Biological algorithm platform: What's the answer for scaling up synthetic biology?

Synthetic biology is fundamentally reshaping the global biomanufacturing landscape, and the penetration of digital and intelligent technologies is driving the industry away from traditional experience-driven R&D models. In recent years, powered by comprehensively upgraded AI-driven systems, synthetic biology has achieved full-process intelligent upgrading—from gene sequence design to functional protein production. In the past, a complete industrial strain or functional protein R&D workflow often took several years; today, intelligent R&D systems have significantly compressed this timeline.

This leap in efficiency does not stem from a single AI prediction tool or high-end gene synthesis equipment alone, but rather from a digital infrastructure that runs through the entire synthetic biology value chain—the bio-algorithm platform. As the next-generation industrial operating system for synthetic biology, this platform effectively integrates fragmented, experience-dependent R&D processes and has become the core foundation for industrialization and large-scale development in the sector.

According to a report released by Global Market Insights Inc., the global synthetic biology market reached $25.6 billion in 2025 and is projected to grow to $128.1 billion by 2035. The core engine driving this high growth is precisely the rapidly iterating next-generation bio-algorithm platform—which is reshaping the traditional "workshop-style" R&D model, upgrading a research approach that relied on personal experience and carried prohibitively high trial-and-error costs into a modern engineering system characterized by standardized full-process workflows, closed-loop data, and self-iterating models, propelling synthetic biology into the stage of industrial-scale application.

I. The DBTL Bottleneck: The Efficiency Ceiling of Traditional R&D

The DBTL Cycle Design, Build, Test, Learn

The DBTL cycle (Design–Build–Test–Learn) is the recognized core workflow of synthetic biology and has been the bottleneck constraining industrialization efficiency for the past several decades.

Under the traditional model, developing an engineered strain for efficient production of a target product or a high-performance functional protein is heavily dependent on the experience of senior researchers. Scientists must rely on subjective judgment to design dozens of metabolic pathways and genetic modification schemes, spend weeks completing gene cloning, part assembly, and other experimental work, then collect yield and activity data through shake-flask cultivation and assays, and finally manually synthesize insights for the next iteration round. A single complete iteration often takes months, resulting in extremely low R&D efficiency.

Even more challenging is the issue of knowledge and data transfer—staff turnover, non-standardized record-keeping, and fragmented data mean that substantial trial-and-error experience accumulated in earlier stages is difficult to preserve and reuse. New projects often start from scratch, and the industry remains trapped in a cycle of low-level repetitive R&D.

At a fundamental level, the core contradiction of traditional R&D lies in the deep disconnect between computational simulation and physical experimentation: design tools, construction equipment, detection systems, and analysis software all operate independently, with inconsistent data standards and incompatible interfaces, creating numerous data silos. High-quality experimental data cannot be effectively fed back to front-end design models, and the industry struggles to accumulate general-purpose data assets, relying instead on fragmented experience for slow iteration.

The bio-algorithm platform precisely addresses this pain point. It is not a single software tool but a unified operating system integrating four key elements: software algorithms, automated hardware, data closed loops, and model self-learning. The platform achieves end-to-end integration of the synthetic biology workflow: the algorithm layer covers intelligent design and pathway prediction; the hardware layer executes DNA synthesis, strain cultivation, and other wet-lab experiments; the data layer standardizes governance of full-process parameters and results; and the learning layer automatically iterates and optimizes models based on new data from each DBTL cycle. This positive closed loop of "dry-lab simulation → wet-lab validation → data feedback" enables the platform to become increasingly precise and efficient with each iteration.

II. Four Core Systems: A Modular Industrial-Grade Architecture

Core Architecture of the Bio-Algorithm Platform

Mature bio-algorithm platforms have all developed standardized, layered underlying architectures that integrate traditionally fragmented R&D capabilities into reusable industrial-grade capacity systems. The MatwingsVenus™ （晓鹜™） platform, independently developed by Matwings Technology, exemplifies the implementation of this architecture.

Layer 1: Biological Part Intelligent Design Engine

This engine leverages an AI protein foundation model that integrates multi-dimensional data—sequence, structure, and function—reducing reliance on the empirical guesswork of researchers. It can directly start from functional requirements to de novo generate artificially designed genetic regulatory elements, functional enzymes, and customized genetic circuits with specific functions. Next-generation models can not only accurately predict protein three-dimensional structures but also deeply simulate interactions between proteins and DNA or ligands, upgrading the inefficient traditional approach of "broad-spectrum screening of tens of thousands of mutants" to intelligent screening and performance prediction of massive candidate sequences in virtual space, significantly compressing development cycles.

Matwings Technology's proprietary MatwingsVenus™ （晓鹜™） platform is an AI agent-centric one-stop protein R&D platform. The platform supports retrieval from billions of real labeled protein data points, integrates over 200 professional design tools, connects with more than 50 platform-certified experts, and is equipped with over 30 domain-specific fine-tuned Skills. Users simply input R&D requirements via natural language, and the system automatically decomposes tasks, orchestrates capabilities, and plans solutions, fully automatically executing core workflows including in-depth protein research, functional enzyme mining, directed evolution, and de novo design, while coordinating with automated wet-lab experiments for validation. The platform breaks through the traditional "sequence → structure → function" one-way prediction pathway, establishing a self-closed-loop intelligent design system that starts from functional requirements and generates original sequences.

Layer 2: Automated Experiment Execution Layer

The bio-algorithm platform is deeply integrated with high-throughput automated hardware, forming a complete technical system that seamlessly combines software algorithms with automated hardware. The platform orchestrates standardized liquid handling workstations, parallel high-throughput bioreactors, online real-time monitoring systems, and other equipment, achieving intelligent coordination and unmanned operation, continuously executing parallel full-process experimental operations including strain construction, cultivation, and testing. This resolves the traditional problems of low throughput, high error rates, and poor standardization in manual operations, enabling batch, high-frequency DBTL iteration.

Layer 3: Full-Chain Standardized Data Governance System

The core competitiveness of a bio-algorithm platform lies not only in algorithms and hardware but also in the continuously accumulating, rigorously standardized, and annotated high-quality datasets. Industry consensus holds that the bottleneck in AI-driven biodesign deployment is not primarily algorithmic—the true determinant of capability ceilings is high-quality data governance and industrial engineering systems.

Traditional laboratory data suffers from inconsistent annotation, non-uniform formats, and scattered storage, making it difficult to use for model training. The bio-algorithm platform, however, enables fully automated data governance across the entire workflow—from operational steps and equipment parameters to result metrics, all are automatically recorded, structurally stored, and standardized tagged without manual organization. Data generated from each experimental round feeds directly into the model training system, forming a positive flywheel of "data iterates models, models optimize experiments, experiments generate more data," building a technological advantage that traditional approaches cannot replicate.

Layer 4: Metabolic System Global Simulation and Digital Twin Layer

Some mature platforms have begun exploring the construction of metabolic network simulation systems, attempting to simulate strain metabolic pathways and product synthesis routes in virtual space to pre-identify metabolic bottlenecks, intermediate product accumulation, cytotoxicity, and other issues. This effectively avoids costly trial-and-error in actual fermenters and significantly improves the success rate of industrial-scale scaling.

III. Industrial Deployment: From Digital Design to Industrial Value

The Bio-Algorithm Platform Powers the Future Bioeconomy

After years of iteration, the bio-algorithm platform has been fully deployed across multiple industrial sectors—biopharmaceuticals, industrial enzyme manufacturing, natural product synthesis, and modern agriculture—delivering quantifiable cost-reduction and efficiency-improvement value.

The collaboration between Matwings Technology and GeneScience Pharmaceuticals is a benchmark case. Leveraging the platform's protein engineering general foundation model, the team completed directed engineering of a non-alkali-resistant single-domain antibody in less than one year, achieving a 4-fold improvement in alkali resistance and successfully scaling to 5,000-liter fermentation production processes for industrial deployment. This case has been recognized by industry media as "the world's first protein product designed by a foundation model that has entered 5,000-liter scale-up production and commercial application."

According to publicly disclosed information, Matwings Technology has successfully delivered over 30 protein design industrialization projects through the MatwingsVenus™ platform, with more than 40 projects in the pipeline and over 40 partner enterprises. The platform compresses the traditional 2- to 5-year protein design cycle to 2 to 6 months, achieving an order-of-magnitude improvement in R&D efficiency.

In the industrial enzyme engineering track, the platform has developed multiple classes of high-performance industrial protein products through AI-directed evolution, intelligent enzyme mining, and performance optimization. According to publicly disclosed data on the Matwings Technology website, a glycosyltransferase engineering project achieved in just 4 months a 7-fold increase in total transglycosylation activity, 95% product specificity, and a 33% reduction in side-reaction hydrolytic activity—a multi-dimensional optimization. A single-domain antibody project achieved 400% improvement in alkali resistance, 1.2-fold affinity enhancement, and an 8°C Tm increase within 4 months. These products achieve significant improvements in core metrics such as acid/alkali tolerance, thermostability, and catalytic activity, fully meeting the industrial-scale production requirements of the chemical, food, and light industry sectors. In the natural product synthesis domain, the platform's engineered strain yields have broken through industrial production thresholds, effectively replacing traditional plant extraction and chemical synthesis supply chain pathways.

In the agriculture and microbiome sector, the full-stack bio-algorithm platform integrates vast functional microbial germplasm resources, whole-genome data, and phenotype knowledge bases. Through microbiome data and phenotype association analysis, it assists in screening candidate microbial strains with functions including plant growth promotion, disease antagonism, and environmental remediation, significantly shortening product development cycles and bringing complex microbial engineering capabilities—formerly accessible only to top-tier research institutions—to a wide range of agricultural, environmental, and aquaculture application scenarios.

IV. The Next Decade: The Operating System for the Programmable Biology Era

The current stage of development of bio-algorithm platforms is analogous to the early evolution of operating systems in the computer industry. As technology iterates and costs decline, the platform will fully permeate the entire biomanufacturing value chain, becoming the core infrastructure of the programmable bio-economy.

The next three to five years will see three major trends:

First, platform architecture will gradually move to the cloud. Biologics R&D is expected to break free from local hardware constraints, enabling users to complete portions of the R&D workflow through cloud-based platforms. By inputting target product requirements, the system can fully automatically execute core R&D stages from enzyme design to strain construction, outputting commercially viable solutions in a short time and lowering the technological adoption barrier for small and medium-sized enterprises.

Second, biological part interfaces will trend toward standardization. As industry data accumulates, standardized modular biological part interface standards are expected to emerge. In the future, various genetic parts and functional modules may be freely called and assembled like open-source software libraries, significantly improving reuse efficiency and iteration speed.

Third, biological functional modules will develop into an ecosystem. As the platform ecosystem matures, industry developers may join the platform to develop, share, and reuse biological functional modules. The biomanufacturing sector is likely to replicate the "app store" model, where enterprises can access mature industrial enzyme or synthetic strain solutions with a single click, conveniently building complete biosynthetic systems as easily as downloading applications.

What the bio-algorithm platform brings to the industry is not merely efficiency improvement—it is a fundamental digital restructuring of the life sciences. It gives complex, dynamic biological systems, for the first time, a standardized industrial operating system that is iterable, closed-loop, reusable, and extensible. If the von Neumann architecture laid the foundation for the digital economy, then domestically developed bio-algorithm platforms such as MatwingsVenus™ （晓鹜™） are building the core underlying operating system for the programmable bio-economy era, driving synthetic biology from a laboratory technology to a core productive force leading future industrial transformation.