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Revolutionizing Synthetic Biology: AI’s Transformative Impact on Accuracy, Speed, and Cost

In the realm of scientific discovery and technological advancement, the intersection of artificial intelligence (AI) and synthetic biology is forging a path toward unprecedented possibilities. As these two fields converge, AI is proving to be a catalyst, accelerating progress in synthetic biology by enhancing accuracy, speed, and cost-effectiveness.

Understanding Synthetic Biology

Synthetic biology, an amalgamation of science, technology, and engineering, is reshaping our ability to manipulate genetic materials within living organisms. Synthetic biology  is defined as the application of science, technology and engineering to facilitate and accelerate the design, manufacture and/or modification of genetic materials in living organisms.

This multidisciplinary field combines biology, chemistry, computer science, and engineering to redesign natural biological systems, creating novel organisms and components not found in the natural world.

Central to synthetic biology are the abilities to read, write, and edit the “code of life,” involving DNA sequencing, synthesis, and gene editing.  One core technology is DNA sequencing, or the ability to read DNA. Another is DNA synthesis, or the ability to write code for DNA.  Gene editing is another core biotechnology. It allows scientists to alter a DNA sequence by adding, swapping or removing genes. For example, it combines the knowledge of genomics and chemical synthesis of DNA for the rapid production of cataloged DNA sequences.

Synthetic biology’s applications are diverse, ranging from bioengineered microorganisms producing pharmaceuticals to playing a pivotal role in drug and vaccine development. In the context of the COVID-19 pandemic, synthetic biology, empowered by AI, accelerates vaccine development and offers innovative diagnostic solutions

Among the potential applications of this new field is the creation of bioengineered microorganisms (and possibly other life forms) that can produce pharmaceuticals, detect toxic chemicals, break down pollutants, repair defective genes, destroy cancer cells, and generate hydrogen for the post-petroleum economy.

Challenges and Core Biotechnologies

The precise manipulation of genetic material is a critical challenge in synthetic biology. Core biotechnologies involve DNA sequencing (reading DNA), DNA synthesis (writing DNA code), and gene editing, enabling alterations to DNA sequences. However, these processes are often slow, expensive, and less advanced, especially in DNA synthesis.

Precision Engineering through AI Algorithms

One of the most significant contributions of AI to synthetic biology lies in its ability to optimize the design and engineering of biological systems. Traditional methods often involve trial and error, consuming valuable time and resources. AI algorithms, on the other hand, analyze vast datasets to predict the behavior of biological components. This enables researchers to design DNA sequences with a higher probability of success, minimizing errors and expediting the engineering process.

AI Integration in the Design-Build-Test-Learn (DBTL) Cycle

In past,  typical synthetic biology workflow for organism engineering was viewed as a cycle of three stages: design maps a behavior specification to a nucleic acid sequence intended to realize this behavior; build draws on synthesis and/or assembly protocols to fabricate said nucleic acid sequence; and test assays (measures) the behavior of cells modified to include the sequence, feeding this information back into the design step completing the cycle.

AI’s integration into the Design-Build-Test-Learn (DBTL) cycle, a fundamental concept in synthetic biology, is accelerating the iterative process of designing, building, testing, and learning from biological systems. The cycle initiates with the Design (D) phase, where a biological system is conceptualized to fulfill desired specifications such as a specific titer, rate, yield, or product. Following this, the Build (B) phase constructs the designed system by assembling DNA parts into a suitable microbial chassis, utilizing synthetic biology tools. Once built, the biological system undergoes the Test (T) phase, involving various assays like measuring production and omics data profiling to evaluate its functionality against the original design.

Design: In the Design phase of the DBTL cycle, engineers embark on determining the arrangement of sensors, actuators, regulatory relationships, and enzymatic pathways to implement a desired biological behavior. This involves mapping the arrangement onto available DNA or RNA components, and the engineering of new components with specified requirements. The design stage encompasses model construction, data mining, sequence design of synthetic promoters, terminators, enzymes, metabolic pathway design, and process design for cell production and fermentation.

Tools for Design:

  • Model construction tools such as COBRA and FluxML.
  • PartsGenie, an open-source software for optimizing synthetic biology parts.
  • MAPPs for mapping reference networks and searching for shortest pathways.
  • NovoPathFinder for designing pathways based on stoichiometric networks.
  • PR-PR, a robot programming language for procedure standardization and communication among biofoundries.

Build: The Build stage involves creating organisms modified with the designed nucleic sequences. This includes synthesizing or assembling sequences to produce physical samples, culturing host organisms, and delivering the sequences to the host through various protocols. Challenges in yield and quality assurance persist in both stages, and while next-generation sequencing aids in quality control, effective planning, resourcing, and executing build protocols remain open challenges.

Automation-Friendly DNA Assembly Tools:

  • Methyltransferase-assisted BioBrick.
  • Twin-Primer Assembly (TPA).
  • Gibson and NEBuilder assembly.
  • Ligase Cycling Reaction (LCR).
  • Yeast in vivo assembly.

Automation Platforms:

  • Q-metric for standardizing automated DNA assembly methods.
  • Transformation-associated recombination (TAR)-based biofoundries, e.g., Amyris Inc.

Test: In the Test phase, the behavior of the newly constructed organism is assayed and measured to evaluate its correspondence with the original specification. Challenges arise in relating assay data to the original specification, as many assays produce voluminous qualitative or relative data. The mapping back to the original specification and the integration with predictive models for improved results are areas of complexity.

Test Workflow:

  • Involves cell culture, cell sorting, and cell analysis.
  • Automation introduces specific requirements to the test workflow.

Learn: The Learn phase, though critical, has historically been weakly supported in the DBTL cycle. Challenges in predictive power for biological systems behavior, reproducibility issues in biological experiments, and a moderate emphasis on mathematical training for synthetic biologists contribute to this weakness. The Learn stage involves systems biology analysis and machine learning, where automation platforms generate substantial data that needs analysis and integration to refine models for guiding subsequent iterative DBTL cycles through standardized procedures.

Platforms like BioAutomata and Automated Recommendation Tool (ART) exemplify how AI automates and optimizes biological processes, reducing the number of experiments required

According to Tara O’Toole, a former undersecretary of Homeland Security, AI is positioned to enhance the precision, speed, and affordability of these core biotechnologies. AI’s role extends to improving DNA sequencing, synthesis, and gene editing, making these processes faster, more accurate, and cost-effective.

Artificial Intelligence (AI) for Synthetic Biology: Enhancing Design and Learning

Artificial Intelligence (AI), encompassing machine learning (ML), has evolved into a transformative force, driving advancements in various fields. ML, a subset of AI, involves algorithms that discern patterns from data to make predictions, with deep learning as a powerful subcategory utilizing neural networks. In the realm of healthcare, AI shows promise in revolutionizing patient care, aiding physicians with up-to-date medical information and minimizing diagnostic errors. In biology, AI’s potential is particularly pronounced, leveraging its capability to analyze vast and intricate datasets, such as genetic mutations contributing to obesity or examining pathology samples for cancerous cells.

A noteworthy application of AI in synthetic biology lies in the automatic design of DNA sequences with specific qualities, as revealed in a study published in Nature Genetics. The study suggests that AI can predict changes in DNA sequences crucial for medical diagnostics, vaccine development, and overall genomic research. This capability proves instrumental in understanding the effects of altering DNA, a traditionally laborious and expensive process. Deep learning-based AI methods emerge as a cost-effective and expeditious alternative for genomic research, demonstrating the potential to significantly impact fields like drug development.

Machine learning, an integral part of AI, plays a vital role in predicting biological system behavior and facilitating the Learn phase in synthetic biology’s Design-Build-Test-Learn (DBTL) cycle. By discerning patterns in experimental data, machine learning can make predictions without an exhaustive understanding of underlying mechanisms. Notably, machine learning applications range from predicting substance use and political views from social media profiles to optimizing biological pathways and diagnosing medical conditions like skin cancer.

However, the practice of machine learning demands scarce statistical and mathematical expertise, creating a competitive landscape for skilled professionals. Despite this challenge, machine learning continues to make strides, particularly in the intersection with quantitative biology based on constraint-based models. Techniques like unsupervised learning and dimensionality reduction have been employed to analyze massive datasets, with applications ranging from bioproduction optimization to the iterative engineering of microbial cell factories.

A landmark development in the fusion of machine learning and synthetic biology is the BioAutomata platform, unveiled in 2019. This fully automated system integrates machine learning algorithms with the iBioFAB robotic system, exemplifying a compelling proof of concept. BioAutomata guides iterative DBTL cycles by autonomously designing experiments, executing them, and analyzing resulting data to optimize user-specified biological processes. By training probabilistic models on initial data, BioAutomata efficiently navigates the optimization space, reducing the number of experiments needed to enhance biosystems, thereby showcasing the potential for AI-driven automation in advancing synthetic biology.

2. Rapid DNA Sequence Analysis and Synthesis

AI-driven tools are transforming the landscape of DNA sequence analysis and synthesis. By leveraging machine learning algorithms, scientists can quickly analyze large genomic datasets, identifying patterns and relationships that might be impossible for humans to discern. Moreover, AI streamlines the synthesis of DNA, making the process faster and more cost-effective. This acceleration in both analysis and synthesis stages propels the overall speed of synthetic biology experiments.

3. Cost Reduction through Predictive Modeling

The cost of conducting synthetic biology experiments has historically been a limiting factor. AI addresses this challenge by introducing predictive modeling that enables researchers to anticipate outcomes with greater accuracy. This forecasting ability empowers scientists to make informed decisions, reducing the likelihood of failed experiments and the associated costs. As AI algorithms learn from each iteration, they contribute to an evolving knowledge base that benefits the entire synthetic biology community.

4. Enhanced Bioprocess Optimization

In the realm of industrial applications, AI plays a pivotal role in optimizing bioprocesses. Whether it’s the production of biofuels, pharmaceuticals, or biochemicals, AI algorithms analyze diverse parameters in real-time. This results in more efficient and cost-effective production processes, as AI adapts and refines strategies based on ongoing feedback and data analysis.

5. Accelerating Drug Discovery and Development

The integration of AI in synthetic biology is propelling advancements in drug discovery. By rapidly analyzing biological data, AI identifies potential drug candidates and assesses their viability with unprecedented speed. This not only expedites the drug development process but also contributes to the discovery of novel therapeutic solutions.

Artificial intelligence makes enzyme engineering easy

Enzymes perform impressive functions, enabled by the unique arrangement of their constituent amino acids, but usually only within a specific cellular environment. When you change the cellular environment, the enzyme rarely functions well—if at all. Thus, a long-standing research goal has been to retain or even improve upon the function of enzymes in different environments; for example, conditions that are favorable for biofuel production. Traditionally, such work has involved extensive experimental trial-and-error that might have little assurance of achieving an optimal result.

Artificial intelligence (AI) emerges as a potent solution, yet it typically relies on experimentally obtained enzyme crystal structures, which might be limited. Researchers, led by Teppei Niide and Hiroshi Shimizu, addressed this challenge by developing a methodology that ranks amino acids based solely on the widely available amino acid sequence of analogous enzymes from different species.

The study focused on malic enzyme, scrutinizing amino acids influencing substrate and cofactor specificity. By identifying evolutionarily conserved amino acid sequences, the researchers discerned mutations adapting to diverse cellular conditions in various species. Leveraging AI, unexpected amino acid residues crucial for the enzyme’s use of different redox cofactors were identified. This innovative approach not only accelerated but also improved the success of reconfiguring the enzyme’s mode of action without altering its fundamental function. This breakthrough holds promise for fields like pharmaceuticals and biofuel production, where tuning enzyme versatility to diverse biochemical environments is critical, even without crystal structures.

Defense and Security Implications

AI and synthetic biology converge to transform defense and security. Genome editing opens avenues to harness biological systems for weapons or engineering tasks impractical with conventional methods. New techniques to edit and modify the genome may allow scientists to harness organisms or biological systems as weapons or to perform engineering tasks typically impractical with conventional methods.

DARPA wants to utilize the potential of Synthetic biology, to provide on-demand bio-production of novel drugs, new materials, food, fuels, sensors and coatings whatever suits the military’s needs. Future advances might include the construction of new biological parts and brain-computer interfaces.

Global Competition and China’s Strategy

A competitive race is unfolding globally, with countries like the United States and China striving to lead in synthetic biology. China, in particular, is aggressively pursuing biotechnological dominance, combining its internet giants with biotech companies. China’s strategic goals involve making biotechnology a significant part of its GDP and attracting global scientific talent.

China has changed regulations for its own version of the Food and Drug Administration to be more like that of the United States in order to more easily market to the world. The country has created a talent pipeline that incentivizes its own students to go into the life sciences and bioengineering. China also has at least 20 programs intended to bring scientific talent from the rest of the world.

Challenges Faced by the United States

The United States faces challenges in translating biological knowledge into products and building robust infrastructure for a thriving bioeconomy. The translation of biology to products is mainly driven by small start-up companies, lacking the comprehensive infrastructure needed for managing epidemics.

Exponential Growth and Private Investments

The field of synthetic biology is experiencing exponential growth, driven by byproducts of the genomic revolution, including high-throughput multi-omics phenotyping and advanced CRISPR-enabled genetic editing. Private investments in synthetic biology totaled around $12 billion from 2009 to 2018, indicating its significance and potential.

The global synthetic biology market in terms of revenue was estimated to be worth $11.4 billion in
2022 and is poised to reach $35.7 billion by 2027, growing at a CAGR of 25.6% from 2022 to
2027. Various elements such as
decreasing cost of DNA sequencing and synthesizing, and increased government funding for synthetic biology research are boosting the growth of this market. However, biosafety, biosecurity, and ethical concerns related to synthetic biology are likely to hinder the growth of this market

A machine learning Automated Recommendation Tool for synthetic biology

Lawrence Berkeley National Laboratory researchers have introduced the Automated Recommendation Tool (ART), an innovative machine learning algorithm designed for synthetic biology, as detailed in their September 2020 study in Nature Communications. ART, a patent-pending tool, employs machine learning and probabilistic modeling to guide synthetic biology systematically, eliminating the need for a comprehensive mechanistic understanding of the biological system. By utilizing various machine learning models and a Bayesian ensemble approach, ART predicts probability distributions for system responses, allowing researchers to better anticipate outcomes.

In collaboration with the Novo Nordisk Foundation Center for Biosustainability and TeselaGen Biotechnology, the team demonstrated ART’s efficacy in managing metabolic engineering to enhance tryptophan production in baker’s yeast.

Tryptophan is an essential amino acid used for making and managing neurotransmitters, muscles, enzymes, and proteins. Tryptophan is required for normal growth in infants and is not produced by the body. First the Danish researchers and their colleagues created a combinatorial library, a collection of chemicals or molecules synthesized by combinatorial chemistry and set up a large phenotypic dataset. Combinatorial chemistry is the chemical synthetic method that enables to produce large quantities of compounds in a single process.

The researchers trained ART to associate certain amino acid production with gene expression using experimental data on a small percentage, just 250 genotypes, out of the 7,776 possible combinations of biological pathways of five target genes as the input training dataset. ART extrapolated how the remaining thousands of combinations would impact tryptophan production, then produced designs to improve high tryptophan production ranked in priority.

The study showcases the potential of machine learning to accelerate complex metabolic engineering processes in synthetic biology, a field anticipated to reach $18.9 billion by 2024 with a compound annual growth rate of 28.8%. This intersection of artificial intelligence and synthetic biology holds promise for future innovations benefiting humanity.

Revolutionizing Drug Discovery: AI and SynBio Convergence

Integrated Biosciences, a pioneering biotech firm at the intersection of synthetic biology (SynBio) and artificial intelligence (AI), has unveiled a groundbreaking drug discovery platform. This platform, featured in Cell Systems, leverages optogenetic tools to precisely control the integrated stress response (ISR), a cellular pathway activated in response to various pathological and aging-related conditions. Max Wilson, Co-founder of Integrated Biosciences, emphasizes the complexity of biological aging and highlights the role of machine learning (ML) in decoding intricate cellular stress responses. ML models, while not always human-interpretable, prove invaluable in predicting outcomes, generating hypotheses, and, in Integrated’s case, predicting small molecules affecting age-associated pathways. SynBio, with its ability to program cells for diverse behaviors, ensures high-quality and relevant data for ML models.

Integrated Biosciences’ platform dynamically modulates signaling pathways, mimicking the aging process and targeting it with small molecules. The company aims to rejuvenate cells by finding compounds that restore youthful signaling dynamics. Wilson sees a future where SynBio, exemplified by T-cell therapy, plays a pivotal role in longevity, potentially enabling engineered cells to deliver rejuvenating factors or even differentiate into organs for treating age-related diseases. The convergence of AI and SynBio represents a biotech renaissance, offering unprecedented innovations and insights into manipulating biological systems. Integrated Biosciences looks to collaborate with biotech companies and VC firms, amplifying the impact of their discoveries and ushering in a new era of biotechnology.

Conclusion: A Symbiotic Future

As AI continues to refine its capabilities in synthetic biology, the synergy between these two fields holds immense promise. The precision, speed, and cost-effectiveness brought about by AI-driven technologies are ushering in a new era of discovery and innovation. From designing customized organisms to expediting drug development, the impact of this collaboration reverberates across multiple scientific disciplines.

The ongoing marriage of AI and synthetic biology not only accelerates progress in the laboratory but also democratizes access to cutting-edge research. As these technologies become more accessible, we can anticipate a surge in breakthroughs and innovations, ultimately reshaping our understanding of biology and its applications in the real world. The journey towards a more efficient, accurate, and affordable synthetic biology future is being propelled by the intelligence of machines, opening doors to possibilities that were once confined to the realm of science fiction.

 

 

References and Resources also include:

https://longevity.technology/news/standing-at-the-precipice-of-a-biotech-renaissance-the-confluence-of-ai-and-synbio/

https://www.sciencedirect.com/science/article/pii/S1369527421001466

https://www.eurekalert.org/news-releases/969812

 

About Rajesh Uppal

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