Synthetic Biology
Strain Metabolic Network Modeling

Strain Metabolic Network Modeling

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Strain Metabolic Network Modeling

Introduction

Metabolic network modeling is a powerful tool for understanding the complex interactions between biological systems and their environments. It involves the use of computational techniques to analyze and simulate the metabolic pathways in organisms, with the goal of predicting their behavior under different conditions. In recent years, metabolic network modeling has become increasingly important in synthetic biology, where it is used to design and optimize strains for a wide range of applications.

At CD Biosynsis, we are committed to providing high-quality services for metabolic network modeling in synthetic biology strains. Our team of experts has years of experience in the industry, and we use the latest techniques and technologies to deliver results that meet the needs of our clients.

Flux-dependent graphs for metabolic networks.Figure 1: Flux-dependent graphs for metabolic networks. (Beguerisse-Díaz, M., Bosque, G., et al. 2018)

Our Services for Metabolic Network Modeling

At CD Biosynsis, we offer a wide range of services for metabolic network modeling in synthetic biology strains. These services include:

Metabolic Pathway Reconstruction

Metabolic Pathway Reconstruction

We use a variety of data sources, including genomic and transcriptomic data, to reconstruct the metabolic pathways in synthetic biology strains. This allows us to build accurate models of the metabolic network and predict the behavior of the system under different conditions.

Flux Balance Analysis

Flux Balance Analysis

We use flux balance analysis (FBA) to predict the metabolic fluxes in synthetic biology strains. FBA is a powerful technique that allows us to optimize the production of target compounds while minimizing the use of resources.

Strain Design and Optimization

Strain Design and Optimization

Based on the metabolic models we build, we can design and optimize strains for a wide range of applications, including the

--production of biofuels,

--pharmaceuticals,

--industrial chemicals.

Model Validation and Refinement

Model Validation and Refinement

We work closely with our clients to validate and refine the metabolic models we build. This ensures that the models accurately reflect the behavior of the system under different conditions and can be used to make reliable predictions.

Our Advantages

At CD Biosynsis, we pride ourselves on our ability to deliver high-quality services for metabolic network modeling.

  1. Expertise: Our team of experts has years of experience in the industry, and we have a deep understanding of the latest techniques and technologies for metabolic network modeling.
  2. Customization: We work closely with our clients to understand their specific needs and develop customized solutions that meet their requirements.
  3. Collaboration: We believe that collaboration is key to success in metabolic network modeling. We work closely with our clients to ensure that our models accurately reflect the behavior of the system under different conditions.
  4. Quality: We are committed to delivering high-quality services that meet the needs of our clients.

Conclusion

Metabolic network modeling is a powerful tool for understanding the complex interactions between biological systems and their environments. At CD Biosynsis, we are committed to providing high-quality services for metabolic network modeling in synthetic biology strains. Our team of experts has years of experience in the industry, and we use the latest techniques and technologies to deliver results that meet the needs of our clients. Contact us today to learn more about our services and how we can help you optimize your metabolic network for your specific application.

References

  1. Beguerisse-Díaz, M., Bosque, G. et al. Flux-dependent graphs for metabolic networks. npj systems biology and applications 4, 32 (2018). https://doi.org/10.1038/s41540-018-0067-y
  2. Yilmaz L. Safak, Walhout Albertha J. M. Metabolic network modeling with model organisms. Current Opinion in Chemical Biology 36: 32-39 (2017). https://doi.org/10.1016/j.cbpa.2016.12.025
Please note that all services are for research use only. Not intended for any clinical use.
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