# Intervention Pathway Discovery via Context-Dependent Dynamic Sensitivity Analysis

## TL;DR

This paper introduces a comprehensive framework for analyzing the sensitivity of biological networks, combining static and dynamic methods to identify key intervention pathways and influential elements in context-dependent scenarios.

## Contribution

The paper presents a novel framework that integrates static and dynamic sensitivity analysis for discrete biological network models, enabling context-aware identification of intervention pathways.

## Key findings

- Dynamic analysis captures context-dependent transient states.
- Framework effectively identifies influential pathways for interventions.
- Application to T cell differentiation demonstrates practical utility.

## Abstract

The sensitivity analysis of biological system models can significantly contribute to identifying and explaining influences of internal or external changes on model and its elements. We propose here a comprehensive framework to study sensitivity of intra-cellular networks and to identify key intervention pathways, by performing both static and dynamic sensitivity analysis. While the static sensitivity analysis focuses on the impact of network topology and update functions, the dynamic analysis accounts for context-dependent transient state distributions. To study sensitivity, we use discrete models, where each element is represented as a discrete variable and assigned an update rule, which is a function of element's known direct and indirect regulators. Our sensitivity analysis framework allows for assessing the effect of context on individual element sensitivity, as well as on element criticality in reaching preferred outcomes. The framework also enables discovery of most influential pathways in the model that are essential for satisfying important system properties, and thus, could be used for interventions. We discuss the role of nine different network attributes in identifying key elements and intervention pathways, and evaluate their performance using model checking method. Finally, we apply our methods on the model of naive T cell differentiation, and further demonstrate the importance of context-based sensitivity analysis in identifying most influential elements and pathways.