CD Biosynsis offers advanced Pseudomonas putida-Based Assay and Modeling Services, integrating cutting-edge experimental analysis with powerful computational modeling to facilitate rational strain design and optimization. P. putida is a robust, versatile microbial host known for its high respiratory capacity and tolerance to inhibitory compounds, making it ideal for biomanufacturing and bioremediation. Our services move beyond simple genetic modification by providing a deep, quantitative understanding of the host's behavior. We combine high-precision In Vitro and In Vivo assays (metabolomics, fluxomics, protein activity) with Constraint-Based Modeling (CBM) and Kinetic Modeling to accurately predict metabolic flux, optimize gene expression levels, and pinpoint systemic bottlenecks. This integrated approach minimizes trial-and-error experimentation, ensuring rapid and predictable development of high-performance P. putida strains.
Get a QuoteThe highly flexible and complex metabolic network of Pseudomonas putida demands sophisticated tools to guide effective metabolic engineering efforts. Our Assay and Modeling platform bridges the gap between genotypic edits and phenotypic outcomes. By experimentally characterizing key cellular metrics (Assays) and using this data to parameterize predictive Models, we can accurately simulate the effects of genetic modifications before they are built in the lab. This capability is central to the Design-Build-Test-Learn (DBTL) cycle [Image of Design-Build-Test-Learn Cycle] , allowing our clients to make informed decisions, prioritize the most effective genetic targets, and drastically reduce the number of iterative experiments required to achieve optimal product yield and purity.
Metabolomics Profiling (Targeted/Untargeted)
Comprehensive GC-MS/LC-MS analysis of intracellular and extracellular metabolites to quantify pathway intermediates and track precursor consumption.
Fluxomics (Isotope Tracing)
Measurement of metabolic fluxes via 13C labeling and mass spectrometry to accurately determine carbon flow through central metabolism and target pathways .
Transcriptomics & Proteomics
Quantification of mRNA (RNA-seq) and protein levels to map gene expression and enzyme concentrations in response to engineering and environmental changes.
Constraint-Based Modeling (FBA)
Using Flux Balance Analysis (FBA) based on the P. putida genome-scale model to predict maximum theoretical yields, optimize substrate utilization, and propose effective gene knockouts.
Metabolic Control Analysis (MCA)
Identifying the rate-limiting steps (bottlenecks) and determining the sensitivity of metabolic fluxes to changes in enzyme activity or concentration.
Kinetic Modeling
Development of dynamic models to simulate time-dependent changes in cell growth, substrate uptake, and product formation under various fermentation strategies.
Optimal Target Recommendation
Using model outputs (e.g., FBA predictions and MCA results) to recommend the most impactful genetic targets for knockout, knock-in, or expression tuning.
Predictive Promoter Tuning
Calculating the optimal expression level for each enzyme in a pathway to maximize flux, guiding the selection of promoter strength and RBS optimization (using Base Editing).
Fermentation Condition Simulation
Simulating the strain's performance under different media compositions, oxygen levels (due to P. putida's high respiration), and nutrient feeding strategies to optimize industrial process parameters.
We connect high-quality experimental data with predictive simulation to deliver highly efficient strain optimization.
1. Design (Modeling & Prediction)
2. Build (Strain Construction)
3. Test (Assay & Data Generation)
4. Learn (Re-Modeling & Optimization)
Establish a computational model (FBA, Kinetic) based on the target pathway and host.
Simulate effects of potential edits (e.g., knockouts, promoter changes) and predict optimal genetic design.
Generate initial hypothesis and experimental plan.
Execute precision gene editing (CRISPR-Cas9, Base Editing) to construct rationally engineered strains.
Build genetic libraries (e.g., promoter libraries) based on model recommendations.
Integrate new experimental data to validate and refine the computational model.
Identify prediction errors, extract new design rules, and recommend the next set of rational edits for further improvement.
Deliver the predictive model and data-driven optimization strategy.
High-Precision Flux Analysis
Expertise in both experimental 13C-Fluxomics and computational FBA modeling for accurate flux determination and visualization in complex P. putida metabolism.
Data-Driven Rational Design
Computational modeling is performed before and after strain construction to ensure all genetic edits are maximally effective and guided by current data, minimizing trial-and-error.
Host-Specific Model Tuning
Use of the established P. putida KT2440 genome-scale model as a base, customized with multi-omics data specific to the client's engineered or wild-type strain.
Accelerated DBTL Cycle
The synergy between assay (Test) and modeling (Design/Learn) dramatically reduces the number of required R&D iterations, leading to faster strain development.
What is the primary benefit of using FBA for P. putida engineering?
FBA (Flux Balance Analysis) provides a computational prediction of the maximum theoretical yield of a product, allowing engineers to identify the primary limiting factors (e.g., carbon flux bottlenecks) and prioritize gene knockouts for flux redirection before expensive lab work begins.
How do you obtain the data to parameterize your computational models?
Model parameters are derived from high-resolution experimental assays, including metabolite concentrations (Metabolomics), enzyme activity levels (Proteomics), and measured metabolic fluxes (Fluxomics/13C labeling), ensuring high prediction accuracy.
Is your modeling compatible with my specific P. putida strain?
Yes. We use the comprehensive P. putida genome-scale model as a base and then customize it with experimental data unique to your specific engineered or wild-type strain, ensuring the model is highly predictive for your system.
What is the benefit of Kinetic Modeling over FBA?
FBA provides steady-state flux predictions. Kinetic Modeling is more advanced, incorporating time-dependent enzyme kinetics, allowing us to simulate dynamic processes like substrate feeding, induction timing, and lag-phase behavior during industrial fermentation.
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.
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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.