אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Motivation: Isotope tracing coupled with Metabolic Flux Analysis (MFA) is a commonly used approach for quantifying cellular metabolic fluxes. Isotope tracing involves feeding cells with isotopic labeled nutrients and tracking the labeling of metabolites via mass spectrometry and NMR. MFA computationally analyzes these isotopic measurements to infer flux. A major limitation of MFA is its strict reliance on computationally hard non-convex optimizations, requiring heuristic solving that does not necessarily converge to optimal solutions and may be of a poor running time performance. Results: Here, we present a novel computational approach, Constraint-Based Isotope Tracing (CBIT), for efficient inference of metabolic flux directly from isotope tracing data – without relying on non-convex optimizations. The CBIT algorithm identifies lower and upper bounds on the most likely flux through each reaction in a metabolic network directly based on experimental isotopic labeling data. It is based on local inference of bounds on relative fluxes through converging reactions and bounds on the abundance of isotopomers (i.e. distinct labeling patterns of metabolites), and the propagation of these bounds throughout the network. The analysis of feasible flux bounds is akin to constraint-based modeling (CBM), a common approach for analyzing flux via genome-scale metabolic networks. Applying CBIT to central metabolism of a T cell leukemia cell line, we show that CBIT infers tight bounds (<6% of glucose uptake rate) on all reactions in the employed network model. An important feature of CBIT is providing a tractable explanation to how each flux bound was derived based on the experimental measurements. The fast running time of CBIT is shown to enable optimal design of isotope tracing experiments, involving numerous repeated flux estimations. Overall, we expect CBIT to be a highly useful tool for designing and analyzing results of isotope tracing experiments.