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AI-Aided Metabolic Services

Metabolic engineering is a rapidly evolving field that focuses on the manipulation of cellular pathways to enhance the production of valuable compounds such as biofuels, pharmaceuticals, and industrial chemicals. Traditionally, metabolic engineering has relied on trial-and-error approaches to identify optimal genetic modifications and culture conditions. At CD ComputaBio, we leverage the power of AI to streamline the metabolic engineering process. Our advanced computational tools can analyze vast amounts of biological data, predict the behavior of engineered metabolic pathways, and identify the most promising targets for genetic manipulation. By combining AI with our deep domain expertise, we can significantly reduce the time and resources required to develop high-performing microbial strains and bioprocesses.

Our Services

Fig1: Typical machine learning (ML) applications in rate-limiting enzyme engineering.

AI-Aided Design and Optimization of Metabolic Processes

Our team employs network-based approaches to model and analyze metabolic pathways as interconnected networks of biochemical reactions. This allows us to capture the complexity of cellular metabolism and understand the functional relationships between different pathway components.

Fig 2: Applications of artificial intelligence to enzyme and pathway design for metabolic engineering

AI-Aided Metabolic Engineering

We can develop comprehensive databases of metabolic pathways and related genetic elements, providing valuable resources for metabolic engineering research and development. By analyzing large datasets of enzyme structures and functions, we can identify potential mutations and modifications that enhance enzyme performance and specificity.

Our Analysis Methods

CD ComputaBio utilizes a range of sophisticated analysis methods to support our AI-aided metabolic engineering services:

Bioinformatics We leverage bioinformatic tools to analyze genome sequences, protein structures, and metabolic networks. This enables us to identify potential gene targets for metabolic engineering and predict the impact of genetic modifications on cellular metabolism.
Metabolic Flux Analysis We employ metabolic flux analysis techniques to quantify intracellular flux distributions and metabolic pathway activities. This information is crucial for understanding the regulatory mechanisms that govern cellular metabolism and guiding the design of engineered pathways.
Machine Learning We harness the power of machine learning algorithms to analyze large-scale omics data, such as transcriptomics, proteomics, and metabolomics. This allows us to uncover complex patterns in biological systems and extract valuable insights for metabolic engineering.
Systems Biology Modeling We use systems biology approaches to integrate multi-omic data and reconstruct comprehensive models of cellular metabolism. These models enable us to simulate the behavior of engineered strains, predict their performance under different conditions, and identify optimal intervention strategies.

Workflow of Our Service

Our AI-aided metabolic engineering services are designed to deliver precise and reproducible results within a structured workflow:

Fig 3: Service workflow of AI-aided metabolic engineering services

Project Scoping - We conduct a thorough assessment of the target metabolic pathway and the desired production objectives to inform the subsequent steps.

Data Collection and Integration - We gather relevant biological data, including genome sequences, omics profiles, and pathway databases.

Prediction and Design - We employ AI algorithms to predict the performance of engineered strains and design optimal genetic modifications.

Experiment Validation - We collaborate with clients to implement genetic modifications in laboratory strains and conduct experimental validation.

Reporting and Recommendations - We deliver comprehensive reports that summarize the findings, methodologies, and recommendations from the metabolic engineering project. We also provide insights into the underlying metabolic regulations, potential bottlenecks, and future directions for optimization.

CD ComputaBio is a leading provider of AI-aided metabolic engineering services. Our team of experts specializes in utilizing cutting-edge technology and computational methods to accelerate the design and optimization of metabolic pathways. With our services, clients can expect to achieve improved yields, reduced production costs, and enhanced product quality for a wide range of biotechnology applications. If you are interested in our services or have any questions, please feel free to contact us.

References:

  • Jang W D, Kim G B, Kim Y, et al. Applications of artificial intelligence to enzyme and pathway design for metabolic engineering[J]. Current Opinion in Biotechnology, 2022, 73: 101-107.
  • Cheng Y, Bi X, Xu Y, et al. Machine learning for metabolic pathway optimization: A review[J]. Computational and Structural Biotechnology Journal, 2023.

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