Paper List
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Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
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ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
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DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
Complex Adaptive Systems Laboratory, The Data Science Institute, University of Technology Sydney, NSW 2007, Australia | CSL Innovation, Melbourne, VIC 3000, Australia
The 30-Second View
IN SHORT: This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited historical batches, infrequent offline measurements (once/twice daily), heterogeneous process conditions, and high-dimensional Raman spectral inputs (3,325 wavenumbers).
Innovation (TL;DR)
- Methodology Systematic benchmarking of three ML strategies (Dimensionality Reduction, Just-In-Time Learning, Online Learning) specifically tailored for cold-start bioprocess monitoring across simulated and real industrial datasets.
- Methodology Identification of key meta-features (feed media composition, process control strategies) that significantly impact model transferability between heterogeneous bioreactor runs.
- Methodology Demonstration that integrating Raman-based real-time predictions with lagged offline measurements enhances monitoring accuracy, providing a hybrid approach to overcome infrequent feedback.
Key conclusions
- Batch learning methods (e.g., PLSR, SVR) perform well in homogeneous settings but struggle in cold-start scenarios, where Just-In-Time Learning (JITL) and Online Learning (OL) show superior adaptability with statistically significant improvements (p<0.05 in Friedman tests).
- Dimensionality Reduction is critical for handling high-dimensional Raman data (3,325 features vs. <30 samples), with supervised methods like PLSR outperforming unsupervised PCA when offline measurements are available.
- Model transferability depends heavily on process meta-features; feed media composition explains up to 40% of performance variance across runs, highlighting the need for context-aware training strategies.
Abstract: In cell culture bioprocessing, real-time batch process monitoring (BPM) refers to the continuous tracking and analysis of key process variables—such as viable cell density, nutrient levels, metabolite concentrations, and product titer—throughout the duration of a batch run. This enables early detection of deviations and supports timely control actions to ensure optimal cell growth and product quality. BPM plays a critical role in ensuring the quality and regulatory compliance of biopharmaceutical manufacturing processes. However, the development of accurate soft sensors for BPM is hindered by key challenges, including limited historical data, infrequent feedback, heterogeneous process conditions, and high-dimensional sensory inputs. This study presents a comprehensive benchmarking analysis of machine learning (ML) methods designed to address these challenges, with a focus on learning from historical data with limited volume and relevance in the context of bioprocess monitoring. We evaluate multiple ML approaches—including feature dimensionality reduction, online learning, and just-in-time learning—across three datasets, one in silico dataset and two real-world experimental datasets. Our findings highlight the importance of training strategies in handling limited data and feedback, with batch learning proving effective in homogeneous settings, while just-in-time learning and online learning demonstrate superior adaptability in cold-start scenarios. Additionally, we identify key meta-features, such as feed media composition and process control strategies, that significantly impact model transferability. The results also suggest that integrating Raman-based predictions with lagged offline measurements enhances monitoring accuracy, offering a promising direction for future bioprocess soft sensor development.