A rapid, versatile method of protein expression and screening can greatly facilitate the future development of therapeutic biologics, proteomic drug targets and biocatalysts. An attractive candidate is cell-free protein synthesis (CFPS), a cell-lysate-based in vitro expression system, which can utilize linear DNA as expression templates, bypassing time-consuming cloning steps of plasmid-based methods. Traditionally, such linear DNA expression templates (LET) have been vulnerable to degradation by nucleases present in the cell lysate, leading to lower yields. This challenge has been significantly addressed in the recent past, propelling LET-based CFPS as a useful tool for studying, screening and engineering proteins in a high-throughput manner. Currently, LET-based CFPS has promise in fields such as functional proteomics, protein microarrays, and the optimization of complex biological systems.
Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building contextspecific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB tool
The control of nutrient availability is critical to large‐scale manufacturing of biotherapeutics. However, the quantification of proteinogenic amino acids is time‐consuming and thus is difficult to implement for real‐time in situ bioprocess control. Genome‐scale metabolic models describe the metabolic conversion from media nutrients to proliferation and recombinant protein production, and therefore are a promising platform for in silico monitoring and prediction of amino acid concentrations. This potential has not been realized due to unresolved challenges: (1) the models assume an optimal and highly efficient metabolism, and therefore tend to underestimate amino acid consumption, and (2) the models assume a steady state, and therefore have a short forecast range. We address these challenges by integrating machine learning with the metabolic models. Through this we demonstrate accurate and time‐course dependent prediction of individual amino acid concentration in culture medium throughout the production process. Thus, these models can be deployed to control nutrient feeding to avoid premature nutrient depletion or provide early predictions of failed bioreactor runs.
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