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TOPICAL COLLECTIONS

Next-Generation Bioengineering: Accelerating Discovery Through Automation, AI, and Synthetic Biology
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Guest Editor

Justin Panich

Justin Panich

Lawrence Berkeley National Laboratory

<p>Justin Panich, PhD, is a research scientist at Lawrence Berkeley National Laboratory and an MBA candidate at the University of California, Berkeley’s Haas School of Business. His research is dedicated to developing industry-applicable biological systems for energy and bioproduct sectors, with a specialized focus on engineering autotrophs and extremophiles to advance biomanufacturing and mining technologies. His work sits at the intersection of molecular biology and process scale-up, focusing on the tools that make biological engineering more predictable and economically viable. As a guest editor for JoVE, he is committed to highlighting standardized, automated workflows that allow scientists to bridge the gap between foundational research and real-world impact. Dr. Panich’s dual background in science and business provides a unique perspective on accelerating the translation of synthetic biology into impactful industrial solutions.</p>

Collection Overview

Synthetic biology has reached a critical inflection point where the traditional "Design-Build-Test-Learn" (DBTL) cycle is being revolutionized by the convergence of high-throughput automation, artificial intelligence, and sophisticated genetic toolkits. This evolution is vital for addressing global challenges, ranging from the engineering of robust microbial strains for industrial bioproduction to the development of complex mammalian cell systems for next-generation therapeutics. By significantly reducing the time and cost required to navigate vast biological design spaces, these integrated technologies accelerate the leap from basic research to functional, scalable biological solutions.

 

This Topical Collection aims to provide a specialized visual platform for scientists to showcase standardized protocols and digital workflows that underpin these advancements. By highlighting the practical integration of robotic liquid handling, machine learning-guided strain and circuit design, and high-throughput genome editing, this collection seeks to demystify complex automated setups and promote interoperability across the global research community. Ultimately, these visual resources will empower the next generation of biotechnologists to achieve higher levels of experimental reproducibility, bridging the gap between computational innovation and bench-top execution in both industrial and pharmaceutical sectors.

Articles

High-Throughput Screening of L-Lactic Acid-Hyperproducing <em>Bacillus coagulans</em> Mutants Using an Automated Droplet Microfluidic Platform
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High-Throughput Screening of L-Lactic Acid-Hyperproducing Bacillus coagulans Mutants Using an Automated Droplet Microfluidic Platform

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