# Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network

arXiv: https://arxiv.org/abs/2405.04011 · HTML: https://mlsanity.com/paper/2405.04011/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 2 of 2)

- [Scalable Gradients for Stochastic Differential Equations](https://mlsanity.com/paper/2001.01328/) (2020)
- [Interpretable Biomanufacturing Process Risk and Sensitivity Analyses for Quality-by-Design and Stability Control](https://mlsanity.com/paper/1909.04261/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example](https://mlsanity.com/paper/2505.03177/) (2025)

## Abstract (arXiv)

Motivated by the pressing challenges in the digital twin development for biomanufacturing systems, we introduce an adjoint sensitivity analysis (SA) approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic stochastic reaction networks representing a multi-scale bioprocess mechanistic model that allows us to integrate disparate data from diverse production processes and leverage the information from existing macro-kinetic and genome-scale models. To support forward prediction and backward reasoning, we develop a convergent adjoint SA algorithm studying how the perturbations of model parameters and inputs (e.g., initial state) propagate through enzymatic reaction networks and impact on output trajectory predictions. This SA can provide a sample efficient and interpretable way to assess the sensitivities between inputs and outputs accounting for their causal dependencies. Our empirical study underscores the resilience of these sensitivities and illuminates a deeper comprehension of the regulatory mechanisms behind bioprocess through sensitivities.