Modeling Strategies for Computational Systems Biology

Cycle 32th Oral Defence of the Phd Thesis

March 20, 2020
Versione stampabile

Venue: Povo Zero, via Sommarive 14 (Povo) – Seminar Room “-1”
Time: 3.00 p.m.

The Oral defence will take place in telematics mode in accordance with the provisions of the Rector with email dated March 10, 2020, in order to limit the spread of the Coronavirus 2019-nCoV epidemic

  • Giulia Simoni - PhD in Mathematics

Mathematical models and their associated computer simulations are nowadays widely used in several research fields, such as natural sciences, engineering, as well as social sciences. In the context of systems biology, they provide a rigorous way to investigate how complex regulatory pathways are connected and how the disruption of these processes may contribute to the development of a disease, ultimately investigating the suitability of specific molecules as novel therapeutic targets. In the last decade, the launching of the precision medicine initiative has motivated the necessity to define innovative computational techniques that could be used for customizing therapies. Indeed, the combination of mathematical models and computer strategies is an essential tool for biologists, which can analyze complex system pathways, as well as for the pharmaceutical industry, which is involved in promoting programs for drug discovery. In this context, we explored different modeling strategies, such as model simulations, sensitivity analysis and quantitative systems pharmacology (QSP) models, to define a novel computational pipeline to perform unsupervised classification. The proposed methodology combines the experimental data connected to a population of patients with a mathematical model of the disease biology to identify the mechanistic causes of interpatient phenotype heterogeneity. We present the application to three test cases in different biological contexts: a whole-body model of dyslipidemia, a QSP model of Gaucher disease type 1 (GD1) and a QSP model of cardiac electrophysiology. In these test cases, the pipeline proved to be accurate and robust, allowing the interpretation of the mechanistic differences underlying the phenotype classification that traditional methods failed to identify. 

Supervisors: Corrado Priami – Luca Marchetti