In the last 10 years, an increasing number of studies employed -omics technologies to describe the molecular changes underpinning cellular functions. However, a large part of these findings is awaiting an experimental validation. This is due to the lack of efficient molecular tools able to control both multiple and distinct genetic interactions. Recently, I described a new CRISPR/12-based genome engineering tool that, for the first time, it provides the constitutive, conditional, inducible, orthogonal and multiplexed engineering of dozens of endogenous genes, simultaneously. In this research proposal, I aim to increase the efficiency of this platform in the context of multiplexed genome engineering applications, such as gene network rewiring. To this aim, I will use techniques inspired by the statistical physics of complex disordered systems, to design a more potent version of my CRISPR/Cas12-based genome engineering tool and, I will use this novel platform to rewire signaling pathways involved in cellular proliferation. This research proposal is structured into the following tasks: rational design of novel Cas12a variants, validation of novel Cas12a variants, gene network rewiring by a novel Cas12a variant. By coupling statistical physics to both protein and genome engineering this project will pave the way for efficient and large-scale engineering of gene networks.
Statistical Inference via Belief Propagation for Dynamical Models of Epidemics, (2015-2017) - Personale interno di riferimento
Ricerca da Enti privati e Fondazioni
Networks have been used to model infectious disease spreading in different forms. In general, the nodes represent single individuals and the edges describe the social contacts or sexual relationships through which the infection can spread. The detailed structure and temporal evolution of contact networks were considered for longtime almost inaccessible, therefore network epidemiology has mostly focused on the analysis of compartment models in the hypothesis of full mixture or on generalized random graphs characterized by a small set of global statistical measures, such as the degree distribution, level of clustering and community structure.However, detailed spatial and temporal data on social contacts are becoming increasingly accessible because of the diffusion of online social networks on mobile phones and through experiments with radio-frequency identification devices (RFID). In this context, an observation (even partial or noisy) of the infection state ofindividuals can in principle provide rich information that can be used for health-related purposes, such as the determination of infectivity parameters, the identification of the source of the outbreak or a prediction of future spread development. Once a model has been selected to describe the contagion process, the extraction of this information can be posed as a parametric Bayesian problem where the interesting quantities can be thought as either posteriors, maximum likelihood or maximum a posteriori points. Unfortunately, these values are computationally hard to calculate exactly. We propose to leverage techniques from Statistical Physics to computethese quantities approximately. Such development will provide efficient algorithmic tools, derived from the Cavity Method of Statistical Physics and its single instance counterpart, known as Belief Propagation, for epidemic inference and prediction that could prove to be invaluable for strategic use of resources when fightinglarge epidemics.
Academic Collaboration Agreement tra il Politecnico di Torino – DISAT e il Centro de Immunologia Molecular (CIM) di Cuba, (data sconosciuta-data sconosciuta) - Responsabile Scientifico
Centro de Inmunologìa Molecular
Convenzione dipartimentale tra HiFiBio e DISAT per una collaborazione in attività di ricerca nel settore della scienza e dell’ingegneria, con riferimento alle aree tematiche legate ai campi della “Prediction of antibody binding affinity” modellando repertori di anticorpi arricchiti con legante , (data sconosciuta-data sconosciuta) - Responsabile Scientifico
“Proteolabio” avente ad oggetto un algoritmo di ottimizzazione per lo screening di proteine ad alto rendimento , (2021-2022) - Responsabile Scientifico