Integrated Structural and Probabilistic Approaches for Biological and Epidemiological Systems

Introduction

Systems in nature are extremely robust, despite huge uncertainties and variability. Studying their nonlinear dynamic behaviour is challenging due to their complexity and the many parameters at play, but it is crucial to understand important phenomena, such as cellular dynamics, onset of diseases, and epidemic spreading.

Parameter-Dependent Simulations

Parameter-dependent simulations can predict the behaviour of natural systems case by case. Yet, the exact models and parameter values are poorly known, while qualitative behaviours are often preserved even with huge parameter variations. This preservation occurs because they rely on the system interconnection structure.

Structural Approaches

Parameter-free structural approaches can check whether a property is preserved for a whole family of uncertain systems exclusively due to its structure. However, when an expected property fails to hold structurally, novel approaches are needed to understand:

1. Why the property fails to hold
2. Which system features prevent it
3. Which key parameters must be finely tuned to enforce it

Project Overview

INSPIRE will develop a unifying framework to analyse and control families of uncertain dynamical systems in biology and epidemiology. This framework will integrate, for the first time, structural, robust, and probabilistic methods tailored to the peculiarities of natural systems.

Project Deliverables

The project will provide:

i) Methodologies to assess (practically) structural properties and unveil the mechanisms that enable or prevent a property, identifying the key parameters or motifs.

ii) Control paradigms that leverage such insights to guarantee a desired global property through targeted local interventions.

iii) Scaling and aggregation approaches that exploit the properties of subsystems to mitigate computational complexity.

Expected Outcomes

The project outcomes will include a mathematical theory as well as algorithms to analyse and control complex uncertain systems in nature. These outcomes will strongly support:

• The analysis and design of biomolecular feedback systems with a desired behaviour
• The identification of therapeutic targets
• The prediction and control of epidemic phenomena