A rule-based model of insulin signalling pathway

BACKGROUND: The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as “combinatorial complexity”, which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions. RESULTS: In this work, we present a comprehensive model of the ISP, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways. After RBM specification, we simulated the dynamic behaviour of the ISP model and validated it using experimental data. We the examined the predicted profiles of all the active species and clustered them in four clusters according to their dynamic behaviour. Finally, we used parametric sensitivity analysis to show the role of negative feedback loops in controlling the robustness of the system. CONCLUSIONS: The presented ISP model is a powerful tool for data simulation and can be used in combination with experimental approaches to guide the experimental design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to Biomodels Database (https://www.ebi.ac.uk/biomodels-main/# MODEL 1604100005). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0281-4) contains supplementary material, which is available to authorized users

Correction to: Joint X-ray/NMR structure refinement of multidomain/multisubunit systems

The article “Joint X-ray/NMR structure refinement of multidomain/multisubunit systems” written by “Azzurra Carlon, Enrico Ravera, Giacomo Parigi, Garib N. Murshudov and Claudio Luchinat” was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 11 October 2018 without open access

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Esperimenti per la misura di campi elettrici in cervello di ratto

Lo scopo di questo lavoro è validare un modello a elementi finiti che simula i campi elettrici presenti all'interno del tessuto cerebrale di ratto quando è applicata una tensione sinusoidale a 4 MHz alla scatola cranica tramite degli elettrodi. Per fare questo è necessario confrontare i risultati ottenuti con questo modello con quelli trovati mediante misura diretta in vivo su ratto. L'obiettivo che si propone di raggiungere, quindi, è quello di progettare un sistema di misura per misurare la tensione presente all'interno del tessuto cerebrale di un ratto quando viene applicata una differenza di potenziale sinusoidale al cranio. La frequenza a cui si vogliono effettuare le misure, ovvero 4 MHz, è maggiore della frequenza di funzionamento dei sistemi di acquisizione solitamente utilizzati per questa tipologia di prove e perciò sarà necessario caratterizzare la strumentazione utilizzata durante l'esperiment

Modeling and simulation of insulin signaling

In questa tesi è stato implementato un modello computazionale del signaling dell'insulina. In particolare, è stato utilizzato l'approccio del rule-based modeling che ci ha permesso di descrivere sistemi biologici complessi mediante un insieme di trasformazioni tra specie chimiche. In seguito, le predizioni ottenute sono state utilizzate per la caratterizzazione dinamica del sistema e per interpretare alcuni dei suoi meccanismi di regolazione, quali per esempio quello che coinvolge IRS-1