Statistical Model Checking for Biological Applications

TL;DR

This paper surveys recent applications of statistical model checking in biology, covering stochastic and differential models, verification, and parameter synthesis, highlighting current methods and open challenges.

Contribution

It provides a comprehensive overview of statistical model checking techniques applied to biological systems, emphasizing recent advances and future research directions.

Findings

Overview of biochemical reaction modeling and Gillespie algorithm

Survey of verification and parameter synthesis in biological models

Identification of open problems and future research directions

Abstract

In this paper we survey recent work on the use of statistical model checking techniques for biological applications. We begin with an overview of the basic modelling techniques for biochemical reactions and their corresponding stochastic simulation algorithm - the Gillespie algorithm. We continue by giving a brief description of the relation between stochastic models and continuous (ordinary differential equation) models. Next we present a literature survey, divided in two general areas. In the first area we focus on works addressing verification of biological models, while in the second area we focus on papers tackling the parameter synthesis problem. We conclude with some open problems and directions for further research.