MPC Antibody

Here’s a structured collection of FAQs tailored to academic research on MPC (Model Predictive Control) applications in monoclonal antibody (mAb) production, integrating experimental design and data analysis principles:

Advanced Research Questions

How do contradictions between MPC predictions and experimental data arise in perfusion processes?

Common sources include:

• Metabolic shifts: CHO cells alter nutrient uptake rates during prolonged culture (e.g., glutamine dependency → lactate consumption) .

• Sensor drift: pH or oxygen probes degrade over 14+ days, requiring recalibration .

Resolution workflow:

• Perform partial least squares (PLS) regression to isolate conflicting variables .

• Validate with offline assays (e.g., Nova Bioprofile for amino acids) .

What methodologies integrate multi-omics data into MPC frameworks?

• Transcriptomic integration: Use RNA-seq data to refine growth rate models via constraint-based reconstruction (e.g., CHO genome-scale metabolic models) .

• Proteomic alignment: Map intracellular antibody titers to mTOR pathway activity using ELISA/MS data .

How can MPC address scalability challenges from bench to pilot-scale bioreactors?

• Grey-box modeling: Combine first-principles mass balance equations with machine learning (e.g., LSTM networks) to predict gradients in 500L reactors .

• Edge computing: Deploy localized MPC nodes to mitigate latency in large-scale systems .

Methodological Guidance

• For FACS-based cell sorting: Use anti-BCMA antibodies (e.g., Vicky-1, MAB193) with APC/Cy7 fluorophores to distinguish high/low BCMA-expressing clones .

• For anti-drug antibody (ADA) assays: Validate bridging ELISA with ≥100 pre-dose samples to exclude false positives .