Customize SciSure to work your way

Cambridge, MA — July 10, 2025 — SciSure, formerly SciShield and eLabNext, is proud to announce the formal sponsorship of The Engine, a nonprofit founded by MIT to  incubate and accelerate early-stage Tough Tech companies from idea to impact. This sponsorship marks the next step in a partnership rooted in a shared mission: helping startups accelerate the development of life-changing technologies, treatments, and healthcare solutions.

SciSure was recently formed from the merger of SciShield and eLabNext. Both companies were longtime partners of The Engine: For years, SciShield supported The Engine with environmental health and safety (EHS) and compliance infrastructure, while eLabNext powered the digital backbone of many resident companies. Now, by joining forces, SciShield and eLabNext are expanding their support—giving Tough Tech entrepreneurs connected access to the critical digital tools, compliance frameworks, and operational resources they need to scale and succeed.

"We’ve seen firsthand how important the right infrastructure is for Tough Tech startups to reach the market," said Jon Zibell, VP of Global Alliances and Marketing for SciSure "By deepening our partnership with The Engine, we’re making it easier for innovators to focus on what matters most—delivering breakthroughs that have a lasting impact on society."

“Given the fact that science is moving more and more towards Tough Tech and Tech Bio (as in technology-first, using advanced computational tools like AI, ML, cloud, and data engineering), it is so important to be able to create an API/SDK-Powered ecosystem for scientists that know that we can support their platforms, automation, and scalability.” said Zareh Zurabyan, VP of Commercial, Americas for SciSure. 

Through this enhanced partnership, The Engine’s resident companies will gain:

• Integrated Digital Operations: Access to eLabNext’s Digital Lab Platform (DLP), combining ELN, LIMS, lab automation, and compliance tracking into one connected system.
• Built-in EHS and Compliance Support: SciShield’s platform ensures startups meet regulatory standards from day one, reducing risk and saving time.
• Early Access Incentives: Special access to tailored onboarding, incentives, and ongoing support for adopting SciShield and eLabNext solutions. 
• Best-in-Class Software: A full suite of software solutions for Scientists, LabOps, and EHS to ensure the highest probability of achieving commercial success and making a lasting impact.
• Education and Collaboration: New opportunities for startups to participate in co-hosted workshops, hands-on training, and knowledge-sharing sessions on lab digitalization, safety, and compliance.

“For years, SciSchield and eLabNext have provided critical infrastructure and resources to fuel the growth of our residents,” said Alexa Monti, Vice President of Business Development and Strategic Partnerships at The Engine. “We are now thrilled to welcome their sponsorship as SciSure. Partnerships like this are essential to building a resilient innovation ecosystem that enables founders to transform breakthrough ideas into world-changing impact.”

About The Engine:

The Engine is a nonprofit incubator and accelerator dedicated to supporting early-stage Tough Tech companies tackling the world's greatest challenges. Founded by MIT in 2016, The Engine provides critical support for Tough Tech companies in the form of specialized lab and fabrication infrastructure, programming and mentorship, and an ecosystem of experts and investors. By bridging the gap between groundbreaking ideas and real-world impact, The Engine plays a crucial role in advancing solutions to complex global issues. For more information, visit engine.xyz

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Media Contact: 

press@engine.xyz

Media Contact:

Jon Zibell
Vice President of Global Alliances & Marketing
‍J.Zibell@scisure.com

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Over the past decade, the life sciences industry has undergone a foundational transformation, one that redefines how biology is explored, understood, and scaled. 

Traditional, wet-lab-driven biotechnology has shifted toward TechBio, a new model grounded in software engineering, data architecture, and AI-native platforms. This transition replaces linear, hypothesis-driven experimentation with computationally designed, automated, and feedback-driven discovery systems.

Whereas classical biotech workflows revolved around physical assays and post-hoc data interpretation, TechBio organizations prioritize upstream data infrastructure, machine learning (ML)-ready outputs, and modular technology stacks from the outset. 

Bioinformaticians have transitioned from support roles to strategic leadership, while wet lab execution has become programmable. Digital platforms, such as ELN/LIMS ecosystems, have become mission-critical infrastructure. Interdisciplinary teams spanning biology, data science, and software engineering collaborate in product-oriented models, similar to those found in SaaS companies. AI tools also augment decision-making at every stage, from target identification to manufacturing.

This guide outlines what TechBio is and ten defining pillars, providing detailed examples. It explores how TechBio organizations engineer not only therapies, but the digital systems that discover, refine, and validate them. 

As the boundary between biology and computation dissolves, TechBio positions itself as the operating system of 21st-century life sciences, offering a blueprint for faster, more scalable, and reproducible scientific innovation.

Welcome to TechBio, where biology meets software engineering, and the future is being coded before it's cultured.

What is TechBio?

TechBio is the convergence of biology, software engineering, and AI, replacing traditional wet-lab workflows with computationally driven, programmable discovery systems. It reimagines life sciences as a scalable, data-centric platform where interdisciplinary teams and digital infrastructure power faster, more reproducible innovation.

10 Pillars Defining the TechBio Transition

1. Data Architecture Before Wet Work

Traditional biotech began with the bench: Run experiments, generate data, and interpret the results later. 

In TechBio, the inverse is true. Teams now design the data schema, ontology, and analysis pipeline first, enabling smart experiment design, ML-ready outputs, and scalable platforms that can adapt over time.

If your data isn't structured for insight on Day 1, you're already behind. Here are some examples:

• Companies like Recursion Pharmaceuticals use imaging and structured data pipelines before any therapeutic hypothesis is even proposed.
• GLP-1 analog research is now modeled using pre-trained metabolic models that simulate downstream effects before synthesis.
• New CRISPR libraries are generated and screened in silico with predictive scoring models, optimizing guide RNAs based on large omics datasets.

This inversion – starting with data design before experimentation – has reoriented R&D pipelines around long-term scalability. As structured data becomes a strategic asset, TechBio companies are increasingly valued not just for their scientific breakthroughs but for the reusability of their data layers. This has profound implications for platform business models, partnerships, and cross-study insights.

2. AI-First vs. Hypothesis-First

Biotech works in a sequential logic: form a hypothesis, test it in an in vitro model, and iterate. 

TechBio builds AI-native systems that surface insights and correlations before human hypotheses even form, accelerating discovery.

The AI isn't replacing the scientist; it's augmenting their intuition at scale. Examples include:

• Insitro and Inceptive are generating drug candidates with ML from genetic/phenotypic data, especially in diseases like ALS and obesity.
• CRISPR screening now uses AI to predict essential gene targets before experiments, significantly reducing the time-to-lead.

The shift from hypothesis-driven to AI-augmented discovery marks a turning point in biological research. Rather than replacing scientists, AI now operates as a collaborative engine that surfaces new dimensions of correlation and causation. The competitive edge is shifting toward organizations that can orchestrate this human–machine loop efficiently, striking a balance between statistical signals and biological plausibility.

3. Platform Engineering as a Core Competency

In TechBio, companies aren’t just developing drugs; they're developing software platforms that standardize workflows, integrate third-party tools, and turn fragmented research into reproducible systems. 

Internal data platforms, LIMS/ELN integrations, and ML pipelines are essential baseline technologies for competitiveness. Some real-world examples include:

• Ginkgo Bioworks builds reusable cell engineering platforms akin to APIs for cells.
• Arda Therapeutics has created a modular inflammation discovery stack from single-cell data, standardizing the computational exploration of immune pathways and cells.

The rise of internal engineering teams and reusable software platforms in the life sciences mirrors the evolution of the tech industry. Platformization allows TechBio companies to rapidly launch programs across therapeutic areas, onboard partners, and generate real-time feedback loops. The result is a higher innovation velocity and better capital efficiency, traits that investors and pharmaceutical partners increasingly favor.

4. Bioinformaticians Are the New Bench Scientists

In a TechBio org, the bioinformatician is no longer “behind the scenes;” they're core to strategy, productization, and decision-making. Teams prioritize hires who can extract signal from noise, build predictive models, and interface with both biologists and back-end engineers. Bioinformaticians are now tasked with defining the experiment design, not just analyzing results after the fact.
As the bottleneck in modern biology moves from experimentation to interpretation, bioinformaticians have emerged as essential architects of discovery. Organizations that empower computational biology as a front-line discipline – not a downstream service – are demonstrating faster time-to-insight, better target validation, and smarter trial design. Talent acquisition in this field is now a core strategic priority.

5. Composable Lab Tech Stacks

Gone are the days of rigid, siloed lab systems. TechBio demands modular, API-connected ecosystems that allow seamless integration between ELN, LIMS, data lakes, assay instruments, and cloud analysis tools. 

Composability – the ability to select, assemble, and reconfigure components, such as services, modules, or APIs – is the new competitive advantage.

Top TechBio orgs are building integrated ecosystems where ELN, LIMS, and assay data sync in real-time, reducing batch errors and improving reproducibility. Composable architecture transforms labs from siloed environments into interoperable, cloud-connected ecosystems. 

This flexibility enables rapid tool swapping, real-time data syncing, and scalable digital operations. As composability becomes a prerequisite, the market is shifting toward vendors and platforms that emphasize integration, standardization, and cross-domain orchestration.

6. Experimental Automation as Software

Wet lab automation has evolved beyond the use of robotic arms. Now it’s programmable. TechBio teams treat lab execution as code: Experiments are version-controlled and modularized, making them reproducible across different geographical locations. Strateos and Emerald Cloud Lab are commercial examples of how this can work, letting scientists run remote assays, QC, and sample processing with code.
By treating lab execution as programmable infrastructure, TechBio closes the loop between in silico design and in vitro execution. Automation not only accelerates throughput but also unlocks a new paradigm of version-controlled science, where reproducibility and traceability become codified. The winners in this space will be those who can abstract biology into code without sacrificing fidelity.

7. Interdisciplinary Product Teams

TechBio orgs are structured like SaaS companies. Product managers, software engineers, data scientists, and bench biologists all contribute to the strategic path for products. Product-market fit isn’t just about efficacy; it’s about workflow usability, data interoperability, and analytical scalability. 

Dyno Therapeutics, a company using AI to discover and optimize better delivery of gene therapies, employs product managers and ML leads alongside virologists to design AAV capsid platforms with specific tropisms.
The productization of science, where multi-disciplinary teams own features, roadmaps, and outcomes, blurs the lines between R&D and product development. TechBio teams now operate like agile startups, iterating on therapeutic designs with the same velocity and feedback mechanisms as SaaS companies. This accelerates both discovery and market alignment, reducing the translational lag between R&D and impact.

8. Open Science Meets IP-Protected Infrastructure

Rather than hoarding findings in PDFs or publications, TechBio companies publish datasets, APIs, and tools while protecting their insights via proprietary ML models and data platforms. It's not just about the molecule or target; it's about the ecosystem that discovers it.

TechBio is redefining the balance between openness and defensibility. By releasing tools and datasets while protecting the infrastructure that operationalizes them, companies can build communities, accelerate adoption, and establish defensible moats around proprietary layers. This hybrid approach to IP strategy mirrors the open-core model in software and is fast becoming the norm in science-forward organizations.

9. AI-Augmented Decision-Making in R&D

From target identification to trial design, AI is infused across the R&D lifecycle. NLP models extract insights from literature, generative models design protein structures, and predictive models flag risks before they manifest. For example:

• GLP-1 and incretin research is being accelerated by multimodal AI models that predict cardiometabolic response based on genetic and dietary data.
• CRISPR off-target prediction tools, such as DeepCRISPR and CRISPR-Net, minimize risk before editing begins.

From discovery to development to manufacturing, TechBio companies are using predictive models to make faster, more informed decisions. This transition lowers risk, reduces cost, and improves outcomes, positioning AI-augmented pipelines as the gold standard for next-generation therapeutics.

10. Speed, Scale, and Signal

TechBio companies operate on startup timelines, not scientific timelines. They use cloud infrastructure, continuous data streaming, and rapid feedback loops to compress cycle times from months to days. Signal extraction and throughput are the key metrics. What used to take 18 months in a wet lab now happens in 6 weeks via computational modeling and robotic execution.

By adopting cloud infrastructure, continuous experimentation, and agile pipelines, companies can reduce the cycle time from question to answer and from idea to impact. As signal extraction becomes the metric that defines productivity, organizations are now judged by how efficiently they can learn, not just how much they can test.

The Venture Capital (VC) & Private Equity (PE) Outlook: Why TechBio Is the New Investor Mandate

The TechBio transition has fundamentally reshaped investor psychology in life sciences. Where traditional biotech relied on long timelines, binary risk, and molecule-centric valuations, today’s VC and PE firms are seeking software-first, platform-oriented, and AI-native biology companies that exhibit repeatable innovation, scalability, and enterprise value beyond a single therapeutic asset.

Key Investment Trends Driving Capital Deployment in TechBio

The biotech investment landscape is shifting, with VC deployment accelerating in late 2025 and favoring AI-native, TechBio firms modeled after high-growth SaaS companies. Private equity is moving away from traditional biotech roll-ups toward digital-first infrastructure plays, such as LIMS and automation platforms. Valuations are compressing for single-asset biotech firms but expanding for multi-modal platforms with in-house AI/ML capabilities. IPO and exit readiness now require both clinical and tech maturity, while firms lacking digital infrastructure face the greatest funding risk.

With TechBio firmly entrenched, here’s what the not-too-distant future looks like:

• Prioritizing Platform over Pipeline: Investors are favoring companies with data platforms or AI discovery engines that can generate multiple assets, rather than a single-drug pipeline. Look at Flagship and Andreessen Horowitz (a16z) continuing to back repeatable discovery systems, such as Generate Biomedicines and Inceptive, instead of molecule-first approaches.
• Computational Biology at a Premium: Companies with ML-native workflows, structured data ontologies, and in silico design capabilities are commanding higher valuations. Recursion Pharmaceuticals’ IPO and valuation, for example, were tied more to its image-based AI infrastructure than its lead program.
• Cross-Disciplinary Teams as a Signal of Quality: Interdisciplinary founding teams that blend machine learning, systems biology, and engineering are seen as higher-execution risk mitigators. PE firms are increasingly conducting technical due diligence not just on pipelines, but also on the infrastructure stack, data operations, and software engineering.
• Shift Toward B2B and SaaS Models in Life Sciences: A wave of investment is flowing into companies that serve the TechBio ecosystem, including cloud-native LIMS/ELN platforms, computational CROs, and automated lab systems. These provide recurring revenue, faster sales cycles, and infrastructure lock-in, metrics that closely align with the tech sector's investment benchmarks.
• AI as a Defensibility Layer: VCs are heavily weighing proprietary AI models as part of the IP moat. It's no longer enough to own a sequence; firms must own the system that designs or predicts the sequence. Investors now look at data exclusivity, model performance, API extensibility, partner integrations, and model improvement over time.

TechBio: Redefining the Future of the Life Sciences

The life sciences funding environment is undergoing the same disruption that has reshaped fintech, media, and cybersecurity: from asset-centric investing to platform- and systems-centric investing. As biology becomes programmable, investors no longer seek the best drug; they seek the best engine for discovering, designing, and optimizing drugs.

The overarching implications of this include a shift to:

• Hiring more software engineers and ML experts than lab techs
• VCs seeking platform-first models with recurring data assets
• Companies prioritizing cloud-native, ML-enabled workflows.
• Faster, reproducible, and AI-augmented discoveries.

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For companies, this means:

• Building infrastructure before pipelines
• Valuing reproducibility as a product
• Prioritizing software engineers and bioinformaticians as co-founders
• Designing business models around feedback loops, not just endpoints

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The leaders of this next chapter won’t just discover, they’ll design biology as an engineered system, built on platforms, powered by data, and scaled with AI.

While Biotech commercialized biology, TechBio will make biology computational.

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If you manage environmental health and/or safety (EHS) in a life science laboratory, you can set up safety protocols, and put up eye-spray stations that you test daily, but building a culture of compliance is a whole other can of worms. Furthermore, the link between EHS and sustainability efforts are not always top-of-mind for lab personnel.

So, how can you educate and compel researchers to be more sustainable?

In the modern lab, where digitalization continues to make research more efficient and streamlined, leveraging software platforms that centralize lab operations – including EHS and sustainability efforts – can help you avoid safety and sustainability mistakes that can cost millions of dollars and could lead to personnel injuries.

In this article, we’ll cover EHS in the life sciences, its link to sustainability,  how to enforce compliance digitally, leveraging tools such as ELN/LIMS, and the best practices for achieving this. Using a decentralized platform for EHS and other lab operations is a mistake you do not want to make, so we also introduce a new type of software tool for laboratories called a Scientific Management Platform (SMP), that combines EHS (especially Exposure Control), with your day-to-day ELN/LIMS.

Environment, Health, and Safety (EHS) in Life Sciences and Biotech

What is EHS?

EHS in life sciences and biotech involves policies and procedures designed to:

• Protect the environment from biohazards and chemical pollutants.
• Ensure employee health through safe practices and exposure controls.
• Maintain workplace safety in laboratories, manufacturing, and research facilities.

EHS Areas of Focus

Environmental Protection

• Waste Management: Disposal of biohazardous and chemical waste following strict guidelines.
• Energy Efficiency: Implementation of green lab initiatives to reduce energy use.
• Sustainable Practices: Incorporating biodegradable materials and reducing water use in lab operations.

Occupational Health

• Exposure Control: Limiting exposure to pathogens, hazardous chemicals, and radiation.
• Health Monitoring: Regular health screenings for employees working with toxic substances.
• Mental Well-being: Programs addressing stress in high-pressure research environments.

Workplace Safety

• Equipment Safety: Regular maintenance of autoclaves, centrifuges, and lab machinery.
• PPE Compliance: Ensuring the use of lab coats, gloves, goggles, and respirators where necessary.
• Emergency Readiness: Spill response training and protocols for accidents involving biological or chemical agents.

Top 10 EHS Examples in Life Sciences and Biotech

Academic and industry labs handle hazardous materials, dangerous equipment, and harmful biological agents. Effective EHS programs ensure compliance to mitigate the risks of these activities. Below are ten examples that highlight essential safety measures in these high-risk settings.

1. ‍Biowaste Management: Segregation and sterilization of biological waste.
2. ‍Fume Hood Usage: Safe handling of volatile chemicals and reagents.‍
3. Pathogen Containment: Use of biosafety cabinets for handling infectious agents.‍
4. Radiation Safety: Monitoring and control for radiological materials in research.‍
5. Chemical Inventory Systems: Tracking and proper storage of hazardous substances
6. ‍Ventilation Systems: Preventing air contamination in laboratories.‍
7. Incident Reporting: Documenting and analyzing lab accidents and near misses.‍
8. Ergonomic Lab Design: Preventing repetitive strain injuries among researchers.
9. ‍Hazard Communication: Clear labeling and documentation of chemical and biological hazards.
10. ‍Training Programs: Regular EHS training tailored to biotech-specific risks.

Real-World Applications of EHS

The examples above apply to various sectors within the industry, from R&D to large-scale manufacturing.

• Pharmaceutical R&D: Safely handling toxic reagents and ensuring GMP compliance.
• Biomanufacturing: Managing emissions and waste from large-scale bioreactors.
• Clinical Trials: Protecting researchers from potential pathogen exposure during specimen handling.
• Gene Therapy Labs: Strict protocols for handling genetically modified organisms (GMOs).

Benefits of EHS in Life Sciences and Biotech

By tailoring EHS practices to the unique challenges of life sciences and biotech, organizations can achieve safer and more compliant operations while driving innovation. The benefits of EHS compliance include:

• Ensures regulatory compliance (e.g., OSHA, WHO, IFC).
• Reduces environmental impact of high-tech lab activities.
• Enhances employee safety and operational efficiency.
• Builds public trust by prioritizing ethical and sustainable practices.

What is the Link Between EHS and Sustainability?

EHS provides a foundation for a laboratory or organizations sustainability practices, by mitigating the environmental impacts of research, ensuring worker safety, and promoting efficient use of resources. In this context, sustainability refers to the adoption of business practices that meet present needs without compromising the ability of future generations to meet theirs. It involves integrating environmental stewardship, social responsibility, and economic viability to achieve long-term success.

EHS Integration with Sustainability:

• Energy Use Reduction: Transitioning to energy-efficient lab equipment.
• Carbon Neutral Labs: Offsetting carbon emissions from lab processes.
• Water Recycling: Using closed-loop systems in biomanufacturing.

Sustainability Through Digitization: The Role of a Scientific Management Platform (SMP) in Labs and Institutions

Introduction to Lab Sustainability through Digitization

Sustainability in labs and research institutions extends beyond environmental conservation; it encompasses efficient resource use, minimizing waste, and adopting practices that ensure long-term viability.

Digitization, particularly through the adoption of Electronic Lab Notebooks (ELNs), Laboratory Information Management Systems (LIMS), or all-in-one SMPs plays a pivotal role in driving sustainability by reducing physical resource dependence and enhancing operational efficiency.

What is an SMP?

An SMP is a digital ecosystem that unifies lab operations, digital research, and EHS. It’s distinguished from other ELN and LIMS platforms in that it is a single platform that serves the needs of an entire life science organization, including scientists, lab operations professionals, EHS and compliance personnel, and leadership.

Impact of an SMP on Lab Sustainability

1. Reduction of Paper and Physical Resource Use

• Traditional Practices: Labs have historically relied on paper-based lab notebooks, forms, and records, leading to significant paper waste.
• Digitized Solution: SMPs, as well as ELNs and LIMS, eliminate the need for physical notebooks and documentation, significantly reducing paper usage. A large institution can save thousands of sheets of paper annually by transitioning to digital systems.

2. Streamlining Data Management

• Resource Optimization: SMPs, ELNs, and LIMS centralize data storage, eliminating redundancies and enhancing accessibility, thereby reducing the energy costs associated with manual data retrieval and storage.
• Cloud Integration: Many systems operate on energy-efficient cloud platforms, further reducing on-site energy consumption.

3. Decreasing Redundancy and Waste

• Inventory Control: SMPs and LIMS improve inventory management, ensuring efficient use of reagents and consumables, reducing overstocking, and minimizing expired materials.
• Experimentation Efficiency: SMPs and ELNs support accurate experiment tracking, preventing redundant trials and saving time and resources.

4. Energy Efficiency in Lab Operations

• Digitally Monitored Systems: SMPs and LIMS can integrate with lab equipment to optimize energy usage, such as monitoring freezers, incubators, and other devices.
• Scheduling and Maintenance: These platforms help schedule equipment usage and maintenance, reducing unnecessary energy consumption.

5. Waste Management and Compliance

• Traceability: LIMS and SMPs enhance the traceability of samples and reagents, ensuring proper disposal of hazardous materials and compliance with environmental regulations.
• Analytics: The analytics modules in these systems can identify wasteful practices and recommend more sustainable workflows.

6. Remote Collaboration and Access

• Virtual Collaboration: SMPs and ELNs enable remote data sharing and collaboration, reducing the need for physical presence and associated travel, contributing to lower carbon footprints.
• Global Integration: Institutions can collaborate globally without duplicating experiments or resources.

Impact of an SMP on Institutional Sustainability

1. Institution-Wide Standardization

Digital systems promote uniformity across labs, ensuring sustainable practices are maintained at every level. Standardization also enables new personnel to quickly and easily adopt standardized practices during onboarding.

2. Educational and Training Benefits

Digital platforms make sustainability training more effective by integrating real-time data tracking and eco-conscious decision-making.

3. Long-Term Cost Efficiency

Though initial implementation costs are high, digitization reduces operational costs in the long term by minimizing resource wastage and improving process efficiency.

4. Scalability and Growth

Digitized labs are better equipped to scale sustainably as they require fewer physical expansions and utilize resources more efficiently.

Impact of an SMP on Global Sustainability

1. Alignment with SDGs

Digitized labs contribute to the United Nations’ Sustainable Development Goals (SDGs) by promoting responsible consumption and production (Goal 12) and climate action (Goal 13).

2. Carbon Footprint Reduction

Digitally driven labs contribute to global carbon reduction efforts through energy efficiency, reduced paper use, and waste minimization.

3. Circular Economy Participation

By optimizing resources and ensuring traceability, labs using ELNs and LIMS align with the principles of a circular economy.

Driving Safety and Sustainability Through Lab Digitalization

Digital tools, like SMPs, are transforming life science labs and organizations by enhancing safety, sustainability, and efficiency. As EHS initiatives align with sustainability goals, platforms like ELNs, LIMS, and SMPs streamline compliance, reduce waste, and optimize operations.

SMPs, in particular, centralize lab management, ensuring resource efficiency, data traceability, and proactive safety measures. By embracing digital transformation, labs can foster a culture of sustainability, minimize environmental impact, and enhance operational excellence, positioning themselves for long-term success in an evolving scientific landscape.

To learn more about SMPs and their role in lab safety and sustainability, contact us.

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