CD Biosynsis provides specialized E. coli Metabolic Pathway Kinetic Modeling Services, offering predictive, dynamic simulation of complex biological systems. Unlike steady-state models, kinetic models describe metabolite concentrations and reaction rates over time, providing precise insights into pathway bottlenecks, regulatory mechanisms, and transient behaviors. Our service integrates experimental data (enzyme kinetics, gene expression, metabolite time courses) with mathematical frameworks to build robust models. We use these models to simulate genetic perturbations (knockouts, overexpression), predict optimal enzyme ratios, and guide iterative strain engineering (DBTL cycle) for maximizing product yield. This quantitative approach reduces experimental trial-and-error, significantly accelerating the Metabolic Pathway Optimization process.
Get a QuoteKinetic modeling is a powerful component of the Design-Build-Test-Learn (DBTL) cycle. By defining reaction rates using rate laws (e.g., Michaelis-Menten) and experimental parameters, the model captures non-linear effects, feedback loops, and metabolite accumulation, which are missed by simpler Flux Balance Analysis (FBA) models. We specialize in parameterizing E. coli pathways by collecting high-quality omics data (fluxomics, metabolomics) and using robust fitting algorithms. The validated kinetic model serves as a "digital twin" of the pathway, allowing rapid in silico exploration of genetic and environmental modifications, such as identifying the optimal timing and dosage for induction or the ideal enzyme ratio for a multi-step reaction.
Pathway Decomposition
Identification of all enzymes, substrates, products, and regulatory steps within the target metabolic pathway.
Kinetic Rate Law Definition
Assignment of appropriate rate equations (e.g., Michaelis-Menten, Hill kinetics) for each enzymatic reaction, including allosteric effects.
Parameter Collection & Fitting
Collection of literature kinetic constants (Km, Vmax) and experimental measurement and fitting of unknown parameters using time-course data.
Bottleneck Identification
Dynamic analysis to precisely locate rate-limiting steps or regulatory inhibition points within the pathway under various conditions.
Optimal Enzyme Ratio Prediction
Simulation of different Gene Knock-in or overexpression levels to determine the ideal stoichiometric balance of enzymes for peak flux.
Transient Behavior Prediction
Modeling of dynamic phenomena, such as product accumulation, pathway switch activation, and response to environmental shifts.
Metabolomics Integration
Using measured metabolite pool sizes (time-course metabolomics) to constrain and validate the model's dynamic predictions.
Sensitivity Analysis
Determining which parameters (e.g., specific Km or Vmax values) most significantly impact the final product concentration or flux.
Model-Guided Experimentation
Providing clear, predictive targets for follow-up experimental work and strain engineering to close the DBTL cycle.
A rigorous data-driven approach for generating predictive models.
Data Collection & Review
Model Construction & Parameterization
Dynamic Simulation & Prediction
Validation & Engineering Guidance
Literature Review: Collection of all known kinetic data (Km, Vmax, inhibition constants) for pathway enzymes.
Experimental Design: Guidance on generating necessary time-course data (metabolite levels, gene expression) for modeling.
Model Framework: Creation of the Ordinary Differential Equations (ODEs) based on defined rate laws.
Parameter Fitting: Calibration of unknown parameters using non-linear regression against experimental time-course data.
In Silico Experimentation: Simulating genetic modifications (e.g., doubling enzyme X expression) and environmental changes.
Results Analysis: Identification of key regulatory nodes and kinetic bottlenecks.
Dynamic Behavior Analysis
Accurately predicts time-dependent effects like metabolite accumulation, transient states, and induction timing.
Precision Bottleneck ID
Identifies exact kinetic limitations and targets for engineering that simpler steady-state models often miss.
Quantitative Optimization
Provides quantitative values for optimal gene expression ratios (Vmax ratios) to maximize flux and minimize side product.
Reduced Experimentation
Guides the DBTL cycle by providing predictive targets, drastically cutting down on time-consuming trial-and-error laboratory work.
"The kinetic model was a breakthrough. It predicted the exact optimal induction timing for our L-tyrosine pathway, which boosted our peak production rate by 40% compared to our empirical testing."
Dr. Emily Roth, PI, Bioprocess Optimization
"Their dynamic simulation clearly showed product accumulation was inhibiting an upstream enzyme. This kinetic insight was the key to redesigning our fermentation strategy and improving yield."
Mr. Kevin Zhou, R&D Manager, Specialty Chemicals
"The model provided quantitative enzyme ratio predictions. We implemented these ratios using their promoter engineering service, and the resulting pathway flux was perfectly balanced."
Ms. Lisa Nguyen, Research Scientist, Metabolic Engineering
"The rigorous parameter fitting using our metabolomics data made the model extremely predictive. It acts as a powerful digital tool that guides all our subsequent genetic modifications."
Dr. Raj Patel, Principal Investigator, Synthetic Biology
"We needed to understand the transient behavior of a switched pathway. The kinetic model accurately simulated the lag phase and transition time, saving us weeks of wet-lab experiments."
Dr. Clara Rodriguez, Chief Scientist, Industrial Biotechnology
What is the main difference between Kinetic Modeling and Flux Balance Analysis (FBA)?
FBA is a linear, steady-state model that maximizes yield but ignores time. Kinetic modeling uses non-linear rate laws (like Michaelis-Menten) to describe dynamic changes in metabolite concentrations and reaction rates over time, capturing regulatory feedback and transient bottlenecks.
What kind of data is needed to build a robust kinetic model?
A robust model requires literature values for kinetic parameters (Km, Vmax) and, critically, time-course experimental data from your strain, including metabolite concentrations (metabolomics) and/or enzyme expression levels.
Can the kinetic model predict the effect of gene overexpression?
Yes. By linking gene expression levels to Vmax (maximum reaction rate) in the model, we can simulate the effect of overexpressing a gene (e.g., Gene Knock-in) and predict the resulting flux and final product titer.
How does the model identify pathway bottlenecks?
The model identifies bottlenecks as reactions with low flux control coefficients or those where high concentrations of the substrate accumulate just upstream of the reaction, indicating a kinetic limitation.
How much does Metabolic Engineering services cost?
The cost of Metabolic Engineering services depends on the project scope, complexity of the target compound, the host organism chosen, and the required yield optimization. We provide customized quotes after a detailed discussion of your specific research objectives.
Do your engineered strains meet regulatory standards?
We adhere to high quality control standards in all strain construction and optimization processes. While we do not handle final regulatory approval, our detailed documentation and compliance with best laboratory practices ensure your engineered strains are prepared for necessary regulatory filings (e.g., GRAS, FDA).
What to look for when selecting the best gene editing service?
We provide various gene editing services such as CRISPR-sgRNA library generation, stable transformation cell line generation, gene knockout cell line generation, and gene point mutation cell line generation. Users are free to select the type of service that suits their research.
Does gene editing allow customisability?
Yes, we offer very customised gene editing solutions such as AAV vector capsid directed evolution, mRNA vector gene delivery, library creation, promoter evolution and screening, etc.
What is the process for keeping data private and confidential?
We adhere to the data privacy policy completely, and all customer data and experimental data are kept confidential.
CRISPR-Cas9 technology represents a transformative advancement in gene editing techniques. The main function of the system is to precisely cut DNA sequences by combining guide RNA (gRNA) with the Cas9 protein. This technology became a mainstream genome editing tool quickly after its 2012 introduction because of its efficient, simple and low-cost nature.
The CRISPR gene editing system with its Cas9 version stands as a vital instrument for current biological research. CRISPR technology enables gene knockout (KO) through permanent gene expression blockage achieved by sequence disruption. Various scientific domains including disease modeling and drug screening employ this technology to study gene functions. CRISPR KO technology demonstrates high efficiency and precision but requires confirmation and verification post-implementation because unsatisfactory editing may produce off-target effects or incomplete gene knockouts which impact experimental result reliability. For precise and efficient Gene Editing Services - CD Biosynsis, Biosynsis offers comprehensive solutions tailored to your research needs.
The CRISPR-Cas9 knockout cell line was developed using CRISPR/Cas9 gene editing to allow scientists to remove genes accurately for research on gene function and disease models and pharmaceutical discovery. Genetic research considers this technology essential due to its high efficiency together with simple operation and broad usability.
If your question is not addressed through these resources, you can fill out the online form below and we will answer your question as soon as possible.
|
There is no product in your cart. |
CD Biosynsis is a leading customer-focused biotechnology company dedicated to providing high-quality products, comprehensive service packages, and tailored solutions to support and facilitate the applications of synthetic biology in a wide range of areas.