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Abstract:The success of biopharmaceuticals as highly effective clinical drugs has 12 recently led industrial biotechnology towards their large-scale production. The 13 ovary cells of the Chinese hamster (CHO cells) are one of the most common 14 production cell line. However, they are very inefficient in producing desired 15compounds. This limitation can be tackled by culture bioengineering, but 16 identifying the optimal interventions is usually expensive and time-consuming. In 17 this study, we combined machine learning techniques with metabolic modelling 18 to estimate lactate production in CHO cell cultures. We trained our poly-omics 19 method using gene expression data from varying conditions and associated 20 reaction rates in metabolic pathways, reconstructed in silico. The poly-omics 21 reconstruction is performed by generating a set of condition-specific metabolic 22 models, specifically optimised for lactate export estimation. To validate our 23 approach, we compared predicted lactate production with experimentally 24 measured yields in a cross-validation setting. Importantly, we observe that 25 integration of metabolic predictions significantly improves the predictive ability 26 of our machine learning pipeline when compared to the same pipeline based on 27 gene expression alone. Our results suggest that, compared to transcriptomic-only 28 studies, combining metabolic modelling with data-driven methods vastly 29 improves the automatisation of cultures design, by accurately identifying optimal 30 growth conditions for producing target therapeutic compounds. 31

Quantum error correction schemes have gained a lot of attention in recent years. This is due to the emergence of small scale quantum devices that make use of superconducting qubits. However these devices are noisy and prone to quantum decoherence and thus errors. Along with quantum error correction there has been a push for new schemes in quantum error mitigation that take a more passive approach in eliminating readout errors. In this research we introduce a software method for quantum error mitigation that maps virtual qubits in a circuit to physical qubits with the least error. The method developed was tested on 9 IBM quantum devices. Results in the study have shown the method can reduce readout errors by up to 35.52%.

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Genome-Scale metabolic models have proven to be incredibly useful.Allowing researchers to model cellular functionality based upon gene expression. However as the number of genes and reactions increases it can become computationally demanding. The first step in genome-scale metabolic modelling is to model the relationship between genes and reactions in the form of Gene-Protein-Reaction Associations (GPRA). In this research we have developed a way to model GPRAs on an Altera Cyclone II FPGA using Quartus II programmable logic device design software and the VHDL hardware description language. The model consisting of 7 genes and 7 reactions was implemented using 7 combinational functions and 14 I/O pins. This model will be the first step towards creating a full genome scale metabolic model on FPGA devices which we will be fully investigating in future studies.