Progress in molecular biology and translational science最新文献

Cell-free systems let researchers carry out biological processes like protein synthesis and metabolism without using living cells. This approach has become increasingly important in synthetic biology because it allows for quick testing of ideas, running many experiments simultaneously, and maintaining tight control over reaction conditions. The main challenge has been figuring out how to optimize these systems, since there are so many variables that interact in unpredictable ways. Artificial intelligence (AI), including machine learning, deep learning, and generative models, has begun to tackle this problem by helping predict experimental outcomes, design new proteins, and find better reaction conditions. The discovery of antimicrobial peptides through deep learning and cell-free protein synthesis, along with a 34-fold increase in protein yield through buffer optimization guided by active learning, are some of the major advancements made possible. The use of Bayesian optimization and neural networks has helped to streamline metabolic pathway designing, enzyme engineering as well as yield prediction, which in turn has diversified the use of AI-driven approaches in biomanufacturing, pharmaceuticals, and diagnostics. In spite of hurdles like data requirements, model transferability, and scalability, the compatibility of AI and cell-free systems gives adequate probabilities of innovations like digital twins and self-driven biomanufacturing units. This chapter explores the integration of AI with cell-free systems, focusing on recent advances, industrial applications, and ending with future directions for synthetic biology.

Cell-free transcription-translation (TXTL) systems provide a robust platform for in vitro protein synthesis, transforming molecular biology, synthetic biology, and biotechnology by replicating natural protein synthesis outside of living cells with exceptional control and flexibility. Initially developed by Nirenberg and Matthaei in the 1960s using E. coli extracts, these systems have undergone substantial evolution, and now incorporate extracts from bacteria, yeast, and eukaryotes to construct comprehensive TXTL platforms. A cell-free extract comprises essential components, such as ribosomes, RNA polymerase, and tRNAs, enabling protein synthesis directed by DNA templates through transcription and translation. TXTL systems, offer distinct advantages, including rapid, efficient, and accurate synthesis of natural and non-natural proteins, enhanced chemical resistance, and streamlined labeling-often surpassing cell-based techniques. Their extensive application span synthetic biology and biopharmaceutical production. Despite this promise, challanges remain, including high cost, limited protein yield, lack of complex post-translational modifications, and extract instability. Future efforts will focus on overcoming these challenges by reducing costs, improving yields, expanding post-translational modification capabilities, enhancing stability, and developing continuous-flow systems. Ultimately, cell-free systems are poised to deepen our understanding of biological processes and drive the development of innovative biotechnological tools.

Non-canonical amino acids (ncAAs) and unnatural amino acids (UAAs) both holds the characteristic features of the amino acids but lacks functional role in the protein synthesis. The ncAAs and UAAs possess unique chemical, physical or biological properties other than the 20 standard amino acids (canonical amino acid) used by the cell system, imparting novel functions in the cell when incorporated into the proteins. Genetic code reprograming (GCR) is a unique technique which would allow to expand the basic building blocks in addition to the natural 20 amino acids. Rewiring of the genetic code allows site-specific incorporation of these ncAAs/UAAs to target protein and impart the novel properties with a commercial value to the target protein. Roles and potential applications of ncAAs and the UAAs have been discussed in detail with relevant findings significant to the protein engineering and appliations in this chapter.

Cell-free systems (CFS) decouple gene expression and metabolic pathways from living cells, offering a rapid, modular platform for biosensing, pathway prototyping, and protein production. This review surveys mechanistic and data-driven computational approaches tailored to CFS design and optimization. We compare deterministic ordinary differential equation (ODE) and stochastic simulation frameworks for modeling transcription-translation dynamics, describe adaptations of genome-scale metabolic models (GEMs) and flux balance analysis (FBA) for extract-based systems, and evaluate machine-learning strategies that learn sequence-to-function mappings from high-throughput cell-free assays. We summarize key software and discuss applications in paper-based diagnostics, reconstructed metabolic pathways, and high-yield cell-free protein synthesis. Recent advances in CRISPR based regulation using pre expressed dCas9 or RNA processing enzymes enable construction of multi-layer genetic circuits in extracts. Finally, we identify current gaps limited standardization of kinetic assays, sparse public datasets, and few hybrids kinetic-constraint modeling studies and propose a roadmap for community resources and hybrid modeling efforts that combine mechanistic clarity with machine learning (ML)-driven speed.

Cell-free systems have also become a revolutionary platform for low-cost diagnostics, providing fast, flexible, and scalable solutions to the conventional cell-based assays. Such systems, which utilize the fundamental biochemical machinery of cells without the intricacies of living organisms, have been of great use in point-of-care (POC) diagnostics, particularly in resource-poor environments. This chapter offers a broad overview of the basic principles, design approaches, and technological breakthroughs behind cell-free diagnostic development. It discusses the biochemical underpinnings of cell-free expression, such as ribosomal function, transcriptional control, and energy regeneration, with emphases on the leading platforms including E. coli lysates, wheat germ extracts, and PURE systems. The application of synthetic biology in the form of gene circuits, CRISPR-Cas tools, and RNA aptamers is presented here in the framework of improving the sensitivity and specificity of diagnostics. The chapter further discusses recent innovations in paper-based assays, microfluidic biosensors, and wearable biosensors, which are capable of offering real-time and field-deployable diagnostics. Major challenges in the form of reagent stability, scalability, and regulatory implications are analyzed carefully along with recent trends such as AI-based system design and personalization of diagnostics. In extensive case studies, the chapter highlights the promise of cell-free systems in filling diagnostic gaps, enhancing access to healthcare, and revolutionizing global health. This book strives to offer an encyclopedic sourcebook for researchers, clinicians, and innovators interested in bringing cell-free diagnostics forward.

Cell-free systems (CFS) have emerged as transformative tools in synthetic biology, enabling the execution of complex biological reactions such as transcription and translation outside the confines of living cells. By eliminating the cellular membrane, CFS allows unprecedented control over biochemical conditions, facilitating rapid prototyping (up to 10x faster than traditional in vivo systems), streamlined design-build-test cycles, and the direct production of proteins, including those that are toxic or difficult to express in vivo. Rooted in pivotal discoveries from the 1960s, CFS technologies have evolved to include refined systems like the PURE system, freeze-dried diagnostics, and programmable biosynthesis platforms, integrating seamlessly with automation, artificial intelligence, and microfluidics. Modern CFS platforms support a broad range of applications, from on-demand vaccine and therapeutic production to environmental monitoring, protein engineering, and sustainable biomanufacturing. Their modular nature makes them ideal for developing genetic circuits, metabolic pathways, and biosensors, while also accelerating high-throughput screening and educational access through platforms like BioBits. Despite challenges such as reagent costs, batch variability, and scalability, recent advances in energy regeneration, lyophilization, and predictive modelling are progressively addressing these hurdles. Ultimately, CFS is not just a powerful research tool; it represents a paradigm shift toward decentralized, programmable biotechnology. From field-deployable diagnostics to space-based biomolecule synthesis, cell-free systems are paving the way for a more responsive, accessible, and innovative future in biological engineering.

Cell-free systems (CFSs) have become powerful tools in synthetic biology, enabling the creation of fast, modular, and customizable biosensors without relying on living cells. By utilizing in vitro transcription and translation, these systems offer a finely controlled biochemical environment suitable for sensing applications in fields such as healthcare, environmental science, agriculture, and food quality assurance. This chapter provides an in-depth look at the design and functionality of CFS-based biosensors, highlighting the construction of genetic circuits, signal output strategies, and device formats including paper-based platforms, microfluidic systems, and wearable technologies. With use cases ranging from pathogen detection to monitoring environmental contaminants, cell-free biosensors are proving especially valuable in point-of-care (POC) and low-resource settings. The chapter also addresses current limitations such as shelf-life, sensitivity, and scalability and explores engineering solutions including AI-assisted design, molecular optimization, and advanced material integration. Looking ahead, the convergence of CFS biosensing with smart technologies such as IoT and distributed fabrication promises a new era of accessible, intelligent diagnostics.

High-throughput screening (HTS) has revolutionized the identification and evaluation of biomolecules by enabling the parallel testing of large libraries of compounds, nucleic acids, and proteins against biological targets. Traditionally conducted in live cells, HTS faces limitations such as cellular toxicity, metabolic interference, and regulatory constraints. Cell-free systems (CFS), which operate in vitro using reconstituted transcription-translation machinery, have emerged as powerful alternatives. These systems circumvent the constraints of cellular physiology, allowing for rapid and tunable expression of biomolecules directly from DNA or RNA templates. This chapter explores the principles, platforms, and applications of CFS-based HTS, highlighting its transformative impact on synthetic biology, drug discovery, diagnostics, and protein engineering. Several cell-free systems are detailed, including those derived from E. coli, wheat germ, rabbit reticulocytes, and the defined PURE system. The integration of CFS with high-throughput platforms such as microplates, droplet microfluidics, and paper-based devices enables cost-effective, scalable, and multiplexed assays. Analytical readouts, including fluorescence, luminescence, mass spectrometry, and digital PCR, provide real-time, sensitive detection of biochemical outputs. Furthermore, automation and machine learning are increasingly incorporated through robotic liquid handling and data-driven DBTL cycles, accelerating discovery and design processes. Despite challenges such as high reagent costs and limited post-translational modifications, innovations such as lyophilized CFS kits, artificial cells, and AI-integrated closed-loop platforms are expanding the frontiers of HTS. Altogether, CFS-based HTS offers a flexible, rapid, and accessible approach for next-generation biomolecular screening and therapeutic development.

Cell-free systems (CFS) have emerged as a key platform in the field of synthetic biology. This is used to understand natural biological systems outside living cells. It contains cell extracts from procaryotes, eucaryotes, which provides a controlled environment for complex biological processes that leads to the synthesis of valuable biomolecules. It lacks natural mechanisms, yet it contains all the necessary components, which are required for the synthesis of desired biomolecules. It is specifically designed for the elimination of barriers to molecular transport across cell membranes. This chapter highlights a basic CFS and its various applications, such as high-throughput protein synthesis and expression, non-canonical amino acids incorporation in proteins, biosensors, drug discovery and in the metabolic engineering. This chapter also focuses on the various case studies and recent advancements to study how these systems are used for the transformation of biotechnology and provides rapid, more adaptable, and affordable solutions in the field of research as well as industrial levels. Altogether, CFS emerged as promising platform in the field of biotechnology, biomedicine, and environmental sustainability.