Enzymatic target identification with dynamic states

As a metabolic network reaches from a state to a steady state, a subset of its fluxes gradually change. The sequence of intermediate states, called the dynamic state, shows the pattern that the underlying network follows to reach the steady state Understanding this pattern is crucial for metabolic engineering as the intermediate states can lead to undesired effects such as toxicity although the final steady state is a desired one. In this paper, we consider the problem of enzymatic target identification in metabolic networks. Unlike existing strategies, we consider the dynamic behavior of the state changes of the networks. Given a goal pattern for the fluxes of a given network, we aim to find the set of enzyme knockouts that will produce a dynamic state similar to that goal pattern. We consider three distance functions to measure the difference between two dynamic states. These are the Euclidean distance, timewarping distance and pattern distance. Euclidean distance restricts the solu...

• Type: Journal
• Year: 2010
• Where: BCB
• Authors: Bin Song, Sanjay Ranka, Tamer Kahveci
• Keywords: BCB 2010, Bioinformatics, Dynamic States, Goal Pattern, Steady State