Oct 14

Oct 14 2024 Insights: iGEM Alumni Startups Funding Trends and SynBio x AI Innovations

by Meghiya Michelle and Fran Antequera (iGEM Startups)

2024 has been an incredible year for iGEM Startups, marked by notable funding trends among iGEM Alumni startups, particularly at the intersection of artificial intelligence (AI) and synthetic biology (SynBio) within our ecosystem. This year alone, seven alumni startups have secured over $95 million in funding—from government grants to multimillion-dollar investments—highlighting the scalability and market potential of these groundbreaking technologies.

iGEM Startups, as a SynBio pre-accelerator supporting both new ventures and established iGEM Alumni startups, we have witnessed a thriving landscape of innovation and growth. With the third-quarter ending and the conclusion of our flagship Venture Foundry program, we're seeing an increase in participation. A total of 81 startups joined our Venture Creation Labs, and more than 15 venture partners have collaborated with us in the Fast Track program, helping bridge the gap between investors and our startups.

iGEM Startups Internal Funding Insights

This year, a substantial portion of funding for iGEM startups has been directed toward the MedTech and HealthTech sectors. Compared to 2023, where four startups raised a total of $4.9 million, the 2024 landscape shows a more balanced distribution of $95 million across seven startups, with not only an increased number of startups receiving funding, but also in the diversity of the funding stages, ranging from government grants to Series B rounds. A JP Morgan report shows a similar trend, highlighting a significant rise in healthcare and MedTech investments in early 2024, with seed and Series A funding reaching $2.4 billion by June.

A key trend among these funded startups is the integration of AI in their core operations, such as diagnostics, drug discovery, and personalized medicine. For instance, LabGenius (iGEM Imperial 2011), focused on AI-driven protein drug discovery, secured £35 million in Series B funding, while BioMatter AI (iGEM Vilnius Lithuania 2017), which specializes in generative AI for enzyme design, raised €6.5 million in seed funding.

This adoption of AI in synthetic biology is accelerating, with the global generative AI in biology market valued at $74M in 2022, and projected to reach $363.37M by 2032, at a CAGR of 17.3%. Reflecting this momentum, in August 2024, the National Science Foundation recently awarded a $15 million grant to iBiofoundry, an initiative under University of Illinois Campagna, an initiative combining synbio, laboratory automation, and AI research.

AI and SynBio in General

“We consider that AI will play a pivotal role in transforming SynBio, and its integration will be crucial for companies in order to be competitive. This will have an impact in accelerating discovery, optimization and engineering of new products, in enhancing automation and scale-up, in improving data integration and interpretation, which will directly impact personalized medicine.”
- Gemma Guinart Mola, Principal at Invivo Partners

The convergence of synthetic biology and AI is proving to be a game-changer. Capgemini’s latest Bioeconomy report highlights AI's ability to address critical challenges in synbio, such as reducing costs, accelerating time-to-market, optimizing bioprocesses, and mitigating environmental and societal risks. Notably, 84% of corporate executives recognize AI’s role in driving bioinnovation, with predictions of increased CVC (Corporate Venture Capital) investment across diverse biosolution sectors.

However, as AI becomes more integrated into biosolutions, challenges remain—especially in the availability of foundational data needed for accurate predictions. The report indicates that 57% of corporations struggle with this data availability, and two-thirds face significant hurdles in optimizing bioprocesses using AI. Despite these challenges, 70% of corporate executives believe that generative AI can enhance the efficiency of R&D processes.

AI Adoption in SynBio: Insights from iGEM Startups Portfolio

Integrating AI into synthetic biology presents both immense opportunities and significant challenges for startups. Two pioneering iGEM Startups, Granza Bio and Plasmid AI, share their experiences and insights on navigating these complexities.

Granza Bio, an Oxford-based startup co-founded by Ashwin Jainarayanan (iGEM IISER Mohali 2016-2018), is innovating therapeutic delivery by using the human body’s natural attack particles to precisely target tissues for cancer treatments and genetic medicines. Having recently raised $7.1 million in seed funding in July 2024, Granza Bio emphasizes the critical role of AI in accelerating development processes.

Although we haven’t publicized, much of the backend development and target screening at Granza Bio heavily relies on the integration of large datasets and generative AI models. We view AI as an invaluable tool that accelerates our development processes by identifying critical patterns, thereby driving more innovative discoveries and therapeutic advancements. The progress we're witnessing now is merely the inception of this journey, with AI poised to empower every scientist, regardless of computational expertise, to effortlessly analyze and interpret complex data.
- Ashwin Jainarayanan, CSO of Granza Bio

Plasmid.AI, a Canadian startup spun out of iGEM Toronto and iGEM Startups 2024 cohort, is revolutionizing the development of phage therapies by eliminating the preclinical discovery phase, aiming to combat antibiotic resistance. Santiago, the CEO of Plasmid.AI emphasizes the opportunities and challenges ahead.

“As AI models continue to improve, we see a world of opportunities. One of the biggest limiting factors of molecular biology research is the lack of funding. We foresee AI allowing researchers to undertake more projects at a fraction of the cost, allowing underfunded labs to exponentially expedite their research through a “virtual lab sandbox". We see AI as an overpowered companion to a research scientist…In the distant future, we believe that research scientists can conduct novel biological research the same way an architect can design and test buildings in silico.
While AI models have gotten better for things like search and speech they’re not where we want them to be for more complex systems. In biology, we’re limited by the lack of good quality training data and an incomplete understanding of the underlying biology. As these challenges are overcome, we want to be positioned at the forefront to expedite the research process.
It may sound crazy, but we strongly believe that once these challenges are overcome, there will be the creation of “1-person iGEM teams”, where a single person can come up with a project and execute it entirely with the help of AI.”
- Santiago Plata Salazar, CEO of Plasmid.AI

SynBio x AI: A Startup Guide with VC Insights

Gemma Guinart Mola, Principal at Invivo Partners, a management company investing in early stage companies in the healthcare sector. Gemma shares her perspective regarding AI adoption in SynBio.

With many startups emphasizing AI in their biosolutions, but how do investors evaluate the effectiveness and scalability of these AI-driven innovations? What metrics are they using?

When evaluating AI-driven solutions in healthcare, Invivo Partners focus on several performance metrics:

Speed, accuracy and scalability: how quickly the AI can process data and generate meaningful insights; how precise its predictions or outputs are; and how feasible is to maintain performance while handling larger datasets or more complex tasks.
Data quality and integrity: the effectiveness of an AI system is heavily dependent on the data it uses. We focus on the type, quality, quantity and ownership of data. Proprietary data is often preferred, as they can provide a unique competitive advantage.
Proof-of-concept: a minimal PoC is crucial. We look for evidence that the AI algorithm can achieve meaningful results in a wet lab, i.e., identifying viable drug candidates that show effectiveness, at least in vitro.
Ability to find good solutions to the problem being tackled: synthetic biology involves the design and construction of different building blocks, which are often highly complex tasks. Therefore, the ability of the AI system to generalize (capacity to apply the learned knowledge into other new situations), develop emergent properties (spontaneous development of new properties derived from AI design choices) and predict behavior (ensure that the designed biological system functions as intended) is key.

What key challenges do you see startups facing when integrating AI into their biosolutions, and how can they overcome them?

Startups face several challenges when integrating AI to their biosolutions:

Data quality and quantity: it is important to use relevant data that can turn into meaningful insights. Therefore, startups must focus on data curation and harmonization, ensuring that the data being used is relevant and representative of the patient population being addressed.
Validation: AI models must be validated through preclinical and clinical trials or real-world evidence to prove their efficacy and safety in the intended clinical context. Startups should engage early in the process with healthcare professionals to design robust validation.
Integration into clinical workflows: startups should also work closely with clinicians during the development to ensure that AI tools are user-friendly, fit current systems and provide value in the decision-making process without negatively interfering with the daily clinical practice.
Competitive landscape: startups developing specialized AI models must focus on differentiating themselves from established AI giants, who often use general models. Working on this differentiation is crucial for startups to emphasize their position and demonstrate the value of their specialized solutions in a crowded market.

What advice would you give to early-stage startups in SynBio that are looking to leverage AI to attract investment?

Startups in the SynBio space need to focus on defining a strong unique selling proposition and in the generation of data supporting it. For companies developing technological platforms, it is important to have a clear development pipeline. In addition, establishing strategic partnerships with pharmaceutical companies and research institutions can provide additional validation and credibility to the system being used. Furthermore, such collaborations may offer opportunities for early monetization, demonstrating the value of the technology being developed. Finally, building a strong team with relevant expertise is critical. Startups need to ensure that their team includes experts in both SynBio and AI.

AI is transforming SynBio by accelerating R&D processes and optimizing bioprocesses, but challenges like limited high-quality data and the complexity of modeling biological systems persist. Current AI capabilities and data processing are still insufficient, often requiring AI x SynBio startups to build solutions from scratch. Overcoming these barriers will require strategic collaborations between startups and larger corporations to leverage resources, enhance data access, and scale AI-driven innovations, positioning these ventures to capitalize on the faster phase.

This year, iGEM Startups will showcase the ventures that have gone through our Venture Foundry program at the iGEM Grand Jamboree in Paris, Porte de Versailles. Six out of the 15 startups pitching at the Startup Showcase are working in the HealthTech and MedTech sectors, with two of them are in AI sectors. Read more about the top sectors of the year and join our discussion at the BioInnovation Fair. Meet the future leaders of the SynBio economy and discover what lies ahead!