CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology

Exploiting unconventional prokaryotic hosts for industrial biotechnology

Developing cost-efficient biotechnological processes is a major challenge in replacing fossil-based industrial production processes. The remarkable progress in genetic engineering ensures efficient and fast tailoring of microbial metabolism for a wide range of bioconversions. However, improving intrinsic properties such as tolerance, handling, growth, and substrate consumption rates is still challenging. At the same time, synthetic biology tools are becoming easier applicable and transferable to nonmodel organisms. These trends have resulted in the exploitation of new and unconventional microbial systems with sophisticated properties, which render them promising hosts for the bio-based industry. Here, we highlight the metabolic and cellular capabilities of representative prokaryotic newcomers and discuss the potential and drawbacks of these hosts for industrial application.

Computer-Aided Strategies for Determining the Amino Acid Composition of Medium for Chinese Hamster Ovary Cell-Based Biomanufacturing Platforms

Chinese hamster ovary (CHO) cells are used for the production of the majority of biopharmaceutical drugs, and thus have remained the standard industry host for the past three decades. The amino acid composition of the medium plays a key role in commercial scale biologics manufacturing, as amino acids constitute the building blocks of both endogenous and heterologous proteins, are involved in metabolic and non-metabolic pathways, and can act as main sources of nitrogen and carbon under certain conditions. As biomanufactured proteins become increasingly complex, the adoption of model-based approaches become ever more popular in complementing the challenging task of medium development. The extensively studied amino acid metabolism is exceptionally suitable for such model-driven analyses, and although still limited in practice, the development of these strategies is gaining attention, particularly in this domain. This paper provides a review of recent efforts. We first provide an overview of the widely adopted practice, and move on to describe the model-driven approaches employed for the improvement and optimization of the external amino acid supply in light of cellular amino acid demand. We conclude by proposing the likely prevalent direction the field is heading towards, providing a critical evaluation of the current state and the future challenges and considerations.

A heuristic approach to handling missing data in biologics manufacturing databases

The biologics sector has amassed a wealth of data in the past three decades, in line with the bioprocess development and manufacturing guidelines, and analysis of these data with precision is expected to reveal behavioural patterns in cell populations that can be used for making predictions on how future culture processes might behave. The historical bioprocessing data likely comprise experiments conducted using different cell lines, to produce different products and may be years apart; the situation causing inter-batch variability and missing data points to human- and instrument-associated technical oversights. These unavoidable complications necessitate the introduction of a pre-processing step prior to data mining. This study investigated the efficiency of mean imputation and multivariate regression for filling in the missing information in historical bio-manufacturing datasets, and evaluated their performance by symbolic regression models and Bayesian non-parametric models in subsequent data processing. Mean substitution was shown to be a simple and efficient imputation method for relatively smooth, non-dynamical datasets, and regression imputation was effective whilst maintaining the existing standard deviation and shape of the distribution in dynamical datasets with less than 30% missing data. The nature of the missing information, whether Missing Completely At Random, Missing At Random or Missing Not At Random, emerged as the key feature for selecting the imputation method.

Transcriptional regulation of the genes involved in protein metabolism and processing in Saccharomyces cerevisiae

Topological analysis of large networks, which focus on a specific biological process or on related biological processes, where functional coherence exists among the interacting members, may provide a wealth of insight into cellular functionality. This work presents an unbiased systems approach to analyze genetic, transcriptional regulatory and physical interaction networks of yeast genes possessing such functional coherence to gain novel biological insight. The present analysis identified only a few transcriptional regulators amongst a large gene cohort associated with the protein metabolism and processing in yeast. These transcription factors are not functionally required for the maintenance of these tasks in growing cells. Rather, they are involved in rewiring gene transcription in response to such major challenges as starvation, hypoxia, DNA damage, heat shock or the accumulation of unfolded proteins. Indeed, only a subset of these proteins were captured empirically in the nuclear-enriched fraction of non-stressed yeast cells, suggesting that the transcriptional regulation of protein metabolism and processing in yeast is primarily concerned with maintaining cellular robustness in the face of threat by either internal or external stressors.

Process development for the continuous production of heterologous proteins by the industrial yeast, Komagataella phaffii

The current trend in industrial biotechnology is to move from batch or fed-batch fermentations to continuous operations. The success of this transition will require the development of genetically stable production strains, the use of strong constitutive promoters, and the development of new medium formulations that allow an appropriate balance between cell growth and product formation. We identified genes that showed high expression in Komagataella phaffii during different steady-state conditions and explored the utility of promoters of these genes (Chr1-4_0586 and FragB_0052) in optimizing the expression of two different r-proteins, human lysozyme (HuLy), and the anti-idiotypic antibody fragment, Fab-3H6, in comparison with the widely used glyceraldehyde-3-phosphate dehydrogenase promoter. Our results showed that the promoter strength was highly dependent on the cultivation conditions and thus constructs should be tested under a range of conditions to determine both the best performing clone and the ideal promoter for the expression of the protein of interest. An important benefit of continuous production is that it facilitates the use of the genome-scale metabolic models in the design of strains and cultivation media. In silico flux distributions showed that production of either protein increased the flux through aromatic amino acid biosynthesis. Tyrosine supplementation increased the productivity for both proteins, whereas tryptophan addition did not cause any significant change and, phenylalanine addition increased the expression of HuLy but decreased that of Fab-3H6. These results showed that a genome-scale metabolic model can be used to assess the metabolic burden imposed by the synthesis of a specific r-protein and then this information can be used to tailor a cultivation medium to increase production.