Deciphering the generating rules and functionalities of complex networks

Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution.

Inferring Excitatory and Inhibitory Connections in Neuronal Networks

The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.

Molecular Dynamics of SARS-CoV-2 Delta Variant Receptor Binding Domain in Complex with ACE2 Receptor

COVID19 pandemic has disrupted our lives since December 2019 causing millions of infections and deaths worldwide. After more than a year we have vaccines that are effective in preventing the disease even though we are far from finished to vaccinate most of the population. Certain countries are doing better vaccinating people while others are far behind and if that is not enough new variants have appeared that put at risk our progress on defeating COVID19. The virus SARS-CoV-2 is mutating and many mutations change the spike glycoprotein which binds to the human receptor ACE2 sometimes making the virus more infectious and able to evade immunity. One virus variant of concern (VoC) is the one called delta which is becoming prevalent very quickly among new infections. The delta variant is a real threat for many people that are not vaccinated. Here I present molecular dynamics of the receptor binding domain in complex with its receptor ACE2 to shed light on the structural interactions that make this variant more dangerous.