Dataset Information

MoVE identifies metabolic valves to switch between phenotypic states.

ABSTRACT: Metabolism is highly regulated, allowing for robust and complex behavior. This behavior can often be achieved by controlling a small number of important metabolic reactions, or metabolic valves. Here, we present a method to identify the location of such valves: the metabolic valve enumerator (MoVE). MoVE uses a metabolic model to identify genetic intervention strategies which decouple two desired phenotypes. We apply this method to identify valves which can decouple growth and production to systematically improve the rate and yield of biochemical production processes. We apply this algorithm to the production of diverse compounds and obtained solutions for over 70% of our targets, identifying a small number of highly represented valves to achieve near maximal growth and production. MoVE offers a systematic approach to identify metabolic valves using metabolic models, providing insight into the architecture of metabolic networks and accelerating the widespread implementation of dynamic flux redirection in diverse systems.

SUBMITTER: Venayak N

PROVIDER: S-EPMC6294006 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MoVE identifies metabolic valves to switch between phenotypic states.

Venayak Naveen N von Kamp Axel A Klamt Steffen S Mahadevan Radhakrishnan R

Nature communications 20181214 1

Metabolism is highly regulated, allowing for robust and complex behavior. This behavior can often be achieved by controlling a small number of important metabolic reactions, or metabolic valves. Here, we present a method to identify the location of such valves: the metabolic valve enumerator (MoVE). MoVE uses a metabolic model to identify genetic intervention strategies which decouple two desired phenotypes. We apply this method to identify valves which can decouple growth and production to syst ...[more]

PMID: 30552335

Similar Datasets

Project description:Cell adhesion complexes (CACs), which are activated by ligand binding, play key roles in many cellular functions ranging from cell cycle regulation to mediation of cell extracellular matrix adhesion. Inspired by single molecule pulling experiments using atomic force spectroscopy on leukocyte function-associated antigen-1 (LFA-1), expressed in T-cells, bound to intercellular adhesion molecules (ICAM), we performed constant loading rate (rf) and constant force (F) simulations using the self-organized polymer model to describe the mechanism of ligand rupture from CACs. The simulations reproduce the major experimental finding on the kinetics of the rupture process, namely, the dependence of the most probable rupture forces (f*s) on ln rf (rf is the loading rate) exhibits two distinct linear regimes. The first, at low rf, has a shallow slope, whereas the slope at high rf is much larger, especially for a LFA-1/ICAM-1 complex with the transition between the two occurring over a narrow rf range. Locations of the two transition states (TSs) extracted from the simulations show an abrupt change from a high value at low rf or constant force, F, to a low value at high rf or F. This unusual behavior in which the CACs switch from one brittle (TS position is a constant over a range of forces) state to another brittle state is not found in forced-rupture in other protein complexes. We explain this novel behavior by constructing the free energy profiles, F(?)s, as a function of a collective reaction coordinate (?), involving many key charged residues and a critical metal ion (Mg2+). The TS positions in F(?), which quantitatively agree with the parameters extracted using the Bell-Evans model, change abruptly at a critical force, demonstrating that it, rather than the molecular extension, is a good reaction coordinate. Our combined analyses using simulations performed in both the pulling modes (constant rf and F) reveal a new mechanism for the two loading regimes observed in the rupture kinetics in CACs.

Dataset Information

MoVE identifies metabolic valves to switch between phenotypic states.

Publications

MoVE identifies metabolic valves to switch between phenotypic states.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets