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Strituvad July Newsletter

During the last meeting in Bologna, partners from the University of Catania and University of Bologna worked together on the necessary steps to progress towards the Uncertainty Quantification and Technical Validation of UISS-TB based on ASME VV-40 standards. In this occasion, we asked Prof Francesco Pappalardo to talk about the UISS-TB platform. Pappalardo is Associate Professor of Computer Science and Vice Director of the Dept. of Drug and Health Science at the University of Catania. He holds a MS (2000) and a PhD (2004) in Computer Science and he is visiting Professor at Boston University. How did the idea of a Universal Immune System Simulator (UISS) come about? Over the last fifteen years, I have worked closely with life scientists and medical doctors to develop and implement computational models for applications in biological knowledge discovery and simulation of biomedical experiments. These interactions were fruitful and they resulted in improved understanding of the modeled systems. The models and simulators I worked on have helped in increasing the efficiency of experimentation and resulted in saving time, effort, and cost. What is your expertise and past experiences with In Silico Trials? I worked on the Universal Immune System Simulator, a complex and large modelling and simulation framework... Full interview on our blog

COMBINE group at University of Catania. From the left: Dr Giuseppe Alessandro Parasiliti Palumbo, Dr Giuseppe Sgroi, Dr Valentina Di Salvatore, Prof Francesco Pappalardo, Dr Giulia Russo

A three-day full immersion meeting in Bologna, Italy, for partners from University of Catania and University of Bologna to speed up both the development and regulatory work on UISS-TB, the computational framework that makes use of a multi-scale, multi-organ, three-dimensional agent based simulator of the immune system. Continue reading


COMBINE group releases the main core of UISS in silico platform as open source under Apache License v2.0

The paper "Verification of an agent-based disease model of human Mycobacterium tuberculosis infection", published on the International Journal for Numerical Methods in Biomedical Engineering, explains how Agent-based Model are a powerful class of computational models widely used to simulate complex phenomena. and focuses on the critical aspect of the model credibility assessment with a step-by-step procedure described in detail.

Position funded by In Silico World to work with COMBINE group on the topic "Agent Based Modeling and Machine Learning approaches for in silico trials development in Multiple Sclerosis"


6-10 Sept. 2021

2nd INTERNATIONAL SCHOOL ON IN SILICO TRIALS The school this year is organized by methods ranging from molecular dynamics to system biology, machine learning, computational fluid dynamics, finite element modelling, and agent-based modelling. It will cover also the use of high performance computing, and the challenges posed by multiphysics and multiscale models.

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