanches, characterized by power law distributions of bursts of spikes both in-vitro and in the resting spontaneous activity.
Near the phase transition between persistent replay and no-replay
regimes of the spontaneous dynamics[9], critical phenomena and neural avalanches are observed, with critical exponents close to the ones experimentally measured. Previous studies have separately addressed the topics of phase-coded memory storage and neuronal avalanches, and this is one of the few works which show how these ideas converge in a single cortical model. This work therefore helps to link the bridge between criticality and the need to have a reservoir of spatio-temporal metastable memories[9].

[1] Huxter John, Burgess Neil, and O'Keefe John," Independent rate and temporal coding in hippocampal pyramidal cells," Nature 425, 828-832 (2003).
[2] C. Kayser, M.A. Montemurro, K. Nikos, and N.K. Logothetis, "Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns," Neuron 61, 597-608 (2009).
[3] S. Panzeri, R.A. Ince, M.E. Diamond, and C. Kayser, "Reading spike timing without a clock: intrinsic decoding of spike trains," Phil. Trans. R. Soc. B 369, 20120467 (2014).
[4] John O'Keefe and Neil Burgess, "Dual phase and rate coding in hippocampal place cells: theoretical signi?cance and relationship to entorhinal grid cells," Hippocampus 15, 853 (2005).
[5] D. Ji and M.A. Wilson, \Coordinated memory replay in the visual cortex and hip pocampus during sleep," Nat. Neurosci. 10, 100-107 (2007).
[6] A. During, A.C. Coolen, and D. Sherrington," Phase diagram and storage capacity of sequence processing neural networks," J. Phys. A: Math. Gen. (1998).
[7] M. Diesmann, M.O. Gewaltig, and A. Aertsen, "Stable propagation of synchronous spiking in cortical neural networks," Nature 402, 529-533 (1999).
[8] S. Scarpetta, A. de Candia, “Information capacity of a network of spiking neurons", submitted arXiv:1906.0, 2019
[9] Scarpetta, S., Apicella, I., Minati, L., De Candia, A. Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network (2018) Physical Review E 97, 062305 2018

Speaker: Dr Silvia Scarpetta (Università di Salerno)

10:30 → 11:00 Coffee Break

11:00 → 11:25 A self-organised method for determining the critical value of percolation-like processes on (multiplex) networks

We illustrate a method for mapping critical processes (percolation, infection, infection in the presence of risk perception) onto self-organised processes, i.e., processes that automatically converges towards the critical threshold. This procedure can be carried on with infinite precision for "convex" systems (systems for which the probability of infection.percolation grows monotonically with control parameters), while for a generic process it may give results with fixed (but arbitrary) precision.

Speaker: Prof. Franco Bagnoli (FI)

11:25 → 11:50 Globule microphase separation explains chromatin folding variability across single-cells.

Speaker: Mattia Conte (Università di Napoli)

11:50 → 12:15 Epigenetica e modellistica

Speaker: Prof. Sergio Cocozza (Università di Napoli)

12:15 → 14:15 Lunch

14:15 → 14:40 Genomic analysis for rare disease diagnosis

Speaker: Dr Michele Pinelli (Tigem Napoli)

14:40 → 15:05 Reverse modelling metabolic networks

Speaker: Dr Andrea De Martino (CNR Roma)

15:05 → 15:30 Paused RNA Polymerase II: a promise or a threat? The difference lies in the 3D genome architecture

Speaker: Dr Gaetano Ivan Dellino (IEO)

15:30 → 16:00 Coffee Break

16:00 → 16:25 Understanding transcription factors through quantitative biology

Davide Cacchiarelli1,2
1TIGEM (Telethon Institute of Genetics and Medicine) – Armenise/Harvard Laboratory of Integrative Genomics
2University of Naples “ Federico II” – Department of Translational Medicine

The original concept of cell differentiation as a unidirectional process of progressively restricted potential and increased specialization has been dramatically revised by the discovery of cellular reprogramming. The appropriate cocktail of transcription factors (TFs) allows the production of Induced Pluripotent Stem Cells (IPSCs) from almost any type of somatic cell, with extended self-renewal capabilities and broad differentiation potential. This concept has infused an unprecedented boost in the use of TFs not only in reprogramming to pluripotency but, in general, to drive cell fate decisions in vitro.
In our laboratory, we design and apply quantitative methods to dissect the role of transcription factors during reprogramming, conversion, and differentiation of human cells. We also study how rare genetic disorders alter the normal function of TFs (and proteins in general) with the final aim to predict the severity of rare genetic variants even before their onset in the general population.
I will describe two general approaches to these scopes: i) an approach of quantitative single-cell genomics to identify the subpopulations that arise during the reprogramming process to pluripotency and reconstruct their relationships. ii) a quantitative method to test in parallel hundreds of distinct rare variants of TP63, a TF that acts as the master regulator of skin development and whose mutation are associated with AEC syndrome, a monogenic disorder with severe skin defects.

Speaker: Dr Davide Cacchiarelli (Tigem and Università di Napoli Federico II)

16:25 → 16:50 The information flow in protein folding

Proteins employ the information stored in the genetic code and translated into their sequences to carry out well-defined functions in the cellular environment. The possibility to encode for such functions is controlled by the balance between the amount of information supplied by the sequence and that left after that the protein has folded into its structure. We study the amount of information necessary to specify the protein structure, providing an estimate that keeps into account the thermodynamic properties of protein folding. We thus show that the information remaining in the protein sequence after encoding for its structure (the "information gap") is very close to what needed to encode for its function and interactions. Then, by predicting the infor- mation gap directly from the protein sequence, we show that it may be possible to use these insights from information theory to discriminate between ordered and disordered proteins, to identify unknown functions, and to optimize artificially-designed protein sequences.

Speaker: Prof. Guido Tiana (MI)

16:50 → 17:15 Impact of different environmental conditions on the swimming strategies of planktonic copepods

Raffaele Pastore*
Department of Chemical, Materials and Production Engineering,
Università di Napoli Federico II, Napoli, Italy.

Marco Uttieri*
Department of Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy.

Suspensions of small planktonic copepods represent a special category in the realm of active matter, as their size is relatively small, whereas the wide range of individual variability and motion patterns resembles that of much larger animals. In this talk, after an overview of copepod swimming behaviour, we will discuss the possibility of detecting how the motion of organisms is affected by different external conditions, such as the presence of food and the effect of gravity, drawing on a large number of three-dimensional trajectories of copepods[1]. We will show that this goal can be quickly obtained by focusing on simple average metrics commonly used to characterize colloidal suspensions, such as the mean square displacement and temporal autocorrelation functions. We find that the presence of food leads to the onset of a clear localization that separates a short-time ballistic from a long-time diffusive regime. Such a benchmark reflects the tendency of copepods to remain temporally feeding in a limited space and disappears when food is absent. Localization is clearly evident in the horizontal plane, but is negligible in the vertical direction, due to the effect of gravity.
We will also show preliminary results demonstrating that a similar approach can be also used to investigate the impact of crowding [2].
Overall, our results suggest that simple average descriptors may provide concise and useful information on the swimming properties of planktonic copepods, even though single organism behaviour is strongly heterogeneous.

[1] R.Pastore, M. Uttieri, G. Bianco, M. Ribera d’Alcalà and M. G. Mazzocchi, Distinctive diffusive properties of swimming planktonic copepods in different environmental conditions, European Physical Journal E 41, 79 (2018).

[2] M. Uttieri, P. Hinow, R. Pastore, G. Bianco, M. Ribera d’Alcalà and M. G. Mazzocchi, Swimming performance of the copepod Centropages typicus at increasing populations densities, In preparation.

*JOINT TALK

Speakers: Dr Marco Uttieri (Stazione Zoologica Anton Dohrn), Dr Raffaele Pastore (Università di Napoli)

17:15 → 17:40 Extracting information from DNA sequences through statistical modelling

DNA sequences convey information on protein codification, but recent experimental observations point at active roles also on spatial conformation (chromatin) through epi-genetic mechanisms.
A key question is: which information about DNA regulation is embedded into DNA sequence?
We show recent results based on our analyses, in which we consider a DNA sequence as the realization of a stochastic process combined with data analysis methods.

Speaker: Prof. Daniel Remondini (Istituto Nazionale di Fisica Nucleare)

21:00 → 00:00 Social Dinner

Thursday 26 September

09:15 → 09:40 Monitoring Insulin Aggregated Structures in the Presence of Epigallocatechin-3-gallate and Melatonin by Molecular Dynamics Simulations

Epigallocatechin-3-gallate (EGCG) and melatonin ability to inhibit human insulin fibrillogenesis has been recently investigated by means of a number of experimental techniques [1]. Infrared and Raman spectroscopy analysis, after long-term incubation, showed a high content of inter-molecular -sheet structures in insulin-EGCG complexes. Near-UV experiments, Thioflavin-T fluorescence measurements and Atomic Force Microscopy images clearly revealed that in the presence of EGCG insulin tends to form amorphous, stable aggregates rather than fibrils. The same kind of experiments showed that melatonin has no significant inhibitory effects on insulin fibril formation.
We present here, together with the experimental results of [1], an extensive classical MD study [2] of the behavior of six insulin molecules in water in the presence and in the absence of either EGCG or melatonin in order to investigate at atomistic level how these molecules can possibly affect the insulin aggregation propensity. For each model system, we performed three independent simulations (replicas) [3], finding out that, while melatonin does not possess well-defined interaction sites with insulin, EGCG interacts mainly with the residues 11-18 and 24-26 of chain B of the insulin molecules. An important result of our simulations, in excellent agreement with experimental data, is that the shapes of the aggregated structures formed in the three model systems are significantly different depending on whether insulin is in the absence or in the presence of EGCG.

References
[1] Carbonaro, M. et al. Int. J. Biol. Macromol. 2018, 115, 1157–1164.
[2] Vitale, A. and Minicozzi, V. submitted to J. Chem. Inf. Model.
[3] Knapp, B. et al. J. Chem. Theory Comput. 2018, 14, 6127–6138.

Speaker: Dr Velia Minicozzi (ROMA2)

09:40 → 10:05 Sequence and structural patterns detected in entangled proteins reveal the importance of co-translational folding

Proteins must fold quickly to acquire their biologically functional three-dimensional native structures. Hence, these are mainly stabilized by local contacts, while intricate topologies such as knots are rare. Here, we reveal the existence of specific patterns adopted by protein sequences and structures to deal with backbone self-entanglement. A large scale analysis of the Protein Data Bank shows that loops significantly intertwined with another chain portion are typically closed by weakly bound amino acids. Why is this energetic frustration maintained? A possible picture is that entangled loops are formed only toward the end of the folding process to avoid kinetic traps. Consistently, these loops are more frequently found to be wrapped around a portion of the chain on their N-terminal side, the one translated earlier at the ribosome. Finally, these motifs are less abundant in natural native states than in simulated protein-like structures, yet they appear in 32% of proteins, which in some cases display an amazingly complex intertwining.

Speaker: Prof. Antonio Trovato (Università di Padova)

10:05 → 10:30 Technologies detecting DNA structure: insights from a polymer-physics-based computational experiment

Speaker: Luca Fiorillo (Università di Napoli)

10:30 → 10:55 Exploring genome spatial rearrangements and higher-order organization with Polymer Physics

Speaker: Dr Andrea Maria Chiariello (Università di Napoli)

10:55 → 11:25 Coffee Break

11:25 → 11:50 WISDOM: toward correlation-based modeling of neurological data.

In this work we introduce the Wishart Distributed Matrices Multiple Order Classification (WISDoM) method. The WISDoM Classification method consists of a pipeline for single feature analysis, supervised learning, cross validation and classification for any problems whose elements can be tied to a symmetric positive-definite matrix representation. The general idea is for information about properties of a certain system contained in a symmetric positive-definite matrix representation (i.e covariance and correlation matrices) to be extracted by modeling an estimated distribution for the expected classes of a given problem.

The application to fMRI resting state data classification and clustering processing fol- lows naturally: the WISDoM classification method has been tested on the ADNI2 (Alzheimer’s Disease Neuroimaging Initiative) database. The goal was to achieve good classification performances between Alzheimer’s Disease diagnosed patients (AD) and Normal Control (NC) subjects, while retaining information on which features were the most informative decision-wise. In our work, the information about topological properties contained in ADNI2 functional correlation matrices are extracted by modeling an estimated Wishart distribution for the expected diagnostical groups AD and NC, and allowed a complete separation between the two groups.

The method has also been tested on a variety of neurological related data, such as EEG time series from public datasets, yielding classification accuracy results with trivial classifiers comparable with benchmarking result obtained with dataset specific fine-tuned classifiers, thus underlining a certain general consistency of the method.

Speaker: Dr Carlo Mengucci (Università di Bologna)

11:50 → 12:15 Complex Human Interactions in MEdical Records and Atlases Network - the CHIMERA Project

The increasing availability of large-scale biomedical literature under the form of public on-line databases, has opened the door to a whole new understanding of multi-level associations between genomics, protein interactions and metabolic pathways for human diseases via network approaches.
Many structures and resources aiming at such type of analyses have been built, with the purpose of disentangling the complex relationships between various aspects of the human system relating to diseases.
In our work we mined large-scale public on-line databases to construct a multi-layer network using human diseases as its core.
Using web scraping for data extraction and a new string standardizer preprocessing pipeline based on synonyms mapping and stemming, we merged many kinds of bio-medical databases in a single network-of-networks structure. Informations about disease, metabolites, drugs and other biomedical significant data were merged in a single database.
A characterization of the structure will be discussed using centrality measures and some of significant queries-subnetworks will be presented. The aim of such network structure is to unveil hidden relationship by the underwhelming overlap between single-type information across different databases.

Speaker: Dr Nico Curti (Università di Bologna)

12:15 → 12:40 Statistical and dynamical properties of the bike mobility

The human mobility is a fruitful research field in the framework of Complex Systems to study the existence of universal statistical laws that could be related to microscopic individual behavior. The main question concerns the possibility of developing a Statistical Mechanics for understanding the empirical observations at macroscopic level. Planning urban mobility is one of the key issues for the life quality and the sustainability of the environmental impact of the future cities. The development of a multimodal mobility is one of the possible solutions, which requires the integration of different transportation networks in order to satisfy the complex mobility demand and the constraints imposed by the urban structure.
The bike mobility could play a fundamental role in this framework. The Bellamossa initiative of the Bologna Municipality allows to collect detailed georeferenced information on single trips performed by cyclists in the urban area of Bologna. Bellamossa is a game driven by an app on smartphones, which records the time and GPS positions every 2 sec. of each trips declared by a user according to different mobility categories: we focus our analysis on pedestrian and bike mobility. The number of participants during 2017 was 10^4 individuals, that perform 1500 trips per day using the bicycle from April to September. Each trip is anonymized so that it is difficult to follow the mobility of single individuals during a day. After reconstructing the trajectories on the road network of Bologna, we consider the problem of inferring the individual mobility demand that is satisfied by the bicycle and characterizing the dynamical features of the observed paths. We detect the mobility demand associated to the main attraction areas and the circadian rhythms of the city and the mobility subnetwork associated to the preferred paths of cyclists. We extend to the bike mobility the methodologies we have developed to study the private car mobility in order to prove the existence of a hazard function that measure the perceived convenience to use the bicycle and the difference dynamical features of the observed paths (i.e. the information entropy) according to their length and to the different origin-destinatio

Speaker: Chiara Mizzi (Università di Bologna)

12:40 → 13:00 Goodbye!