This study investigates the application of generative models for synthetic data generation in pathway optimization experiments within the field of metabolic engineering. Conditional Variational Autoencoders (CVAEs) use neural networks and latent variable distributions to generate new, plausible data samples. We adapt this model by conditioning the training process on the target flux to acquire increased performance.

Additionally, a baseline model, namely Probabilistic Principal Component Analysis (PPCA), was selected for a comparative analysis to generate the underlying latent space to test the hypothesis that a type of Variational Autoencoder (VAE) can be used to learn a reduced-dimensional latent space for configurations of a kinetic pathway model. A dataset comprising 5000 hypothetical configurations of a kinetic pathway model was utilized to extract relationships between elements of a kinetic pathway.

The results indicate that PPCA can model the underlying distribution of the dataset when the latent space is large enough. However, the traditional CVAE might struggle to capture the underlying distribution, resulting in an entangled latent space. The study suggests that an implementation of $\beta$-CVAE could lead to a better balance between parts of the objective function during training, offering improved prospects for generating cost-efficient kinetic pathways for combinatorial pathway optimization experiments.

This research explores the landscape of dataset generation through the lens of Probabilistic Principal Component Analysis (PPCA) and β-Conditional Variational Auto-encoder (β-CVAE) models. We conduct a comparative analysis of their respective capabilities in reproducing datasets that mirror the distribution of the original data that comes from a hypothetical pathway kinetic model based on an E.coli strain using varied parameter settings falling within a specified range. The requirement of significant prior investment in acquiring accurate details about the distinct mechanisms governing each reaction and its parameters for the construction of these kinetic models push us to find alternative ways to generate data that guide metabolic engineering processes. This paper tries to find a viable option through compression algorithms that reduce dimensionality. The PPCA model demonstrates commendable fidelity in capturing overarching patterns, though areas for refinement in reproducing specific data points are identified. In contrast, the β-CVAE model exhibits higher fidelity, precision, and consistency, positioning it as a robust choice for data generation tasks. This study was constrained by both time and the specificity of the model architectures and the dataset. These limitations underscore the imperative for continual exploration and refinement within the dynamic landscape of generative modeling. Opportunities could be found in the refinement of both VAE, CVAE and β-CVAE models utilizing varied hyperparameters alongside different architectures, to increase applicability across diverse datasets within the realm of metabolic engineering.

This research investigates the application of Generative Adversarial Networks (GANs) and probabilistic Principal Component Analysis (PPCA) in generating synthetic data for pathway optimization in metabolic engineering. The study aims to compare the performance of these generative models, addressing key questions regarding their utilization, the quality of generated data compared to experimental data, and overall efficiency. The dataset comprises 5000 parameter configurations of kinetic models that simulate a hypothetical pathway. Constructing kinetic models traditionally involves obtaining complex scientific knowledge, a process that may be alleviated through a data-driven approach. Results indicate that both models, tried with different sizes of latent space, demonstrate good performance in modeling the underlying latent space of the data. However, GANs with the right set of parameters exhibit a better performance, evidenced by lower KL divergence and superior visual structure in the generated data. The findings highlight the potential of GANs to outperform probabilistic PCA, offering valuable insights for more cost-effective and streamlined strain optimization in metabolic engineering. Overall, this research advocates for further investigation of GANs capabilities in metabolic engineering as a potentially powerful tool for synthetic data generation.

Metabolic engineering is an important field in biotechnology, aimed at optimizing cellular processes to produce desired compounds. In this thesis, we focus on predicting the metabolome from the proteome, as understanding this relationship is crucial for understanding cellular metabolism. We investigate the usage of additional biological information like protein-protein interactions and cellular stoichiometry to improve the predictive performance of metabolome prediction models. We also employ explanation algorithms to gain key insights into the regulatory processes of a yeast cell.

We demonstrate the effectiveness of our approach by predicting the metabolic fold-change of multiple yeast kinase knockouts. Our results show that incorporating additional biological information does not significantly improve the accuracy of the metabolome prediction models. Furthermore, we identified enzymes that are relevant for all metabolites used in this study, which indicates the existence of a global set of regulatory enzymes.

Overall, our study shows that through careful manipulation of the limit amount of data decent performance can be expected when predicting the metabolome. We apply a broad spectrum of machine learning algorithms to identify optimal model architecture. The methods and insights presented in this thesis could be used for creating a general pipeline for predicting a broad spectrum of metabolites from the proteome.