Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911937266188288 |
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| author | Choy, Keilung Xie, Wei |
| author_facet | Choy, Keilung Xie, Wei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_04011 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network Choy, Keilung Xie, Wei Molecular Networks Machine Learning 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. |
| title | Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network |
| topic | Molecular Networks Machine Learning |
| url | https://arxiv.org/abs/2405.04011 |