# Towards Probabilistic Formal Modeling of Robotic Cell Injection Systems

## TL;DR

This paper introduces a probabilistic formal modeling approach using model checking to analyze robotic cell injection systems, addressing uncertainties and uncovering issues missed by traditional simulation methods.

## Contribution

It presents a novel probabilistic formal analysis methodology for robotic cell injection systems, improving failure detection over traditional simulation-based approaches.

## Key findings

- Discovered a discrepancy in the impedance force control algorithm
- Demonstrated the effectiveness of probabilistic model checking in failure analysis
- Addressed uncertainties ignored in previous analyses

## Abstract

Cell injection is a technique in the domain of biological cell micro-manipulation for the delivery of small volumes of samples into the suspended or adherent cells. It has been widely applied in various areas, such as gene injection, in-vitro fertilization (IVF), intracytoplasmic sperm injection (ISCI) and drug development. However, the existing manual and semi-automated cell injection systems require lengthy training and suffer from high probability of contamination and low success rate. In the recently introduced fully automated cell injection systems, the injection force plays a vital role in the success of the process since even a tiny excessive force can destroy the membrane or tissue of the biological cell. Traditionally, the force control algorithms are analyzed using simulation, which is inherently non-exhaustive and incomplete in terms of detecting system failures. Moreover, the uncertainties in the system are generally ignored in the analysis. To overcome these limitations, we present a formal analysis methodology based on probabilistic model checking to analyze a robotic cell injection system utilizing the impedance force control algorithm. The proposed methodology, developed using the PRISM model checker, allowed to find a discrepancy in the algorithm, which was not found by any of the previous analysis using the traditional methods.