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Deep hybrid modeling of a HEK293 process: combining Long Short-Term Memory (LSTM) networks with first principles equations
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  • João R. C. Ramos,
  • José Pinto,
  • Gil Poiares-Oliveira,
  • Ludovic Peeters,
  • Patrick Dumas,
  • Rui Oliveira
João R. C. Ramos
Universidade NOVA de Lisboa Faculdade de Ciencias e Tecnologia
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José Pinto
Universidade NOVA de Lisboa Faculdade de Ciencias e Tecnologia
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Gil Poiares-Oliveira
Universidade NOVA de Lisboa Faculdade de Ciencias e Tecnologia
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Ludovic Peeters
GlaxoSmithKline Biologicals SA Rixensart
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Patrick Dumas
GlaxoSmithKline Biologicals SA Rixensart
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Rui Oliveira
Universidade NOVA de Lisboa Faculdade de Ciencias e Tecnologia

Corresponding Author:[email protected]

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Abstract

In this paper, Long Short-Term Memory (LSTM) networks and multilayered feedforward neural networks (FFNNs) were combined with first principles equations in a hybrid workflow to describe human embryonic kidney 293 (HEK293) culture dynamics. Experimental data of 27 extracellular state variables in 20 fed-batch HEK293 cultures were collected in a parallel high throughput 250 mL cultivation system. The adaptive moment estimation method (ADAM) with stochastic regularization and cross-validation were employed for deep learning. A total of 784 hybrid models with varying deep neural network (DNN) architectures, depths, layers sizes and node activation functions were compared. In most scenarios, hybrid LSTM models outperformed hybrid FFNN models in terms of training and testing error. Hybrid LSTM models revealed to be less sensitive to data resampling than FFNN hybrid models. As disadvantages, Hybrid LSTM models are in general more complex (higher number of parameters) and have a higher computation cost than FFNN hybrid models. The hybrid model with the highest prediction accuracy consisted in a LSTM network with 7 internal states connected in series with dynamic material balance equations. This hybrid model correctly predicted the dynamics of the 27 state variables (R 2=0.93 in the test data set), including biomass, key substrates, amino acids and metabolic by-products for around 10 cultivation days.
22 Aug 2023Submitted to Biotechnology and Bioengineering
23 Aug 2023Submission Checks Completed
23 Aug 2023Assigned to Editor
23 Aug 2023Review(s) Completed, Editorial Evaluation Pending
29 Sep 2023Reviewer(s) Assigned