Engineering Of Biological Systems Research Articles

Progress in electrical impedance tomography and bioimpedance.

Progress in Electrical Impedance Tomography and Bioimpedance
 
Guest Editors
 
• Prof. Richard Bayford. Department of Natural Science, Middlesex University, London, UK.
• Prof. Rosalind Sadleir, School of Biological and Health Systems Engineering, Arizona State University, USA
• Prof. Inéz Frerichs, Department of Anaesthesiology and Intensive Care Medicine, University Medical Centre Schleswig-Holstein, Campus Kiel, Kiel, Germany.
• Prof. Tong In Oh, Department of Biomedical Engineering, Kyung Hee University, South Korea.
• Prof. Steffen Leonhardt, Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Germany.
 
Scope

This focus collection aims at presenting recent advances in electrical impedance tomography (EIT), including algorithms, hardware, and clinical applications. 

Editorial 

This focus collection of articles published by the journal Physiological Measurement introduces the Progress in Electrical Impedance Tomography (EIT) and Bioimpedance. It follows conferences in South Korea and Germany, that provided a platform for new research ideas.&#xD.

Twin Scholarships of Glycomedicine and Precision Medicine in Times of Single-Cell Multiomics.

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges persist, particularly in the simultaneous analysis of the glycome and transcriptome in single cells owing to technical limitations. In addition, holistic views of the complex molecular interactions between glycomics and other omics types remain elusive. We underscore and call for a paradigm shift toward the exploration of integrative glycan platforms and analysis methods for single-cell multiomics research and precision medicine biomarker discovery. The integration of multiple datasets from various single-cell omics levels represents a crucial application of systems biology in understanding complex cellular processes and is essential for advancing the twin scholarships of glycomedicine and precision medicine.

ProMENDA: an updated resource for proteomic and metabolomic characterization in depression

Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn.

Unlocking the Transformative Power of Synthetic Biology

Artificial Intelligence (AI) combined with Synthetic Biology has the potential to change the way we approach medicine, agriculture, and manufacturing. AI automates tasks, optimizes experimental designs, and predicts biological behaviours, resulting in more efficient design and engineering of biological systems. However, there are challenges such as data limitations, interpretability issues, and ethical considerations like biosafety and biosecurity concerns that need to be addressed. AI can be used to analyze vast amounts of data and identify patterns. This has led to successful applications of AI in high-throughput screening and biomanufacturing, which can drive innovation and address critical challenges. AI-powered closed-loop systems for real-time monitoring and control of biological processes also show promise in providing real-time feedback and optimizing systems on the fly. Despite these advancements, it's important to consider ethical implications to ensure the responsible development and application of AI in synthetic biology. Proper consideration of challenges and ethical considerations can help leverage the power of AI to drive innovation and tackle pressing societal challenges. Overall, the potential of AI in synthetic biology is significant. By addressing challenges and ethical considerations, we can use them effectively to solve pressing problems.

Development of Cell-Free Transcription-Translation Systems in Three Soil Pseudomonads.

In vitro transcription-translation (TX-TL) can enable faster engineering of biological systems. This speed-up can be significant, especially in difficult-to-transform chassis. This work shows the successful development of TX-TL systems using three soil-derived wild-type Pseudomonads known to promote plant growth: Pseudomonas synxantha, Pseudomonas chlororaphis, and Pseudomonas aureofaciens. All three species demonstrated multiple sonication, runoff, and salt conditions producing detectable protein synthesis. One of these new TX-TL systems, P. synxantha, demonstrated a maximum protein yield of 2.5 μM at 125 proteins per DNA template, a maximum protein synthesis rate of 20 nM/min, and a range of DNA concentrations with a linear correspondence with the resulting protein synthesis. A set of different constitutive promoters driving mNeonGreen expression were tested in TX-TL and integrated into the genome, showing similar normalized strengths for in vivo and in vitro fluorescence. This correspondence between the TX-TL-derived promoter strength and the in vivo promoter strength indicates that these lysate-based cell-free systems can be used to characterize and engineer biological parts without genomic integration, enabling a faster design-build-test cycle.

Oncolytic Viruses in the Era of Omics, Computational Technologies, and Modeling: Thesis, Antithesis, and Synthesis.

Oncolytic viruses (OVs) are the frontier therapy for refractory cancers, especially in integration with immunomodulation strategies. In cancer immunovirotherapy, the many available "omics" and systems biology technologies generate at a fast pace a challenging huge amount of data, where apparently clashing information mirrors the complexity of individual clinical situations and OV used. In this review, we present and discuss how currently big data analysis, on one hand and, on the other, simulation, modeling, and computational technologies, provide invaluable support to interpret and integrate "omic" information and drive novel synthetic biology and personalized OV engineering approaches for effective immunovirotherapy. Altogether, these tools, possibly aided in the future by artificial intelligence as well, will allow for the blending of the information into OV recombinants able to achieve tumor clearance in a patient-tailored way. Various endeavors to the envisioned "synthesis" of turning OVs into personalized theranostic agents are presented.

Synthetic biology encompasses metagenomics, ecosystems, and biodiversity sustainability within its scope

We envision the convergence of synthetic biology (SynBio) and metagenomics as a significant development for the engineering of complex biological systems. The entire biosphere with its diverse life forms can also be considered as a reservoir for evolutionary innovations and a source of modules for SynBio. Metagenomics, which is a large part of biodiversity, should be considered as an important source of modules. The abstraction hierarchy of amalgamating SynBio and metagenomics (“synthetic metagenomics”) entails the standardized integration of parts, devices, circuits, and modules into functional chassis. These principles transcend the boundaries of single cell design and apply to the engineering of biodiversity sustainability in multicellular entities, their interconnections, and their dynamics in communities and whole ecosystems. Examples include applications in environmental sustainability, such as analysis of antimicrobial resistance in waste management, bioremediation of oil spills, and degradation of plastics. Future research and experimental interventions will ultimately provide a strong link between bioengineering, metagenomics, microbial consortia, ecosystems, and biodiversity sustainability under the umbrella of synthetic biology.

AWWA Water Science Author Spotlight

Having recently published an article in AWWA Water Science, Victoria Nystrom answered questions from the publication's editor-in-chief, Kenneth L. Mercer, about the research. Seasonal Impacts on Biofiltration Acclimation Dynamics and Performance Victoria E. Nystrom, Ashley N. Evans, Jason T. Carter, and Ben Kuhnel Victoria Nystrom collects water samples from a biofilter as part of an ozone and biofilter pilot study that was conducted for the US Army Corps of Engineers in Washington, D.C. Victoria and Arcadis coworker Nissim Gore-Datar explore the parks and downtown Austin, Texas. I am thankful to have had many mentors in my life between undergraduate school, graduate school, and my current company, Arcadis. One mentor who stands out is Dr. Kelsey Pieper. I got connected with her when I began undergraduate research in the Department of Biological Systems Engineering at Virginia Tech. Through the years, I worked under her and then alongside her. She transitioned from being my superior in the field; to getting me connected with my advisor and being a member on my advisory committee; and to being a friend, colleague, and continuous mentor. Without her support, technical expertise, and humor, I would not be where I am in my career today. At the time this study was conducted, there were two broad objectives of the research: (1) understand the importance of monitoring biofilter acclimation and (2) fill the larger knowledge gap in the industry around the use of biofiltration and the benefits and challenges associated with this type of treatment. There was a unique opportunity with the City of Denton [Texas] to monitor the acclimation of biofilters and understand the duration of this process as their approach was to bring half the biofilters on line in the winter and the remaining half on line in the summer. With this research and other studies that have been completed to date, we've been able to recognize the influence of seasonal changes on biofilter performance for removal of water quality parameters of interest such as total organic carbon and manganese. As many utilities consider the use of biofiltration, we need to understand how to optimize this treatment during the acclimation phase to ensure regulatory compliance and also to meet internal targets set by utilities. Scuba diving with her sister in the south Pacific Ocean. Victoria (far right) and ski friends at the top of a mountain in Wyoming. The findings of this research highlight the use of a sustainable water treatment solution that can help address an array of contaminants but could also act as a buffer for other emerging contaminants in the future. As we learn more about the health effects of various contaminants, drinking water regulations becoming stricter (e.g., California proposed revisions to notification and response levels for manganese in drinking water) and utilities proactively pursue lower targets in their finished water. Effective treatment processes need to be in place. I grew up in the suburbs of Philadelphia, drinking well water. While I had very little knowledge of water treatment, the progression of my childhood and career naturally influenced my drinking water research. Private wells are not regulated by EPA; thus, homeowners are responsible for maintaining their wells and quality of their drinking water. Understanding how my parents were responsible for maintaining their own mini-distribution system at home led me to want to better understand how other people receive their drinking water and how to have treatment processes in place to ensure it is safe and clean. I love traveling and enjoying the outdoors like many of my colleagues in the drinking water and environmental engineering industry. I've been an avid skier since I was five years old and annually travel with 30-plus friends to the Teton Mountains for the best skiing in the United States. On the opposite end of the spectrum, I enjoy scuba diving and taking underwater photos; swimming with a whale shark is on my bucket list! I am also a dog lover, and while I don't have my own dog, I am an aunt to many and thoroughly spoil them when I visit friends and family. To learn more about Victoria's research, visit the article, available online at https://doi.org/10.1002/aws2.1225.

In vitro validation of Cathepsin S docking models

Abstract In dendritic cells, the dominant lysosomal protease involved in Ii chain degradation and antigen digestion is Cathepsin S, a cysteine protease in the papain family. Extracellularly, Cathepsin S also contributes to pathological processes in cardiovascular disease, cancer, and autoimmune arthritis: unlike most lysosomal proteases, it retains its activity at serum pH. In computational molecular docking studies comparing peptides from the literature, we found that Cathepsin S shows a pH-dependent specificity switch, with predicted dissociation constants for specific peptide sequences differing at lysosomal and serum pH. The aim of this study is to further characterize this specificity switch and to compare docking results to kinetic parameters of Cathepsin S digestion in vitro. We identified peptide sequences of interest from docking, including sequences from the Ii chain and elastin, and digested these peptides (LifeTein) using human Cathepsin S (Calbiochem), collecting samples for LC-MS analysis over 16 hour reaction times. We report the kinetic parameters of Cathepsin S catalysis at both lysosomal and serum pH. Digestion results are compared to docking models and the models most predictive of catalytic activity are identified. Further investigation is needed to determine whether pH-dependent catalytic activity and selectivity of Cathepsin S will enable the design of protein-based therapies and inhibitors optimized for their targeted microenvironments. This work was partially supported by the NIDA/NIH award UG3DA048775, the USDA National Institute of Food and Agriculture, AFRI project #2021-0858, and the department of Biological Systems Engineering at Virginia Tech.

Deciphering crucial genes in multiple sclerosis pathogenesis and drug repurposing: A systems biology approach

This study employed systems biology and high-throughput technologies to analyze complex molecular components of MS pathophysiology, combining data from multiple omics sources to identify potential biomarkers and propose therapeutic targets and repurposed drugs for MS treatment.This study analyzed GEO microarray datasets and MS proteomics data using geWorkbench, CTD, and COREMINE to identify differentially expressed genes associated with MS disease. Protein-protein interaction networks were constructed using Cytoscape and its plugins, and functional enrichment analysis was performed to identify crucial molecules. A drug-gene interaction network was also created using DGIdb to propose medications.This study identified 592 differentially expressed genes (DEGs) associated with MS disease using GEO, proteomics, and text-mining datasets. 37 DEGs were found to be important by topographical network studies, and 6 were identified as the most significant for MS pathophysiology. Additionally, we proposed six drugs that target these key genes.Crucial molecules identified in this study were dysregulated in MS and likely play a key role in the disease mechanism, warranting further research. Additionally, we proposed repurposing certain FDA-approved drugs for MS treatment. Our in silico results were supported by previous experimental research on some of the target genes and drugs. SignificanceAs the long-lasting investigations continue to discover new pathological territories in neurodegeneration, here we apply a systems biology approach to determine multiple sclerosis's molecular and pathophysiological origin and identify multiple sclerosis crucial genes that contribute to candidating new biomarkers and proposing new medications.

Opportunities and challenges of digital world of multimodal interventional breast radiology (review)

The article presents the evolution of the introduction of digital technologies into the oncomammoscreening system, multimodal technologies of interventional radiology, statistics on the growth of malignant breast neoplasms in the world and Russia, shows the advantages of digitalization of radiology, interdisciplinary integration with omix systems biology technologies, which contributes to the development of a new direction of radiogenomics, and also points out the existing problems of introducing the latest technologies of interventional radiology, the need to prepare multimodal specialists. The purpose of the study . To show the possibilities of modern digital radiological non-invasive and invasive technologies, as well as the need for interdisciplinary integration and training of multimodal specialists for the early detection and determination of the phenotype of breast cancer, increasing the effectiveness of choosing adequate treatment tactics aimed at increasing the duration and quality of life of patients. Materials and methods. There are given literary data on development of radiological digital methods of diagnostics of breast diseases, including technologies of interventional radiology. New data are also presented on the direction of radiogenomics, born due to the interdisciplinary integration of radiological digital systems, interventional technologies and high-tech systems biology. Results. The review presents the development of equipment and radiological methods for early recognition of breast diseases, based on technical progress, including the active introduction of digitalization and informatization in medicine. New models of artificial intelligence act as a physician assistant in screening. Advantages of various versions of interventional radiology technologies in oncomammoscreening are shown to increase accuracy of pathomorphological diagnostics and determination of tumor phenotype. The latest data on feasibility of interdisciplinary integration of «computer vision» on signs of medical imaging with capabilities of systems biology in determination of tumor phenotype with correlation validity in 71% are given. Conclusions. The undeniable advantages of digitalization and the new capabilities of interventional radiology in detecting the earliest forms of diseases in an interdisciplinary format open the prospect of high-precision diagnostics and an adequate choice of organ-saving therapeutic tactics. The training of a multimodal specialist – radiologist – radiologist-diagnostics and interventional radiologist, who has a wide range of methods of radiological diagnostics, including invasive interventions, requires correction of organizational forms of work, new educational programs not only in the specialty, but also in the basics of digitalization, which will ensure the rational and effective use of modern achievements of science and technology.

Emerging Life Sciences Series: Q&A with the Editor

Emerging Life Sciences Series: Q&A with the Editor

Strong expression of Cas9 under a new 3′-truncated TEF1α promoter enhances genome editing in Yarrowia lipolytica

The non-conventional yeast Yarrowia lipolytica is gaining interest in biotechnology as a workhorse for the production of proteins, lipids and other biomolecules. Site-specific genome editing is however limited in this yeast. Although, this was much improved by the recent adaptation of a CRISPR-Cas9 genome editing protocol for Y. lipolytica based on a tRNA-sgRNA fusion, yet, in the latter protocol, Cas9 is under the control of a synthetic hybrid promoter, pUAS1B8-TEF(136) that is associated with some drawbacks. This hybrid promoter contains tandem repeats are suggested to cause in vivo and in vitro inconveniences like polymerase slippage, random genetic rearrangements and cloning difficulties. Here we report a newly designed synthetic TEF promoter to drive Cas9 expression, pTEF(-41–406)-Kozak, which is a rationally 3′-truncated version of the already known 5′-truncated pTEF(406) promoter of Y. lipolytica fused to a synthetic Kozak sequence. Our comparison of the promoters’ strength using hrGFP reporters and RT-qPCR showed that the synthetic pTEF(-41–406)-Kozak has an equivalent expression strength to that of pTEF(406), yet is at least 5 times stronger than the hybrid pUAS1B8-TEF(136). The pTEF(-41–406) promoter mediated high expression of Cas9 and was not associated with any growth defects. Moreover, expression of Cas9 under pTEF(-41–406)-Kozak increased the gene integration efficiency by up to 40 % relative to that when Cas9 is expressed under pUAS1B8-TEF(136). Both pTEF(-41–406) and pUAS1B8-TEF(136) performed equally well as drivers of Cas9 expression with respect to gene deletion as demonstrated both on the genotypic and phenoypic levels. This is the first study conducting rational 3′-truncation in TEF promoter in Y. lipolytica based on in silico analysis of promoter sequence and structure. This approach of promoter engineering can be extended to the engineering of other yeast promoters to generate small-sized synthetic biology parts for convenient engineering of biological systems. This work provides a strong Cas9 expression cassette for more convenient and efficient CRISPR-Cas9-mediated genome editing in Y. lipolytica which will facilitate harnessing the full potential of this industrial strain.

Research progress on metabolomics in the quality evaluation and clinical study of Panax ginseng.

Panax ginseng, an essential component of traditional medicine and often referred to as the king of herbs, has played a pivotal role in medicine globally for several millennia. Previously, traditional phytochemical methods were mainly used for quality evaluation and pharmacological mechanism studies of ginseng, resulting in the lack of systematicness and innovation and hindering the development and utilization of ginseng resources. Since the beginning of the new century, systems biology technology represented by metabolomics has shown unique advantages in the modernization and internationalization of herbal medicine, establishing a bridge for communication between traditional medicine and modern medicine. P. ginseng, a special herb used in medicine and food, is one of the main research objects for qualitative and quantitative analysis of metabolomics and has gradually become the focus of researchers globally. Here, we conducted a comprehensive summary and analysis of numerous studies published in ginseng metabolomics. This review aims to provide more novel ideas for the quality evaluation, development, and clinical application of ginseng in the future and offer more useful technical references for the modernization and internationalization of herbal medicine based on metabolomics.

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts, as these stress states can lead to suboptimal growth and lower yields of target products. To enable the design of genetic circuits for reporting or mitigating the stress states, it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria. Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria, motivating computational methods that can identify robust biomarkers for experimental characterisation and verification. Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis, this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments. We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification (mean f1-score achieved at 0.99), as well as in a range of independent datasets (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.

Modulating myoblast differentiation with RNA-based controllers.

Tunable genetic controllers play a critical role in the engineering of biological systems that respond to environmental and cellular signals. RNA devices, a class of engineered RNA-based controllers, enable tunable gene expression control of target genes in response to molecular effectors. RNA devices have been demonstrated in a number of systems showing proof-of-concept of applying ligand-responsive control over therapeutic activities, including regulation of cell fate decisions such as T cell proliferation and apoptosis. Here, we describe the application of a theophylline-responsive RNA device in a muscle progenitor cell system to control myogenic differentiation. Ribozyme-based RNA switches responsive to theophylline control fluorescent reporter expression in C2C12 myoblasts in a ligand dependent manner. HRAS and JAK1, both anti-differentiation proteins, were incorporated into RNA devices. Finally, we demonstrate that the regulation of HRAS expression via theophylline-responsive RNA devices results in the modulation of myoblast differentiation in a theophylline-dependent manner. Our work highlights the potential for RNA devices to exert drug-responsive, tunable control over cell fate decisions with applications in stem cell therapy and basic stem cell biology research.

A Closed Form Model for Molecular Ratchet-Type Chemically Induced Dimerization Modules.

Chemical-induced dimerization (CID) modules enable users to implement ligand-controlled cellular and biochemical functions for a number of problems in basic and applied biology. A special class of CID modules occur naturally in plants and involve a hormone receptor that binds a hormone, triggering a conformational change in the receptor that enables recognition by a second binding protein. Two recent reports show that such hormone receptors can be engineered to sense dozens of structurally diverse compounds. As a closed form model for molecular ratchets would be of immense utility in forward engineering of biological systems, here we have developed a closed form model for these distinct CID modules. These modules, which we call molecular ratchets, are distinct from more common CID modules called molecular glues in that they engage in saturable binding kinetics and are characterized well by a Hill equation. A defining characteristic of molecular ratchets is that the sensitivity of the response can be tuned by increasing the molar ratio of the hormone receptor to the binding protein. Thus, the same molecular ratchet can have a pico- or micromolar EC50 depending on the concentration of the different receptor and binding proteins. Closed form models are derived for a base elementary reaction rate model, for ligand-independent complexation of the receptor and binding protein, and for homodimerization of the hormone receptor. Useful governing equations for a variety of in vitro and in vivo applications are derived, including enzyme-linked immunosorbent assay-like microplate assays, transcriptional activation in prokaryotes and eukaryotes, and ligand-induced split protein complementation.

Artificial Intelligence: Bioengineers' Ultimate Best Friend

Artificial Intelligence: Bioengineers' Ultimate Best Friend

Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR.

The use of standardized components and processes in engineering underpins the design-build-test model, and the engineering of biological systems is no different. Substantial efforts to standardize both the components and the methods to validate the engineered biological systems is ongoing. This study has developed a panel of control materials encoding the commonly used reporter genes GFP and RFP as DNA or RNA molecules. Each panel contained up to six samples with increasingly small copy number differences between the two reporter genes that ranged from 1- to 2-fold differences. These copy number differences represent the magnitude of changes that may need to be measured to validate an engineered system. Using digital PCR (dPCR), we demonstrated that it is possible to quantify changes in both gene and gene transcript numbers both within and between samples down to 1.05-fold. We corroborated these findings using a simple gene circuit within a bacterial model to demonstrate that dPCR was able to precisely identify small changes in gene expression of two transcripts in response to promoter stimulation. Finally, we used our findings to highlight sources of error that can contributed to the measurement uncertainty in the measurement of small ratios in biological systems. Together, the development of a panel of control materials and validation of a high accuracy method for the measurement of small changes in gene expression, this study can contribute to the engineering biology “toolkit” of methods and materials to support the current standardization efforts.

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.