SIGNOR, a database of causal interactions (Perfetto et al, 2016, Lo Surdo et al, 2023, De Marinis et al, 2021). Since its establishment, SIGNOR has steadily grown in terms of data content, citations (> 300) and international collaborations and ranked first among ‘causal interaction’ resources, for protein coverage and number of interactions annotated (Turei et al, 2021). We implemented bioinformatics strategies to exploit SIGNOR data to dissect the mechanism of diseases (Lo Surdo et al, 2018, Iannuccelli et al 2020, Pugliese et al, 2021, Perfetto et al, 2021).

SignalingProfiler and mechanistic modelling, a computational strategy for multi-omics integration that combines phosphoproteomic and transcriptomic data with causal networks to generate context-specific mechanistic models of signal transduction. Application of SignalingProfiler to Acute Myeloid Leukemia cellular models revealed a new role for WEE1-CDK1 axis in mediating drug resistance in cell lines and patient-derived primary blast ( Venafra et al, NPJ Syst Biol Appl, 2024) (see also Massacci et al, 2023 and Pugliese et al, 2023).

Link github: https://github.com/SaccoPerfettoLab/SignalingProfiler

Logical Modelling, we have contributed to the development of single- and multi-scale dynamic modeling approaches, in the context of Acute Myeloid Leukemia (Palma et al, 2021). In a recent project, we demonstrated that data in SIGNOR could be exploited to understand the dynamics of the molecular mechanisms of diseases and to build tools for patient stratification and diagnosis in personalized medicine (Latini et al, 2024).

ProxPath, a graph-theory-based algorithm that uses causal information to functionally link proteins to cellular pathways and phenotypes. We have applied ProxPath to highlight the molecular mechanisms of convergence between the 'synaptic' and 'epigenetic' pathways in the context of autism-spectrum disorders (Iannuccelli et al, Molecular Psychiatry, 2024).

Link github: https://github.com/SaccoPerfettoLab/ProxPath