Join us in
04 Days 06 H 56 Min 18 Sec
Join us in
04 Days 06 H 56 Min 18 Sec
Hyperscale AIxBIO
AI is Rewriting the Rules of Biology and The Future is Programmable
May 5-8
2025
San Jose Convention Center
California, USA
May 5-8
2025
San Jose Convention Center
California, USA



About the event
From foundational AI and machine learning to DNA, RNA, and amino acid language models, a new wave of innovation is reshaping biology. These tools are powering everything from enzyme and protein engineering to computational pipelines for novel enzymes, polymers, food ingredients, drug discovery, antibody development, and engineered cell therapies.
At the cutting edge of bio + tech, scientists are tackling core challenges—scaling data generation, overcoming data scarcity, and benchmarking AI models to unlock new breakthroughs. Diffusion models, automated science, and longevity research are just the beginning.
Join the scientists, technologists, and entrepreneurs redefining what's possible when AI meets Biology—and Biology meets intelligence.
Top Sessions
1
-
Unbound Biology: The Next Era of (Bio)Computing
The future of computing is being rewritten by biology. In this landmark session, Axios Managing Editor for Science & World, Alison Snyder, sits down with Stanford professor and synthetic biology pioneer Drew Endy and Microsoft CTO Kevin Scott to explore how programming living systems will transform the architecture of innovation. From designing cells with logic and memory to harnessing biological systems for sensing, computation, and decision-making, biology is becoming a powerful substrate for information processing. Join us for a forward-looking conversation on the convergence of synthetic biology and computing—and what it means for the future of technology, medicine, and planetary health.
[…]
2
-
Rewriting Life’s Code to Create New Polymers, Materials, and Medicines
In terrestrial life, DNA is copied to messenger RNA, and the 64 triplet codons in messenger RNAs are decoded – in the process of translation – to synthesize proteins. Cellular protein translation provides the ultimate paradigm for the synthesis of long polymers of defined sequence and composition but is commonly limited to polymerizing the 20 canonical amino acids. I will describe our progress towards the encoded synthesis of non-canonical biopolymers. These advances may form a basis for new classes of genetically encoded polymeric materials and medicines. To realize our goals, we are re-imagining some of the most conserved features of the cell; we have created new ribosomes, new aminoacyl-tRNA synthetase/tRNA pairs, and organisms with entirely synthetic genomes in which we have re-written the genetic code.
[…]
3
-
AI-Driven Breakthroughs: Accelerating Drug Discovery and Genetic Medicine
In an era where AI is transforming medicine, drug discovery and genetic therapies are entering a new frontier. This session will explore how AI accelerates the development of innovative therapies, from optimizing molecular designs to enabling precise, tissue-specific genetic interventions. Discover how cutting-edge AI technologies are advancing therapeutic modalities, enhancing delivery systems, and unlocking the potential of personalized medicine. Join leading experts as they share groundbreaking insights into AI-driven innovation and its profound impact on the future of drug discovery and genetic medicine.
[…]
4
-
Hyperscale Biology: Designing Intelligence in Molecules
What happens when the architect behind the Transformer turns his focus to RNA and molecular design? Jakob Uszkoreit, Co-Founder of Inceptive and co-author of Attention is All You Need, joins John Cumbers, founder and CEO of SynBioBeta, for a conversation at the intersection of AI and biology. As synthetic biology enters the age of hyperscale—powered by generative models, molecular learning, and programmable therapeutics—Jakob shares his vision for reimagining intelligence not just in machines, but in the molecules that power life. Don’t miss this rare dialogue on the future of biodesign, computation, and the growing interface between synthetic biology and deep learning.
[…]
5
-
AI x RNA: Foundation Models for Rational Drug Design
Atomic AI operates at the convergence of artificial intelligence and RNA drug discovery — two revolutionary fields with significant potential, yet not without challenges. In developing an RNA foundation model for rational drug design, Atomic pioneered novel approaches to generate, process, and model RNA data (structure, function, and interactions) from first principles. This talk will reveal their methodological journey, critical insights gained, and identify persistent gaps in the broader field that currently constrain AI's capacity to transform drug discovery.
[…]
6
-
AI-Driven Insights: Predicting Efficacy and Toxicity in Next-Gen Drug Discovery
AI is revolutionizing drug discovery—not just by accelerating timelines, but by enhancing our ability to predict how drugs behave in the body. This session highlights cutting-edge approaches like dynamic systems simulations that model mechanistic interactions at the cellular level. Learn how platforms are able to generating causal hypotheses to forecast both efficacy and toxicity, unlocking smarter, safer therapeutic development.
[…]
7
-
Next-Gen Medicine: AI-Powered Advances Reshaping Human Therapeutics
Dive into the cutting-edge today, not just the promises of tomorrow. This dynamic breakout session features biotech visionaries actively leveraging machine learning and AI to transform the engineering of human therapeutics. Hear firsthand from industry pioneers pushing the boundaries—from accelerating drug target discovery to streamlining clinical development pipelines. Learn how advanced analytics and big data are already enhancing personalized medicine, boosting clinical success rates, and rapidly reshaping the future landscape of healthcare. Don't miss this inside look at the real-world innovations redefining what's possible in therapeutic discovery and precision medicine.
[…]
8
-
Machine Learning and Multiplexed Libraries for Large-Scale Synthetic Biology
Every major AI advance was built on massive amounts of data. ChatGPT was built on top of the Internet, new coding agents train on millions of public codebases, and AlphaFold leveraged the Protein DataBank. Learn about the ways that synthetic biologists are leveraging the combination of DNA sequencing and synthesis to fuel new modeling advances.
[…]
9
-
Automating Biology Research with an AI Scientist
What if the next breakthrough biologist isn’t human? In this spotlight talk, Sam Rodriques, founder of Future House, shares his vision for an AI scientist that can generate hypotheses, design experiments, and accelerate discovery in biology. As automation and machine learning reshape the scientific method, Future House is pioneering a new model for research—where algorithms, not just researchers, drive innovation. Join this session for a look inside the lab of the future, where biology is accelerated by intelligence at machine speed.
[…]
10
-
Assessing Progress in AI for Protein Engineering: Where Are We Now, and Where Are We Going?
Protein engineering is the most active frontier for AI in biotech. We are seeing the emergence of models that can make "zero shot" predictions of antibodies, and new variants of enzymes that would take millions of years for Evolution to produce. How should we be evaluating these models and their designs? What can we expect from the field of AI protein design moving forward?
[…]
11
-
Genome Modeling and Design Across All Domains of Life
All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation—from noncoding pathogenic mutations to clinically significant BRCA1 variants—without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon–intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.
[…]
12
-
AI 'Cheat Codes' Speed Therapeutic Cell Creation via Transcription Factors
One of the biggest challenges faced by the regenerative medicine field today is the ability to efficiently and scalably generate therapeutic cells. This session explores cutting-edge transcription factor-based approaches for rapid and efficient differentiation of stem cells into therapeutic cell types. We will examine how synthetic biology techniques are revolutionizing cell therapy by enabling faster production of clinically relevant cells. Speakers will discuss recent advances, challenges, and future directions in harnessing transcription factors for directed differentiation, with a focus on applications in regenerative medicine and drug discovery.
[…]
13
-
From Data to Delivery: Harnessing AI-Driven Tools for Next-Generation Gene Therapies
A new era in synthetic biology is unfolding as AI, genome sequencing, and DNA synthesis technologies come together to unlock breakthroughs once considered unattainable. In gene therapy, AI is poised to address one of the most pressing and costly hurdles of the field: efficient and targeted gene delivery. This talk will examine how advanced, AI-enabled approaches to synthetic biology for gene delivery are transcending the limitations of naturally occurring vectors, broadening tissue targeting to organs such as the eye, muscle, and brain, and accelerating the path from concept to clinical success. Attendees will gain actionable insights into harnessing these AI-driven tools to improve patient access, reduce costs, and usher in a new generation of transformative genomic medicines.
[…]
1
-
Unbound Biology: The Next Era of (Bio)Computing
The future of computing is being rewritten by biology. In this landmark session, Axios Managing Editor for Science & World, Alison Snyder, sits down with Stanford professor and synthetic biology pioneer Drew Endy and Microsoft CTO Kevin Scott to explore how programming living systems will transform the architecture of innovation. From designing cells with logic and memory to harnessing biological systems for sensing, computation, and decision-making, biology is becoming a powerful substrate for information processing. Join us for a forward-looking conversation on the convergence of synthetic biology and computing—and what it means for the future of technology, medicine, and planetary health.
[…]
2
-
Rewriting Life’s Code to Create New Polymers, Materials, and Medicines
In terrestrial life, DNA is copied to messenger RNA, and the 64 triplet codons in messenger RNAs are decoded – in the process of translation – to synthesize proteins. Cellular protein translation provides the ultimate paradigm for the synthesis of long polymers of defined sequence and composition but is commonly limited to polymerizing the 20 canonical amino acids. I will describe our progress towards the encoded synthesis of non-canonical biopolymers. These advances may form a basis for new classes of genetically encoded polymeric materials and medicines. To realize our goals, we are re-imagining some of the most conserved features of the cell; we have created new ribosomes, new aminoacyl-tRNA synthetase/tRNA pairs, and organisms with entirely synthetic genomes in which we have re-written the genetic code.
[…]
3
-
AI-Driven Breakthroughs: Accelerating Drug Discovery and Genetic Medicine
In an era where AI is transforming medicine, drug discovery and genetic therapies are entering a new frontier. This session will explore how AI accelerates the development of innovative therapies, from optimizing molecular designs to enabling precise, tissue-specific genetic interventions. Discover how cutting-edge AI technologies are advancing therapeutic modalities, enhancing delivery systems, and unlocking the potential of personalized medicine. Join leading experts as they share groundbreaking insights into AI-driven innovation and its profound impact on the future of drug discovery and genetic medicine.
[…]
4
-
Hyperscale Biology: Designing Intelligence in Molecules
What happens when the architect behind the Transformer turns his focus to RNA and molecular design? Jakob Uszkoreit, Co-Founder of Inceptive and co-author of Attention is All You Need, joins John Cumbers, founder and CEO of SynBioBeta, for a conversation at the intersection of AI and biology. As synthetic biology enters the age of hyperscale—powered by generative models, molecular learning, and programmable therapeutics—Jakob shares his vision for reimagining intelligence not just in machines, but in the molecules that power life. Don’t miss this rare dialogue on the future of biodesign, computation, and the growing interface between synthetic biology and deep learning.
[…]
5
-
AI x RNA: Foundation Models for Rational Drug Design
Atomic AI operates at the convergence of artificial intelligence and RNA drug discovery — two revolutionary fields with significant potential, yet not without challenges. In developing an RNA foundation model for rational drug design, Atomic pioneered novel approaches to generate, process, and model RNA data (structure, function, and interactions) from first principles. This talk will reveal their methodological journey, critical insights gained, and identify persistent gaps in the broader field that currently constrain AI's capacity to transform drug discovery.
[…]
6
-
AI-Driven Insights: Predicting Efficacy and Toxicity in Next-Gen Drug Discovery
AI is revolutionizing drug discovery—not just by accelerating timelines, but by enhancing our ability to predict how drugs behave in the body. This session highlights cutting-edge approaches like dynamic systems simulations that model mechanistic interactions at the cellular level. Learn how platforms are able to generating causal hypotheses to forecast both efficacy and toxicity, unlocking smarter, safer therapeutic development.
[…]
7
-
Next-Gen Medicine: AI-Powered Advances Reshaping Human Therapeutics
Dive into the cutting-edge today, not just the promises of tomorrow. This dynamic breakout session features biotech visionaries actively leveraging machine learning and AI to transform the engineering of human therapeutics. Hear firsthand from industry pioneers pushing the boundaries—from accelerating drug target discovery to streamlining clinical development pipelines. Learn how advanced analytics and big data are already enhancing personalized medicine, boosting clinical success rates, and rapidly reshaping the future landscape of healthcare. Don't miss this inside look at the real-world innovations redefining what's possible in therapeutic discovery and precision medicine.
[…]
8
-
Machine Learning and Multiplexed Libraries for Large-Scale Synthetic Biology
Every major AI advance was built on massive amounts of data. ChatGPT was built on top of the Internet, new coding agents train on millions of public codebases, and AlphaFold leveraged the Protein DataBank. Learn about the ways that synthetic biologists are leveraging the combination of DNA sequencing and synthesis to fuel new modeling advances.
[…]
9
-
Automating Biology Research with an AI Scientist
What if the next breakthrough biologist isn’t human? In this spotlight talk, Sam Rodriques, founder of Future House, shares his vision for an AI scientist that can generate hypotheses, design experiments, and accelerate discovery in biology. As automation and machine learning reshape the scientific method, Future House is pioneering a new model for research—where algorithms, not just researchers, drive innovation. Join this session for a look inside the lab of the future, where biology is accelerated by intelligence at machine speed.
[…]
10
-
Assessing Progress in AI for Protein Engineering: Where Are We Now, and Where Are We Going?
Protein engineering is the most active frontier for AI in biotech. We are seeing the emergence of models that can make "zero shot" predictions of antibodies, and new variants of enzymes that would take millions of years for Evolution to produce. How should we be evaluating these models and their designs? What can we expect from the field of AI protein design moving forward?
[…]
11
-
Genome Modeling and Design Across All Domains of Life
All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation—from noncoding pathogenic mutations to clinically significant BRCA1 variants—without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon–intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.
[…]
12
-
AI 'Cheat Codes' Speed Therapeutic Cell Creation via Transcription Factors
One of the biggest challenges faced by the regenerative medicine field today is the ability to efficiently and scalably generate therapeutic cells. This session explores cutting-edge transcription factor-based approaches for rapid and efficient differentiation of stem cells into therapeutic cell types. We will examine how synthetic biology techniques are revolutionizing cell therapy by enabling faster production of clinically relevant cells. Speakers will discuss recent advances, challenges, and future directions in harnessing transcription factors for directed differentiation, with a focus on applications in regenerative medicine and drug discovery.
[…]
13
-
From Data to Delivery: Harnessing AI-Driven Tools for Next-Generation Gene Therapies
A new era in synthetic biology is unfolding as AI, genome sequencing, and DNA synthesis technologies come together to unlock breakthroughs once considered unattainable. In gene therapy, AI is poised to address one of the most pressing and costly hurdles of the field: efficient and targeted gene delivery. This talk will examine how advanced, AI-enabled approaches to synthetic biology for gene delivery are transcending the limitations of naturally occurring vectors, broadening tissue targeting to organs such as the eye, muscle, and brain, and accelerating the path from concept to clinical success. Attendees will gain actionable insights into harnessing these AI-driven tools to improve patient access, reduce costs, and usher in a new generation of transformative genomic medicines.
[…]
1
-
Unbound Biology: The Next Era of (Bio)Computing
The future of computing is being rewritten by biology. In this landmark session, Axios Managing Editor for Science & World, Alison Snyder, sits down with Stanford professor and synthetic biology pioneer Drew Endy and Microsoft CTO Kevin Scott to explore how programming living systems will transform the architecture of innovation. From designing cells with logic and memory to harnessing biological systems for sensing, computation, and decision-making, biology is becoming a powerful substrate for information processing. Join us for a forward-looking conversation on the convergence of synthetic biology and computing—and what it means for the future of technology, medicine, and planetary health.
[…]
2
-
Rewriting Life’s Code to Create New Polymers, Materials, and Medicines
In terrestrial life, DNA is copied to messenger RNA, and the 64 triplet codons in messenger RNAs are decoded – in the process of translation – to synthesize proteins. Cellular protein translation provides the ultimate paradigm for the synthesis of long polymers of defined sequence and composition but is commonly limited to polymerizing the 20 canonical amino acids. I will describe our progress towards the encoded synthesis of non-canonical biopolymers. These advances may form a basis for new classes of genetically encoded polymeric materials and medicines. To realize our goals, we are re-imagining some of the most conserved features of the cell; we have created new ribosomes, new aminoacyl-tRNA synthetase/tRNA pairs, and organisms with entirely synthetic genomes in which we have re-written the genetic code.
[…]
3
-
AI-Driven Breakthroughs: Accelerating Drug Discovery and Genetic Medicine
In an era where AI is transforming medicine, drug discovery and genetic therapies are entering a new frontier. This session will explore how AI accelerates the development of innovative therapies, from optimizing molecular designs to enabling precise, tissue-specific genetic interventions. Discover how cutting-edge AI technologies are advancing therapeutic modalities, enhancing delivery systems, and unlocking the potential of personalized medicine. Join leading experts as they share groundbreaking insights into AI-driven innovation and its profound impact on the future of drug discovery and genetic medicine.
[…]
4
-
Hyperscale Biology: Designing Intelligence in Molecules
What happens when the architect behind the Transformer turns his focus to RNA and molecular design? Jakob Uszkoreit, Co-Founder of Inceptive and co-author of Attention is All You Need, joins John Cumbers, founder and CEO of SynBioBeta, for a conversation at the intersection of AI and biology. As synthetic biology enters the age of hyperscale—powered by generative models, molecular learning, and programmable therapeutics—Jakob shares his vision for reimagining intelligence not just in machines, but in the molecules that power life. Don’t miss this rare dialogue on the future of biodesign, computation, and the growing interface between synthetic biology and deep learning.
[…]
5
-
AI x RNA: Foundation Models for Rational Drug Design
Atomic AI operates at the convergence of artificial intelligence and RNA drug discovery — two revolutionary fields with significant potential, yet not without challenges. In developing an RNA foundation model for rational drug design, Atomic pioneered novel approaches to generate, process, and model RNA data (structure, function, and interactions) from first principles. This talk will reveal their methodological journey, critical insights gained, and identify persistent gaps in the broader field that currently constrain AI's capacity to transform drug discovery.
[…]
6
-
AI-Driven Insights: Predicting Efficacy and Toxicity in Next-Gen Drug Discovery
AI is revolutionizing drug discovery—not just by accelerating timelines, but by enhancing our ability to predict how drugs behave in the body. This session highlights cutting-edge approaches like dynamic systems simulations that model mechanistic interactions at the cellular level. Learn how platforms are able to generating causal hypotheses to forecast both efficacy and toxicity, unlocking smarter, safer therapeutic development.
[…]
7
-
Next-Gen Medicine: AI-Powered Advances Reshaping Human Therapeutics
Dive into the cutting-edge today, not just the promises of tomorrow. This dynamic breakout session features biotech visionaries actively leveraging machine learning and AI to transform the engineering of human therapeutics. Hear firsthand from industry pioneers pushing the boundaries—from accelerating drug target discovery to streamlining clinical development pipelines. Learn how advanced analytics and big data are already enhancing personalized medicine, boosting clinical success rates, and rapidly reshaping the future landscape of healthcare. Don't miss this inside look at the real-world innovations redefining what's possible in therapeutic discovery and precision medicine.
[…]
8
-
Machine Learning and Multiplexed Libraries for Large-Scale Synthetic Biology
Every major AI advance was built on massive amounts of data. ChatGPT was built on top of the Internet, new coding agents train on millions of public codebases, and AlphaFold leveraged the Protein DataBank. Learn about the ways that synthetic biologists are leveraging the combination of DNA sequencing and synthesis to fuel new modeling advances.
[…]
9
-
Automating Biology Research with an AI Scientist
What if the next breakthrough biologist isn’t human? In this spotlight talk, Sam Rodriques, founder of Future House, shares his vision for an AI scientist that can generate hypotheses, design experiments, and accelerate discovery in biology. As automation and machine learning reshape the scientific method, Future House is pioneering a new model for research—where algorithms, not just researchers, drive innovation. Join this session for a look inside the lab of the future, where biology is accelerated by intelligence at machine speed.
[…]
10
-
Assessing Progress in AI for Protein Engineering: Where Are We Now, and Where Are We Going?
Protein engineering is the most active frontier for AI in biotech. We are seeing the emergence of models that can make "zero shot" predictions of antibodies, and new variants of enzymes that would take millions of years for Evolution to produce. How should we be evaluating these models and their designs? What can we expect from the field of AI protein design moving forward?
[…]
11
-
Genome Modeling and Design Across All Domains of Life
All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation—from noncoding pathogenic mutations to clinically significant BRCA1 variants—without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon–intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.
[…]
12
-
AI 'Cheat Codes' Speed Therapeutic Cell Creation via Transcription Factors
One of the biggest challenges faced by the regenerative medicine field today is the ability to efficiently and scalably generate therapeutic cells. This session explores cutting-edge transcription factor-based approaches for rapid and efficient differentiation of stem cells into therapeutic cell types. We will examine how synthetic biology techniques are revolutionizing cell therapy by enabling faster production of clinically relevant cells. Speakers will discuss recent advances, challenges, and future directions in harnessing transcription factors for directed differentiation, with a focus on applications in regenerative medicine and drug discovery.
[…]
13
-
From Data to Delivery: Harnessing AI-Driven Tools for Next-Generation Gene Therapies
A new era in synthetic biology is unfolding as AI, genome sequencing, and DNA synthesis technologies come together to unlock breakthroughs once considered unattainable. In gene therapy, AI is poised to address one of the most pressing and costly hurdles of the field: efficient and targeted gene delivery. This talk will examine how advanced, AI-enabled approaches to synthetic biology for gene delivery are transcending the limitations of naturally occurring vectors, broadening tissue targeting to organs such as the eye, muscle, and brain, and accelerating the path from concept to clinical success. Attendees will gain actionable insights into harnessing these AI-driven tools to improve patient access, reduce costs, and usher in a new generation of transformative genomic medicines.
[…]

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Copyright SynBioBeta 2025
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Copyright SynBioBeta 2025
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Copyright SynBioBeta 2025