Multivariate statistical data analysis of cell‐free protein synthesis toward monitoring and control

Mechanism-based and data-driven modeling in cell-free synthetic biology

Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.

Simultaneous spectrophotometric determination of Co (II) and Co (III) in acidic medium with partial least squares regression and artificial neural networks

This study aims at the application of two chemometric techniques to visible spectra of acetic acid solutions of Co (II) and Co (III) for simultaneous determination thereof. Spectral data of 145 samples in the range of 400-700 nm were used to build the models. Partial least squares regression models were developed for which latent variables were determined using internal cross-validation with a leave-one-out strategy and 3 and 2 latent variables were selected for Co(II) and Co(III) based on root mean square error of cross-validation. For these models, root mean square errors of prediction were 1.16 and 0.536 mM and coefficients of determination were 0.975 and 0.892 for Co (II) and Co (III). As an alternate method, artificial neural networks consisting of three layers, with 10 neurons in hidden layer, were trained to model spectra and concentrations of cobalt species. Levenberg-Marquardt algorithm with feed-forward back-propagation learning resulted root mean square errors of prediction of 0.316 and 0.346 mM for Co (II) and Co (III) respectively and coefficients of determination were 0.996 and 0.988.

Designing of an extract production protocol for industrial application of cell-free protein synthesis technology: Building from a current best practice to a quality by design approach

Cell-Free Protein Synthesis (CFPS) has, over the past decade, seen a substantial increase in interest from both academia and industry. Applications range from fundamental research, through high-throughput screening to niche manufacture of therapeutic products. This review/perspective focuses on Quality Control in CFPS. The importance and difficulty of measuring the Raw Material Attributes (RMAs) of whole cell extract, such as constituent protein and metabolite concentrations, and of understanding and controlling these complicated enzymatic reactions is explored, for both centralised and distributed industrial production of biotherapeutics. It is suggested that a robust cell-free extract production process should produce cell extract of consistent quality; however, demonstrating this is challenging without a full understanding of the RMAs and their interaction with reaction conditions and product. Lack of technology transfer and knowledge sharing is identified as a key limiting factor in the development of CFPS. The article draws upon the experiences of industrial process specialists, discussions within the Future Targeted Healthcare Manufacturing Hub Specialist Working Groups and evidence drawn from various sources to identify sources of process variation and to propose an initial guide towards systematisation of CFPS process development and reporting. These proposals include the development of small scale screening tools, consistent reporting of selected process parameters and analytics and application of industrial thinking and manufacturability to protocol development.

Biotechnology Applications of Cell-Free Expression Systems

Cell-free systems are a rapidly expanding platform technology with an important role in the engineering of biological systems. The key advantages that drive their broad adoption are increased efficiency, versatility, and low cost compared to in vivo systems. Traditionally, in vivo platforms have been used to synthesize novel and industrially relevant proteins and serve as a testbed for prototyping numerous biotechnologies such as genetic circuits and biosensors. Although in vivo platforms currently have many applications within biotechnology, they are hindered by time-constraining growth cycles, homeostatic considerations, and limited adaptability in production. Conversely, cell-free platforms are not hindered by constraints for supporting life and are therefore highly adaptable to a broad range of production and testing schemes. The advantages of cell-free platforms are being leveraged more commonly by the biotechnology community, and cell-free applications are expected to grow exponentially in the next decade. In this study, new and emerging applications of cell-free platforms, with a specific focus on cell-free protein synthesis (CFPS), will be examined. The current and near-future role of CFPS within metabolic engineering, prototyping, and biomanufacturing will be investigated as well as how the integration of machine learning is beneficial to these applications.