CeCaFLUX

CeCaFLUX computes metabolic flux distributions from instationary ^13C (INST-MFA) labeling data to enable quantitative analysis of dynamic cellular metabolism.

Key Features:

• Instationary ^13C (INST-MFA) support: Performs instationary ^13C metabolic flux analysis to model time-dependent isotopic labeling dynamics.
• Evolutionary optimization and parallel execution: Employs evolutionary optimization algorithms executed in parallel to fit flux distributions to ^13C-labeled data and improve convergence and computational throughput.
• Real-time visualization: Visualizes flux optimization trajectories and convergence metrics during the optimization process.
• Flux database: Stores and enables comparison of flux distributions to support consistency across INST-MFA studies.

Scientific Applications:

• Metabolic engineering: Estimates flux distributions to guide strain optimization and microbial production pathway engineering.
• Systems biology: Analyzes dynamic metabolic responses and network behavior using instationary ^13C labeling experiments.
• Fluxome characterization: Elucidates metabolic network fluxes in diverse organisms from ^13C-labeled time-course data.

Methodology:

Optimizes metabolic flux distributions from instationary ^13C data using evolutionary algorithms executed in parallel and accounts for dynamic changes in metabolism over time.