Optimal design of metabolic flux analysis experiments for anchorage-dependent mammalian cells using a cellular automaton model.
Metabolic flux analysis (MFA) is widely used to quantify metabolic pathway activity. Typical applications involve isotopically labeled substrates, which require both metabolic and isotopic steady states for simplified data analysis. For bacterial systems, these steady states are readily achieved in chemostat cultures. However, mammalian cells are often anchorage dependent and experiments are typically conducted in batch or fed-batch systems, such as tissue culture dishes or microcarrier-containing bioreactors. Surface adherence may cause deviations from exponential growth, resulting in metabolically heterogeneous populations and a varying number of cellular "nearest neighbors" that may affect the observed metabolism. Here, we discuss different growth models suitable for deconvoluting these effects and their application to the design and optimization of MFA experiments employing surface-adherent mammalian cells. We describe a stochastic two-dimensional (2D) cellular automaton model, with empirical descriptions of cell number and non-growing cell fraction, suitable for easy application to most anchorage-dependent mammalian cell cultures. Model utility was verified by studying the impact of contact inhibition on the growth rate, specific extracellular flux rates, and isotopic labeling in lactate for MCF7 cells, a commonly studied breast cancer cell line. The model successfully defined the time over which exponential growth and a metabolically homogeneous growing cell population could be assumed. The cellular automaton model developed is shown to be a useful tool in designing optimal MFA experiments.
Department of Chemical Engineering, University of California, Berkeley, California 94720, USA.
Adam L
AL
Meadows
Siddhartha
S
Roy
Douglas S
DS
Clark
Harvey W
HW
Blanch
D001943:Breast Neoplasms / Q000378:metabolism*; D002448:Cell Adhesion; D045744:Cell Line, Tumor; D049109:Cell Proliferation; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000378:metabolism*; D015398:Signal Transduction
D009363:Neoplasm Proteins