Giulia Simoni
The Microsoft Research - University of Trento Centre for Computational and Systems Biology
9 Papers
15 Citations
Giulia Simoni is an academic researcher from The Microsoft Research - University of Trento Centre for Computational and Systems Biology. The author has contributed to research in topics: Context (language use) & Modelling biological systems. The author has an hindex of 4, co-authored 9 publications.
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Papers
A Retrospective Analysis of the COVID-19 Pandemic Evolution in Italy
Anna Fochesato,Giulia Simoni,Federico Reali,Giulia Giordano,Enrico Domenici,Enrico Domenici,Luca Marchetti +6 more
TL;DR: A retrospective analysis of the Italian epidemic evolution up to mid-December 2020 is performed to gain insight into the main characteristics of the original strain of SARS-CoV-2, prior to the emergence of new mutations and the vaccination campaign.
10
A comparison of deterministic and stochastic approaches for sensitivity analysis in computational systems biology
TL;DR: The performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems).
Integrated quantitative systems pharmacology (QSP) model of lysosomal diseases provides an innovative computational platform to support research and therapeutic development for the sphingolipidoses
Chanchala D. Kaddi,Federico Reali,Luca Marchetti,Bradley Niesner,Silvia Parolo,Giulia Simoni,Susana Zaph,Mengdi Tao,Ruth E. Abrams,Zachary van Rijn,John P. Leonard,M. Judith Peterschmitt,Ana Cristina Puga,Kevin Mange,Jeffrey S. Barrett,Corrado Priami,Edward H. Schuchman,Karim Azer +17 more
7
A robust computational pipeline for model-based and data-driven phenotype clustering
Giulia Simoni,Chanchala D. Kaddi,Mengdi Tao,Federico Reali,Danilo Tomasoni,Corrado Priami,Corrado Priami,Karim Azer,Susana Neves-Zaph,Luca Marchetti +9 more
TL;DR: An innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology is defined that is accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show.
3
QSPcc reduces bottlenecks in computational model simulations.
Danilo Tomasoni,Alessio Paris,Stefano Giampiccolo,Federico Reali,Giulia Simoni,Luca Marchetti,Chanchala D. Kaddi,Susana Neves-Zaph,Corrado Priami,Corrado Priami,Karim Azer,Rosario Lombardo +11 more
- 01 Sep 2021
TL;DR: Lombardo et al. as mentioned in this paper presented a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code.