Microbial Metabolic Flux Analysis Service - Creative Proteomics Metabolomics

Q: How does Microbial MFA resolve the EMP vs. ED pathway split?

Using position‑specific tracers (such as [1‑ 13 C]glucose), the Embden‑Meyerhof‑Parnas (EMP) pathway yields a 1:1 mixture of labeled and unlabeled trioses, while the Entner‑Doudoroff (ED) pathway cleaves KDPG directly into [1‑ 13 C]pyruvate and unlabeled GAP. Relative pathway throughput between EMP, ED, and the pentose phosphate pathway is estimated using position‑sensitive fragment mass isotopologues, parallel tracer regimes, and stoichiometric model fitting with reported confidence intervals.

Q: What microbial species and fermentation modes are supported?

We support diverse microbial hosts including Escherichia coli , Bacillus subtilis , Corynebacterium glutamicum , Pseudomonas putida , Saccharomyces cerevisiae , Pichia pastoris , Aspergillus niger , Trichoderma reesei , and Synechocystis . Supported cultivation modes include batch, fed‑batch, chemostat continuous cultures, and microtiter plate cultivations (with steady‑state vs. dynamic modeling assigned during feasibility review).

Q: What statistical metrics validate the calculated microbial fluxes?

Calculated fluxes are validated using global Chi‑square goodness‑of‑fit testing against experimental variance. Furthermore, non‑linear profile likelihood estimation computes 95% confidence intervals for identifiable net and exchange fluxes to confirm that reported parameters are mathematically identifiable and robustly constrained by the experimental dataset.

Microbial Metabolic Flux Tracing Across Strain Engineering Hosts

Microbial cell factories exhibit rapid metabolic turnover and flexible pathway routing to balance growth, redox maintenance, and target product synthesis. Because static biomass yields (OD₆₀₀, gDW/L) and end-point fermentation titers cannot reveal internal pathway bottlenecks, futile cycles, or carbon diversion into competing overflow pathways, isotope flux analysis is essential. Our Microbial MFA service quantifies intracellular carbon traffic across bacteria, yeasts, filamentous fungi, and cyanobacteria—resolving central carbon partitioning across glycolytic pathway metabolic flux analysis and central carbon metabolic flux analysis to optimize carbon yield:

(i) Glycolytic Pathway Splits (EMP vs. ED vs. PPP): Estimating the carbon split between the Embden-Meyerhof-Parnas (EMP) pathway, Entner-Doudoroff (ED) pathway, and oxidative Pentose Phosphate Pathway (oxPPP) using position-sensitive fragments and model fitting.

(ii) Overflow Metabolism & Fermentation Byproducts: Quantifying carbon diversion into acetate (PoxB, Pta-AckA), ethanol (AdhE), and lactate (LdhA) under high-glucose batch or fed-batch bioreactor conditions.

(iii) TCA Cycle Architecture & Glyoxylate Shunt: Measuring the stoichiometric partitioning between oxidative TCA cycling, the isocitrate lyase (AceA) glyoxylate shunt bypass, and anaplerotic phosphoenolpyruvate carboxylase (Ppc) vs. carboxykinase (Pck) fluxes within the TCA cycle pathway metabolic flux analysis and associated anaplerotic network.

(iv) Autotrophic Carbon Fixation in Cyanobacteria: Resolving Calvin-Benson-Bassham (CBB) cycle throughput, photorespiratory 2-phosphoglycolate recycling, and day/night mixotrophic flux partitioning via time-resolved INST-MFA.

(v) Yeast & Fungal Precursor Allocation: Tracking precursor allocation into recombinant proteins, lipids, and secondary metabolites across S. cerevisiae, P. pastoris, and Aspergillus, with compartmental acetyl-CoA pools assessed when supported by compartment-specific measurements or sufficient model constraints.

Looking for microbial metabolite concentration measurements? If your study focuses on quantifying absolute or relative metabolite pool sizes (μmol/g dry cell weight or mg/L) without stable isotope tracing, please explore our Microbial Metabolomics Service.

Analytical & Quenching Considerations in Microbial Fluxomics

Microbial fluxomics requires specialized sampling strategies to capture fast-turnover intermediates while avoiding cold-shock leakage and spectral artifacts:

Rapid Sampling & Low-Temperature Quenching: Rapid vacuum filtration or cold organic solvent quenching protocols are evaluated per host matrix to arrest fast-turnover central intermediates while minimizing cold-shock leakage and sampling artifacts.
Metabolic & Isotopic Steady-State Verification: For studies targeting steady-state ¹³C-MFA, metabolic and isotopic steady states are experimentally verified during balanced exponential growth; non-steady-state fermentations or transient phases transition to dynamic modeling workflows.
Dual LC-MS & GC-MS Orthogonal Profiling: High-resolution LC-MS resolves intact phosphorylated intermediates (G6P, 6PG, PEP, FBP), while GC-MS-based metabolic flux analysis provides fragment-specific carbon isotopologue constraints from hydrolyzed biomass proteinogenic amino acids, which offer high stability, high abundance, and time-integrated labeling information across biomass accumulation.
Natural Abundance Deconvolution: Algorithmic matrix deconvolution (IsoCor, AccuCor) strips natural ¹³C, ¹⁵N, ²H, ¹⁸O, and silyl tag backgrounds across all measured fragments.

Tracer Selection Matrix for Microbial Host Systems

Tracer selection is customized based on microbial species, pathway topology, and metabolic engineering objectives:

Microbial Host System	Recommended Tracer Regime	Target Pathway Node	Diagnostic Flux Readout
Escherichia coli & Enterobacteria	[1-¹³C]Glucose + [U-¹³C₆]Glucose (80:20 ratio)	EMP vs. ED vs. PPP split, acetate overflow, Ppc anaplerosis	Fragment-specific amino acid isotopologues resolving EMP vs. ED partitioning, oxPPP throughput, and glyoxylate shunt bypass.
Saccharomyces cerevisiae & Yeasts	[1,2-¹³C₂]Glucose + [U-¹³C₆]Glucose	Crabtree ethanol overflow, PC vs. PDH bypass, glycerol shunt	Fractional glucose shunted to ethanol (M+2), glycerol synthesis, and mitochondrial vs. cytosolic precursor allocation.
Pichia pastoris (Komagataella) (Subject to project-specific feasibility review)	[U-¹³C₆]Glucose + [¹³C]Methanol (co-feeding)	Methanol dissimilation vs. assimilation, AOX promoter flux	Formaldehyde dissimilation into CO₂ vs. xylulose-5-P assimilation feeding recombinant protein synthesis.
*Filamentous Fungi (Aspergillus)* (Subject to project-specific feasibility review)	[1-¹³C]Glucose + [U-¹³C₆]Glucose	Organic acid overflow (citrate, itaconate), secondary metabolites	Cytosolic citrate cleavage, cis-aconitate decarboxylase flux, itaconate vs. TCA cycle partitioning.
Cyanobacteria & Autotrophs (Subject to project-specific feasibility review)	NaH¹³CO₃ (time-resolved pulse-chase)	CBB carbon fixation, day/night mixotrophic flux switching	Time-series Rubisco net fixation velocity, photorespiratory phosphoglycolate recycling, and glycogen accumulation requiring non-stationary INST-MFA.

Technical Platform Capabilities for Microbial MFA

EMU-Based Isotopomer Modeling: Mathematical Elementary Metabolite Unit (EMU) algorithms solving complex microbial reaction networks and parallel tracer combinations within our 13C metabolic flux analysis framework.
Biomass Composition & Macromolecular Balance: Integrates strain-specific biomass equations (protein, RNA, DNA, lipid, carbohydrate fractions) to enforce strict precursor drains for cell growth.
Dynamic and Non-Stationary INST-MFA: Supports steady-state metabolic flux analysis alongside rapid dynamic and INST metabolic flux analysis for autotrophic cyanobacteria, slow-growing hosts, and transient bioreactor shifts.
Redox Demand & NADPH Pathway Evaluation: When supported by appropriate tracer design and network constraints, evaluates NADPH-producing pathway contributions (oxPPP, Isocitrate Dehydrogenase, Malic Enzyme) and cellular redox demands.
Complementary Positional Validation: Orthogonal validation of position-specific carbon labeling via high-field NMR-based metabolic flux analysis.

End-to-End Microbial MFA Workflow

Strain Scoping & Network Formulation

We define the stoichiometric reaction network, carbon atom transition mappings, and candidate ¹³C tracer schemes matched to your microbial host and cultivation regime.

Bioreactor Cultivation & Rapid Sampling

Strains are cultured in tracer-formulated media under verified steady-state or dynamic conditions, followed by matrix-optimized fast filtration or cold solvent quenching.

Mass Spectrometry Data Acquisition

Intracellular intermediates and biomass-derived amino acids are profiled for mass isotopologues on LC-MS and GC-MS; bioreactor exchange rates are quantified from matched supernatants.

Natural Abundance Deconvolution & QC

Raw mass isotopologue distributions are algorithmically corrected for natural isotope background using IsoCor/AccuCor, verified with pooled quality control standards.

EMU Mathematical Modeling & Optimization

Non-linear parameter estimation calculates intracellular net and exchange fluxes, backed by Chi-square goodness-of-fit testing and profile likelihood 95% confidence intervals for identifiable parameters.

Publication-Ready Flux Maps & Deliverables

Delivery of comprehensive quantitative datasets, model configuration files, and publication-oriented vector pathway maps supported by our fluxomics data analysis service.

Six-step workflow for microbial metabolic flux analysis

Sample Submission & Microbial Sampling Guidelines

Sampling protocols and biomass requirements are finalized during feasibility review based on microbial host, growth rate, and cultivation mode:

Sample Type	Typical Input Guidelines	Harvesting & Quenching Principles	Storage & Transport
Bacterial Cell Pellets (E. coli, Bacillus)	0.5–2.0 mg dry cell weight (1–3 mL culture at OD₆₀₀ = 1.0–2.0)	Fast vacuum filtration on 0.45 μm membrane filters or direct −40 °C solvent quenching, immediate liquid nitrogen freeze.	Store at −80 °C; ship on dry ice
Yeast Biomass (S. cerevisiae, Pichia)	1.0–5.0 mg dry cell weight (2–5 mL culture)	Rapid centrifugation (4 °C, 4,000 × g, 30 s) or fast filtration, aspirate supernatant, freeze pellet in liquid nitrogen.	Store at −80 °C; ship on dry ice
Filamentous Fungi (Aspergillus, Trichoderma)	5–20 mg dry mycelial biomass	Filter mycelial mat through Miracloth/vacuum filter, rinse with cold saline, snap-freeze in liquid nitrogen.	Store at −80 °C; ship on dry ice
Cyanobacteria & Microalgae	2–10 mg dry biomass (5–10 mL culture)	Fast filtration under active illumination to prevent dark shifts, instant liquid nitrogen immersion.	Store at −80 °C; ship on dry ice
Spent Fermentation Broth	≥ 500 μL matched supernatant	Collect matched broth across sampling points, filter through 0.22 μm syringe filter, aliquot for extracellular rate profiling.	Store at −80 °C; ship on dry ice

Deliverables & Quantitative Reporting from Microbial MFA

Deconvoluted Microbial MID Matrix: Natural-abundance-corrected fractional labeling (M+0 to M+n) across central intermediates, organic acids, and proteinogenic amino acids (.csv / Excel).
Bioreactor Boundary Exchange Rates: Quantified specific substrate consumption, byproduct secretion, and specific growth rates (delivered when boundary rates are measured).
Stoichiometric Model Specifications: Complete reaction definitions, carbon atom transition mappings, and project configuration files.
Calculated Reaction Rates & Split Ratios: Net fluxes (v_net) and exchange velocities (v_xch) with accompanying sensitivity analysis and 95% confidence intervals for identifiable fluxes (delivered when boundary rates are measured and parameters are identifiable).
Statistical Fit Diagnostics: Sum of squared residuals (SSR), Chi-square goodness-of-fit evaluation, and residual diagnostic plots.
Publication-Ready Pathway Maps: High-resolution vector diagrams (PDF/SVG) illustrating microbial carbon flux distribution with flux-scaled arrow widths.

Microbial mass isotopologue distribution bar plot resolving EMP, ED, and PPP splits

Mass isotopologue distribution (MID) bar plot of alanine, aspartate, and glutamate resolving the EMP vs. ED pathway split and glyoxylate shunt flux in engineered bacteria.

Quantitative microbial metabolic pathway flux map with overflow branching and confidence intervals

Publication-oriented microbial metabolic pathway flux map illustrating estimated reaction rates, acetate overflow, and 95% confidence intervals.

How We Scope Microbial 13C-MFA for Strain Engineering and Fermentation

Study feasibility, tracer selection, and mathematical modeling strategies depend directly on microbial host physiology, cultivation mode, and target pathways. Rather than applying a uniform protocol, we evaluate projects against defined experimental scenarios:

Project Scenario	Recommended Strategy	Required Inputs & Feasibility Constraints
Exponential-phase bacterial / yeast strain comparison	Steady-state ¹³C-MFA	Specific growth rate (μ), substrate uptake rate, product/byproduct secretion rates, dry cell weight (DCW), and isotopic steady-state biomass amino acid MIDs.
Fed-batch production phase	Dynamic / non-stationary feasibility assessment	Feed profile and nutrient feeding curves, residual substrate concentrations, dilution rate, time-series intermediate MIDs, and exhaust gas CO₂/O₂ (where available).
Filamentous fungi / fungal pellets	Sampling and heterogeneity feasibility review	Morphology assessment (pellet vs. dispersed mycelium), sampling representativeness, rapid biomass/broth separation protocol, and cell wall disruption validation.
Cyanobacteria / autotrophic systems	Time-resolved INST-MFA	Calibrated light regime, continuous ¹³CO₂ or NaH¹³CO₃ delivery, gas exchange rates, and rapid sub-minute time-series labeling data.
Non-model microbes or synthetic pathways	Atom mapping & measurable node verification	Host genotype/knockout annotations, complete medium carbon source composition, target pathway stoichiometry, and analytical coverage of target product bypasses.

Why is rapid sampling and quenching critical for microbial flux analysis?

Microbial central intermediates turn over rapidly during active growth. Delayed harvesting or unoptimized washing can expose cells to transient hypoxia or cold shock, risking metabolic leakage and spectral artifacts. Validated fast filtration or low-temperature organic quenching halts enzyme interconversion within seconds to preserve representative in vivo mass isotopologue distributions.

How does Microbial MFA resolve the EMP vs. ED pathway split?

Using position-specific tracers (such as [1-¹³C]glucose), the Embden-Meyerhof-Parnas (EMP) pathway yields a 1:1 mixture of labeled and unlabeled trioses, while the Entner-Doudoroff (ED) pathway cleaves KDPG directly into [1-¹³C]pyruvate and unlabeled GAP. Relative pathway throughput between EMP, ED, and the pentose phosphate pathway is estimated using position-sensitive fragment mass isotopologues, parallel tracer regimes, and stoichiometric model fitting with reported confidence intervals.

Can Microbial MFA quantify the glyoxylate shunt bypass?

Yes. The glyoxylate shunt bypasses the two oxidative decarboxylation steps of the TCA cycle (isocitrate dehydrogenase and α-ketoglutarate dehydrogenase) via isocitrate lyase (AceA) and malate synthase (AceB). By using [1-¹³C]glucose or [U-¹³C₆]glucose and measuring the isotopologue patterns of α-ketoglutarate (glutamate) versus succinate/malate (aspartate), EMU network modeling resolves the flux fraction passing through the glyoxylate shunt.

What microbial species and fermentation modes are supported?

We support diverse microbial hosts including Escherichia coli, Bacillus subtilis, Corynebacterium glutamicum, Pseudomonas putida, Saccharomyces cerevisiae, Pichia pastoris, Aspergillus niger, Trichoderma reesei, and Synechocystis. Supported cultivation modes include batch, fed-batch, chemostat continuous cultures, and microtiter plate cultivations (with steady-state vs. dynamic modeling assigned during feasibility review).

What statistical metrics validate the calculated microbial fluxes?

Calculated fluxes are validated using global Chi-square goodness-of-fit testing against experimental variance. Furthermore, non-linear profile likelihood estimation computes 95% confidence intervals for identifiable net and exchange fluxes to confirm that reported parameters are mathematically identifiable and robustly constrained by the experimental dataset.