The world of biotech should take precedence in this decade as we need new classes of medicine, fuel, food, and biomaterial at scale to make our planet healthier and greener. However, for that to happen at scale, we need the underlying tech to make the processes repeatable and data ML and AI ready. That is why we need to tech-enable every biostartup on day 1.
As Gingko Bio says, “Why innovate with biology? Because you can’t eat software!”
The future will be built in the lab. From precision medicines, biofuel, fabric, and food to biosecurity sensors - most physical goods are going to be grown biologically in labs.
Life science companies are working to solve some of this century's biggest problems as quickly as possible. However, biostartups typically do not have access to the right digital foundation required to scale their companies. The primary issue facing any growing life science company is disconnected data. Data is incredibly siloed across different tools and laboratories and connecting this disparate data requires many hours of manual labor or custom engineering resources and code development.Based on our user research with 200+ biotech founders, most biostartups lack the digital resources to create a custom digital solution to centralize their data, automate repetitive tasks, and efficiently collaborate with partners in real-time (e.g., data transfer with CROs). As a result, companies spend 40–50% of their time and resources on redundant administrative work that drives up the development costs of life-saving and enhancing products.
So, why do we need tech bio?
"Biotechs can't afford to do old school science, one experiment at a time. From Day 1, they need to be building processes, infrastructure, and tools that allow them to scale their work, get results faster, and make better decisions. Most biotechs aren't equipped to build their own data management solutions for these problems - fortunately, Scispot has developed a suite of tools and a platform to deploy them that fits into a biotech company of any size and complexity." Sarah Warren, Biotech Founder.
Every bio company’s true asset is their data. But to tap into that data, you need to grow the tree of tech bio to truly scale the company.
Bio is slowly merging with tech to become a true engineering discipline. When “tech” and “bio” come together, the whole is greater than the sum: tech + bio > TechBio. TechBio as a discipline:
Reduces variability to support automation & scalability
Leverages AI & ML to support deeper analysis, and
Promotes communication within & between labs to support information exchange & drive the innovation ecosystem
The TechBio Revolution
We firmly believe the world is now entering the TechBio revolution, where every biotech will operate as a TechBio company, using tech and data to make science programmable, fast, and scalable. These TechBio companies will create new classes of medicine, fuel, food, and biomaterial at scale to make our planet healthier and greener. Steve Jobs predicted the current transition of biotech into TechBio when he said, “The biggest innovations of the 21st century will be the intersection of biology and technology.”
TechBios are massively scalable as they reduce the variability in their research by leveraging artificial intelligence and machine learning to continuously learn from every new data point. As a result, they become faster, more efficient, and more scalable over time.
TechBio is a Journey, Not a Destination
In recent months, the term TechBio has been overused by venture capitalists to categorize bioplatform and synthetic biology (SynBio) companies like Ginkgo Bioworks, Zymergen, Amyris AMRS, Moderna Therapeutics, and Mammoth Biosciences, but TechBio goes beyond platform and SynBio companies.
“Biology is inherently variable and, historically, drug development has been expensive and high-risk. TechBios are data-driven companies that use computation, automation, and machine learning to reduce variability, lower risk, and speed up the time to market for new products.” Satya Singh, Co-Founder and President, Scispot
TechBios are massively scalable because their workflows are repeatable and predictable. These companies reduce the variability in their research by leveraging artificial intelligence and machine learning to continuously learn from every new data point. They become faster, more efficient, and more scalable over time.
Bio is hard, but tech doesn’t need to be. However, currently, bio start-ups have two options when it comes to managing the data of their samples, inventory, equipment, and experiments.
Option 1: Horizontal SaaS that has no biology context such as Airtable, Notion, and Google Sheets.
Option 2: Using old school ELN + LIMS that have no to limited configurability either through GUI (graphical user interface) or CLI (command line interface) such as Benchling and Dotmatics. These tools are often designed for big pharma and enterprise customers and are too rigid for modern biostartups and may require up to 6 months for implementation.
None of the options are viable if the bio company needs to scale fast and make decisions faster, have repeatable processes, and have a feedback loop to learn from the failures. If we want the next decade to be a bio decade, we need SaaS tools catered toward biology.
What bio company needs is to build their growing unified digital brain that has neurons (data) connected through synapses (APIs). Every ELN (electronic lab notebook) & LIMS (lab information management system) together with other data sources should help build a unified digital brain that can templatize routine tasks, and automate the workflows.
As a result, the bio company can easily build their experiment metadata knowledge graph and reduce the number of variables in their research. However, the current tooling doesn’t let the company configure their information management either through GUI or CLI. All the current tooling force the bio companies to retrofit their workflows into the tooling. The tooling should really let you mold the infrastructure and align it with the bio workflows.
A Flexible, Configurable Tech Stack for Biostartups - Scispot™
Scispot is creating the first tech stack for modern biostartups to help them become efficient, scalable TechBio companies. Scispot’s toolkit acts as a glue to stitch together R&D data spread across different tools, wet and dry labs. By eliminating data silos, Scispot makes R&D workflows reproducible and traceable, which makes biotech startups more scalable.
“Our mission is to build the most intuitive, configurable, and integrable tech stack to enable the TechBio revolution.” Guru Singh, Co-founder and CEO, Scispot.
With Scispot™, life science companies can easily connect research, operations, and inventory data. Our flexible tech stack allows biostartups to centralize company-wide data, templatize routine research, and automate non-scientific tasks. Companies can also use Scispot to instantly connect with partners and stakeholders to share data, plan and execute experiments, and provide updates. The platform is fully configurable and offers a range of no-code and programming tools for startups with varying digital expertise.
Here is a summary of how Scispot toolkit helps bio companies to be truly tech bio from day 1:
💡 Scispot offers a digitization and automation suite to life science companies that have limited or no digital resources and expertise. The platform is fully configurable and enables biostartups to set up their digital replica within a few minutes, using GUI
💡 For computational scientists and data engineers, we’ve created LabSQL, a user-friendly suite of programming apps, including secure Open API and Jupyter Notebook connector, as well as graphical user interface (GUI) and command-line interfaces. With LabSQL, companies can continually build and adapt their own personalized lab management system. Computational scientist can easily design their LIMS using CLI.
Popular Scispot Use Cases 🌱 If you are an early-stage biotech founder, you can build the right digital foundation with Scispot, a configurable and unified platform plus integrations.
Over 50 biotech founders have built their biostartups on Scispot. For instance, CellChorus has built on Scispot since Day 1 to allow rapid scaling. With Scispot, CellChorus designs and standardizes research workflows and inventory management.
“As a company that integrates AI with immune cell imaging and microscale manufacturing, we have been a digital life sciences company from the start. Scispot allows us to scale our operations rapidly to serve more researchers and developers of novel therapies to improve patient care.” Daniel Meyer, CEO of CellChorus.
🦄If you are an engineering manager at a fast-growing biotech scaleup, you can use Scispot to complement your internal tech stack and as a glue to stitch together data scattered across your in-house ELN, LIMS, and Google Sheets. We like how an Engineering Manager at Culture Biosciences puts it:
“Scispot is very easy to integrate into our current system. It provided us with some ready-to-use workflows for automatically recording inventory, executing media recipes, and tracking a lot of the things that we do here. This raises our quality of work to a whole new level."
👩🏼💻If you are a computational biologist, you can use Scispot to custom design your own LIMS and access data from your wet lab team right in your Jupyter Notebooks.
👨🏻🔬If you are a wet lab scientist, you can design and execute your experiment by dragging and dropping pre-built templates. 👨🏿🔬 If you are a lab manager, you can automate inventory management, ordering, and equipment maintenance.
A Connected TechBio Ecosystem (aka Labverse™)
The life science industry is moving away from large companies doing most experiments in-house towards more agile, often virtual, interdependent companies that outsource various aspects of their workflows. This new model makes data management extra challenging with data siloed across wet labs, dry labs, CROs, and academic collaborators.
Together with our customers and partners, Scispot is creating Labverse™, a connected ecosystem of TechBios, CROs, and academic labs, making it super simple for biostartups to selectively collaborate with different partners. In the Labverse, connected labs within and across organizations can collaborate to design and run experiments. Labs can selectively exchange data and resources, for example:
Send data to CROs in the right machine-readable format by sharing Labsheets™ templates
Automatically receive data from CROs or other partners in the standardized format of choice
Set up auto-cleaning and fix data discrepancies with our smart flags
Easily merge CRO or external data with internal data without creating duplicates
Share non-proprietary data dictionaries and workflows with others
Outcomes of TechBio
Outcome 1: Standardize your protocols and bring your data in one spot to enable scalability.
Scispot has been widely adopted, becoming the go-to platform of many biotech startups and scaleups across North America and Europe, including Culture Biosciences, Talus Biosciences, Phase Biolabs, Micro Meat, CellChorus, PhenoSwitch Bioscience, AjaLabs, IgnyteBio, and many other growing biotechs. As a result, numerous biotechs have created their personalized LIMS with Scispot to eliminate data silos & standardize their workflows.
Lindsay Pino, Co-founder and CTO, Talus Bio, and the Talus Bio team, used Scispot to build a personalized proteomics sample manager.
Outcome 2: Reduce the variability in the research by leveraging artificial intelligence and machine learning to continuously learn from every new data point.
CellChorus is a dynamic TechBio startup. Their proprietary Time‑lapse Imaging Microscopy in Nanowell Grids (TIMING™) platform with neural network-based detection identifies individual immune cells and provides a comprehensive analysis of cell function, movement, and cell-cell interactions. CellChorus has built on Scispot since Day 1 to allow rapid scaling. With Scispot, CellChorus designs and standardizes research workflows and inventory management.
Outcome 3: Make your R&D programmable and repeatable TechBios use Scispot to create a library of templates for protocols, databases, processes, and integrations, so they can drag & drop these template blocks to design your experiments and workflows with lightning speed.
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