From Manual Pipettes to AI-Driven Labs: A Decade of Evolution in Synthetic Biology

The Rise of a New Synbio Era

In a recent episode of talk is biotech!, a podcast hosted by Guru Singh, Founder and CEO of Scispot, featuring discussions with biotech entrepreneurs including Kevin Chen, Co-Founder and CEO of Hyasynth Bio, a pioneering biotech company focused on producing cannabinoids in yeast, the two biotech entrepreneurs discussed how far synthetic biology ("synbio") has come and what it means for today's startup founders.

Just ten years ago, a biotech experiment often meant painstaking gene editing with pipettes and trial-and-error cloning. Today, a founder can design DNA on a computer, order custom gene constructs online, and have automated robots run dozens of experiments in parallel - all in a fraction of the time and cost it used to take. This article explores the technological evolution in synthetic biology and synbio, highlighting concrete advances in gene editing, lab automation, DNA synthesis, workflows, cost reduction, turnaround time, and platform services that are empowering biotech startups to scale R&D quickly and cost-effectively.

Scispot, a company providing advanced AI-driven lab operating systems, is known for offering the best AI stack to life science labs. This emphasis on technology reflects a broader transformation underway in synthetic biology. Over the past decade, the field has evolved from manual, labor-intensive bench work into a high-speed, automated, and data-driven enterprise.

From Labor-Intensive Processes to Streamlined Workflows

In the early 2010s, synthetic biology work was largely manual and slow. Building a new genetic construct meant weeks of cloning DNA fragments by hand, often using methods like BioBrick assembly (standardized DNA parts) that had to be stitched together one by one. Success was far from guaranteed - experiments commonly failed when engineered DNA was inserted into cells, due to unpredictable interactions in the cell environment.

Gene Editing Revolution

Gene editing was also cumbersome before the CRISPR revolution. Before 2013, scientists relied on bespoke enzymes like zinc-finger nucleases (ZFNs) and TALENs to modify genes. These tools worked but were expensive, required expert skill in protein engineering, and could only target one gene at a time. By contrast, the emergence of CRISPR-Cas9 around 2013 was a watershed moment - it popularized the concept of routine gene editing and made precise DNA modification much less labor-intensive and cheaper than ZFNs or TALENs.

With a simple guide RNA, researchers could direct CRISPR to virtually any gene, dramatically simplifying workflows. In short, what used to require months of planning and tinkering by PhD-level experts could now be done in days by any trained scientist using a CRISPR kit. This shift, as Kevin Chen notes, has enabled even small research teams to "modify DNA to accomplish incredible things" on a routine basis.

DNA Synthesis Cost Revolution

Another revolution in this period was the move from physical DNA assembly to ordering DNA online. As late as the 2000s, a scientist might spend weeks in the lab cutting and pasting DNA pieces (enzymatic digestion, ligation, transformation, etc.) to clone a new gene sequence. Today, it is often more cost-effective to simply order a custom-made gene from a synthesis service than to clone it yourself.

The cost of DNA synthesis has plummeted so much that researchers now treat DNA as a cheap commodity. A decade ago, synthesizing a gene cost on the order of $1-$10 per base pair, whereas now it can be $0.10 or less per base pair. This 10× to 100× cost reduction completely changed experimental strategy. For example, instead of laboriously cloning one gene variant, a startup team can afford to order multiple variants and test all of them, speeding up discovery.

The turnaround time has also shrunk: companies like Twist Bioscience advertise express gene synthesis services delivering DNA in about 4-7 business days. Compare that to the past, when cloning a gene in-house might take several weeks (not counting the possibility of failures requiring repeats).

Kevin Chen's experience at Hyasynth Bio reflects this shift - what began as a long, trial-and-error effort to engineer yeast in the mid-2010s can today be accelerated by readily available DNA synthesis and editing tools that simply did not exist when Hyasynth started. In fact, Hyasynth's very mission, producing cannabinoids in yeast, was considered science fiction in the early 2010s; by 2019, researchers had successfully achieved yeast-based production of cannabinoids as a milestone for the field. This illustrates how quickly synbio capabilities have expanded.

Key Technological Advancements Driving Synthetic Biology

Synthetic biology's progress over the past decade can be attributed to a convergence of technological advancements that streamlined each step of the R&D process. Guru Singh and Kevin Chen emphasized several game-changers during their talk is biotech! discussion: high-throughput DNA construction methods, automation in the lab, and powerful computational tools.

The Design-Build-Test-Learn Cycle Revolution

It's helpful to consider the typical "design-build-test-learn" cycle in biotech and how new technology has turbocharged each phase:

Design Phase: Design now starts on computers - researchers use software to design DNA sequences and even simulate how genetic circuits might behave. Cloud-based DNA part libraries and databases (e.g. Addgene's repository of plasmids) allow teams to reuse and remix existing biological parts instead of reinventing the wheel.

Build Phase: Techniques like Golden Gate assembly (a one-pot DNA assembly method) have become standard, replacing slower manual cloning. Labs have also adopted gene editing not just for single tweaks but for large-scale genome modifications; CRISPR's toolkit has expanded (including CRISPRi for gene regulation and base editing for single-letter DNA changes), giving scientists granular control over genomes.

Test Phase: This phase saw perhaps the most dramatic acceleration. Rather than testing one engineered strain at a time, modern synthetic biology leverages massively parallel experiments. Thanks to cheaper DNA and new screening methods, researchers can design hundreds of thousands of genetic variants in one experiment and read the results via next-generation sequencing.

For example, by embedding DNA barcodes or reporters in each variant, a single pooled experiment can reveal which designs worked best, shifting the bottleneck from lab work to data analysis. Kevin Chen pointed out that a decade ago one had to carefully model and guess the best genetic design (due to limited capacity to test many candidates), whereas now brute-force empirical testing of many designs is feasible - "data analysis becomes your new bottleneck, not design and assembly as before."

Learn Phase: Finally, the learn phase (closing the loop with insights) has been supercharged by computational biology and AI. Labs now routinely apply machine learning to large datasets, identifying patterns in genetic data or suggesting optimal design tweaks. Chris Voigt's group at MIT, for instance, demonstrated this with Cello, an automated genetic circuit design platform, heralding an era where algorithms assist in engineering biology.

The upshot is that each iteration of the design-build-test-learn cycle is faster and more reliable than it was ten years ago.

Synthetic Biology Progress: Then vs. Now

Aspect	Circa 2010-2012 (Then)	Circa 2023-2025 (Now)
Gene Editing Tools	Relatively niche and complex (ZFNs, TALENs); costly custom protein design for each target. Editing one gene at a time was an achievement.	Democratized by CRISPR-Cas9 (introduced 2013): simple guide RNA design instead of protein engineering. Much less labor-intensive and cheaper than earlier methods. Enables editing of multiple genes simultaneously and high-throughput screens.
DNA Construction	Manual cloning of DNA fragments (BioBrick, PCR & ligation). Often a slow, multi-week process with high failure rates. Standardized part libraries existed (e.g. iGEM) but assembly had to be done serially piece by piece.	Modular cloning kits (e.g. Golden Gate) and automated assembly now standard. Many labs have access to dedicated DNA assembly robots. Researchers commonly order synthetic genes rather than clone - synthesis costs fell from ~$1-10 per base to <$0.10 per base in ~10 years. As a result, ordering a gene is often cheaper and faster than DIY cloning.
Automation in Labs	Limited to well-funded labs. Robots (for pipetting, etc.) were expensive ($50k-$100k+) and required specialists to program. Most early-stage companies could not afford automation, relying on human technicians for experiments.	Lab automation is far more accessible. E.g. the Opentrons OT-2 robot costs about $4,000 (roughly 10× cheaper than traditional lab robots), allowing even small startups to automate routine liquid-handling tasks. "Automation for any lab" is now a reality, with user-friendly robots and software. This democratization lets startups scale up experiments without hiring armies of lab techs.
Throughput & Scale	Design-Build-Test cycle was slow. Each cycle (from idea to result) could take months. Limited throughput - you might test a handful of genetic constructs in parallel. Modeling was used to narrow options because doing dozens of wet-lab iterations was impractical.	High-throughput by default. Thanks to cheap DNA and better workflows, teams can test hundreds or thousands of designs in one go. Turnaround for a cycle is faster (for instance, gene synthesis in ~1 week, automated assays in days). Rather than one scientist-one experiment, automation allows a few scientists to run dozens of experiments, learning much faster. Data analysis (often aided by AI) is now the primary rate-limiter, not the experiment itself.
Data Management	Mostly manual or in disparate spreadsheets/notebooks. Scaling an experiment often introduced data chaos. Limited integration between lab instruments and data analysis; many processes weren't digitized.	Integrated lab data platforms. Modern labs use electronic lab notebooks (ELNs), laboratory information management systems (LIMS), and specialized lab operating systems (e.g. Scispot) to centralize data. These platforms connect instruments, automatically log results, and even use AI (e.g. Scispot's Scibot AI assistant) to spot trends across experiments. This not only improves data quality and traceability, but also frees up researchers from menial data wrangling.
Cost to Startup	High upfront costs. Setting up a basic synbio lab could run into hundreds of thousands of dollars in equipment and months of effort. Many processes (gene sequencing, synthesis) had to be outsourced at great expense. Venture capital was cautious - only a few synbio startups (often spun out of academia) got funded, due to long development timelines.	Lower barrier to entry. Many critical services are now available on-demand: DNA sequencing for <$1,000 per human genome, gene synthesis for a few hundred dollars, and even "Cloud labs" that perform experiments remotely. Startup teams can rent robotic lab time or use shared facilities instead of buying everything. The result: a proliferation of synbio startups tackling diverse problems, and investors more willing to back them now that development cycles are faster. Some founders even bootstrap companies to proof-of-concept with minimal resources, something virtually unheard of a decade ago.

The Emergence of Platform Services and Ecosystems

One of the most profound changes benefiting early-stage biotech founders is the rise of platform services in synthetic biology. Instead of each startup building every capability in-house, a rich ecosystem of specialized platforms now exists. Guru Singh describes this as a shift toward a "collaborative model for scaling to production" - companies can focus on their unique science while leveraging external platforms for support.

DNA Synthesis and Engineering Services

For example, why struggle for months to set up a DNA synthesis pipeline when firms like IDT, Twist Bioscience, or GenScript will synthesize any sequence to order? Need to run a high-throughput screen or fermentation? Services like Ginkgo Bioworks (which operates large automated "foundries") offer organism engineering as a service, allowing startups to outsource some R&D and get results faster than they could alone.

Cloud Laboratories

Similarly, cloud laboratories (such as Emerald Cloud Lab or Strateos) let researchers program experiments remotely; robots at the cloud facility execute the protocol and return the data. This means a startup in, say, a small office can perform complex bioexperiments without owning a wet lab at all.

Kevin Chen recounts how in Hyasynth's early days, they had to build new protocols and persuade investors to fund unproven science - essentially creating a roadmap from scratch. Now, a new synbio entrepreneur has roadmaps to follow and vendors to partner with at each step, from DNA assembly to scale-up bioprocessing.

Digital Lab Infrastructure

Even within a lab, platform thinking has taken hold. Rather than treating each experiment as a one-off project, modern biotech companies set up reusable workflows and automation pipelines. Scispot's platform is one example: it functions as a Lab Operating System, uniting data from instruments, protocols, inventory, and analysis in one place.

This kind of digital backbone means a startup can quickly repeat or scale experiments, maintain data integrity, and even apply AI to optimize processes. The payoff is huge for young companies - as Guru Singh notes, the ultimate vision is a democratization of biotech where even small teams "can run numerous experiments, primarily in silico, with minimal wet lab work."

We are already seeing the beginnings of that: some design tasks have moved entirely on-screen (computational protein design, AI-driven metabolic pathway prediction, etc.), and only the final testing might be done in a physical lab. All these platform services and software tools collectively shorten the innovation cycle. A founder with a clever biotech idea can test it rapidly: design the DNA on Monday, have it synthesized and delivered next week, tested in an automated assay the week after, with data streaming into a cloud platform for analysis.

Empowering Startups to Scale Faster and Smarter

For early-stage biotech founders, the evolution of synthetic biology technology is not just a story of scientific progress - it's a practical enabler for business. Faster workflows and lower costs mean startups can reach key milestones (like proof-of-concept or prototype development) in months rather than years. This agility can be the difference between attracting investment or exhausting funds.

Real-World Impact: The Hyasynth Bio Story

Kevin Chen's journey with Hyasynth Bio underscores the contrast: it took nearly a decade of R&D to bring their yeast-based cannabinoid production to fruition, partly because when they started, the toolset was limited and expensive. Today, a new company in the same space could likely achieve similar milestones far more quickly by leveraging the advances we've outlined.

In Guru Singh's words, we're approaching a future where biotech innovation might move at "software speed" - meaning rapid cycles of build and test - and that fundamentally changes the startup game.

Cost Efficiencies and Experimental Freedom

Consider cost efficiencies: when gene synthesis cost drops by 90%+ and automation cuts labor by 70%, a startup's dollar goes much further. Founders can do more experiments with the same budget, increasing the odds of finding a successful result. It also allows more shots on goal - exploring multiple product ideas or biological pathways in parallel, which was rarely feasible before.

No longer must a young biotech "bet the farm" on a single engineered strain or process; they can iterate and pivot quickly if data points another direction. Lower costs and readily available infrastructure have even enabled bioentrepreneurs in non-traditional locations to launch companies. For instance, one report noted nearly $1 billion invested in deep-tech biology startups in India from 2013-2023, a sign that the synbio revolution is spreading globally, not just in Silicon Valley.

Speed and Investor Confidence

Speed is another critical factor. With streamlined workflows, the turnaround time from idea to experimental result is shorter than ever. Founders can demonstrate technical progress to stakeholders on startup-friendly timescales. This has begun to shift investor attitudes - biotech is no longer viewed as a "slow burn" field only suited for big pharma or academia.

As one talk is biotech! discussion highlighted, if tomorrow's breakthroughs can happen in months or even hours instead of years, it "dramatically changes how investors view the industry." We are already seeing venture capital flock to synthetic biology platforms and product companies, encouraged by quicker results and the success of early exemplars.

Focus on Innovation

Finally, the new paradigm frees founders to focus on innovation. When much of the grunt work (gene synthesis, basic assays, data management) can be offloaded to services or automated, small teams can put their energy into the creative and high-level aspects of their science. This is exactly how tech startups operate - using cloud computing and open-source tools so they can concentrate on product development - and now biotech startups can do the same with the growing synbio tech stack.

As Kevin Chen's story illustrates, even when "no roadmap exists," a combination of grit and modern tools can turn a bold research idea into a biotech breakthrough. Founders today should take heart that they have an unprecedented toolbox at their disposal.

Conclusion

In just a decade, synthetic biology has undergone a transformation akin to moving from the handicraft era to the industrial and digital age. Tasks that once kept scientists hunched over lab benches for hours - pipetting, cloning, debugging experiments - are increasingly automated or outsourced. Design and analysis, powered by AI and cloud computing, have risen to prominence. The result is a faster, more predictable R&D pipeline.

For early-stage biotech startups, this evolution lowers barriers to entry and accelerates progress: you can do more science with less money and time, leveling the playing field with larger competitors. The conversation between Guru Singh and Kevin Chen on talk is biotech! encapsulates this excitement. Companies like Scispot provide the digital and AI "tech stack" for labs, while pioneers like Hyasynth prove that small teams can achieve what once required big-institution resources.

The technological evolution of the past ten years - from manual gene tinkering to cloud-synchronized, AI-assisted experimentation - has turned synthetic biology into a founder-friendly arena. With continued advances, we may soon see biotech innovation happening at a pace that surprises even the software industry.

For those about to launch or grow a synbio startup, there has never been a better time to build. Armed with the latest tools and platforms, today's biotech founders truly stand on the shoulders of a decade of innovation in synthetic biology and synbio, empowered to scale their experiments quickly, cost-effectively, and confidently into the future of biotech startups.