# Interpretable Biomanufacturing Process Risk and Sensitivity Analyses for Quality-by-Design and Stability Control

arXiv: https://arxiv.org/abs/1909.04261 · HTML: https://mlsanity.com/paper/1909.04261/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes](https://mlsanity.com/paper/2105.06543/) (2021)
- [Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review](https://mlsanity.com/paper/2506.12322/) (2025)
- [Biomanufacturing Harvest Optimization with Small Data](https://mlsanity.com/paper/2101.03735/) (2021)
- [Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control](https://mlsanity.com/paper/2205.02410/) (2022)
- [Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network](https://mlsanity.com/paper/2405.04011/) (2024)
- [Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control](https://mlsanity.com/paper/2201.03116/) (2022)
- [Sensitivity Analysis on Policy-Augmented Graphical Hybrid Models with Shapley Value Estimation](https://mlsanity.com/paper/2411.13396/) (2024)

## Abstract (arXiv)

While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and quality attributes (CPPs/CQAs), we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most "informative" data collection, and improve production stability.