Network approaches to Genome-Wide Association studies

In the framework of large-scale genotypic studies (describing the distribution of allele frequencies inside human genome) we characterize the Linkage Disequilibrium (LD) matrix as a network of relationships between alleles. We propose a suitable matrix discretization threshold, after a characterization of the distribution of noisy values inside LD matrix. We compare the main network parameters of a real LD matrix with two null models (Erdos-Renyi random network and a rewiring of the original network), in order to highlight the peculiar features of the LD network. We conclude stating the need of adequate computing tools for handling the high-dimensional data coming from Genome-Wide genotyping datasets

Large-scale modelling of neuronal systems

The brain is, without any doubt, the most complex system of the human body. Its complexity is also due to the extremely high number of neurons, as well as the huge number of synapses connecting them. Each neuron is capable to perform complex tasks, like learning and memorizing a large class of patterns. The simulation of large neuronal systems is challenging for both technological and computational reasons, and can open new perspectives for the comprehension of brain functioning. A well-known and widely accepted model of bidirectional synaptic plasticity, the BCM model, is stated by a differential equation approach based on bistability and selectivity properties. We have modified the BCM model extending it from a single-neuron to a whole-network model. This new model is capable to generate interesting network topologies starting from a small number of local parameters, describing the interaction between incoming and outgoing links from each neuron. We have characterized this model in terms of complex network theory, showing how this learning rule can be a support for network generation

Incentives and Implementation in Marriage Markets with Externalities

We study the implementability of stable correspondences in marriage markets with externalities. We prove that, contrary to what happens in markets without externalities, no stable revelation mechanism makes a dominant strategy for the agents on one side of the market to reveal their preferences. However, the stable correspondence is implementable in Nash equilibrium

Network approaches to Genome-Wide association studies

In the framework of large-scale genotypic studies (describing the distribution of allele frequencies inside human genome) we characterize the Linkage Disequilibrium (LD) matrix as a network of relationships between alleles. We propose a suitable matrix discretization threshold, after a characterization of the distribution of noisy values inside LD matrix. We compare the main network parameters of a real LD matrix with two null models (Erdos-Renyi random network and a rewiring of the original network), in order to highlight the peculiar features of the LD network. We conclude stating the need of adequate computing tools for handling the high-dimensional data coming from Genome-Wide genotyping datasets

Network approaches to Genome-Wide Association studies

In the framework of large-scale genotypic studies (describing the distribution of allele frequencies inside human genome) we characterize the Linkage Disequilibrium (LD) matrix as a network of relationships between alleles. We propose a suitable matrix discretization threshold, after a characterization of the distribution of noisy values inside LD matrix. We compare the main network parameters of a real LD matrix with two null models (Erdos-Renyi random network and a rewiring of the original network), in order to highlight the peculiar features of the LD network. We conclude stating the need of adequate computing tools for handling the high-dimensional data coming from Genome-Wide genotyping datasets

Biophysical modelling of memory and learning by neuroinformatics and systems biology

Learning and memory mechanisms are currently studied at molecular level and theoretical approaches need to be extended by precise and detailed biophysical models for the comprehension of the existing results and for design of new experiments. Synaptic plasticity is, at least partially, based on the molecular transition between a synaptic state of permanent (long-term) potentiation (LTP) and depression (long-term depression, LTD). Phosphorylation and dephosphorylation of receptors mediating synaptic transmission is the major mechanisms for LTP/LTD induction and regulation. This knowledge has fuelled new theoretical and experimental investigations, mainly focused on the stability versus meta-stability properties as a function of the involved enzymes