Modulation of Rho Guanine Exchange Factor Lfc Activity by Protein Kinase A-Mediated Phosphorylation▿

Lfc is a guanine nucleotide exchange factor (GEF) for Rho that demonstrates an unusual ability to associate with microtubules. While several phosphorylated residues have been detected in the Lfc polypeptide, the mechanism(s) by which phosphorylation regulates the exchange activity of Lfc remains unclear. We confirm that Lfc is a phosphorylated protein and demonstrate that 14-3-3 interacts directly and in a phosphorylation-dependent manner with Lfc. We identify AKAP121 as an Lfc-binding protein and show that Lfc is phosphorylated in an AKAP-dependent manner by protein kinase A (PKA). Forskolin treatment induced 14-3-3 binding to Lfc and suppressed the exchange activity of wild-type Lfc on RhoA. Importantly, a mutant of Lfc that is unable to associate with 14-3-3 proteins was resistant to inhibition by forskolin. Tctex-1, a dynein motor light chain, binds to Lfc in a competitive manner with 14-3-3

A new reliability-based data-driven approach for noisy experimental data with physical constraints

Data Science has burst into simulation-based engineering sciences with an impressive impulse. However, data are never uncertainty-free and a suitable approach is needed to face data measurement errors and their intrinsic randomness in problems with well-established physical constraints. As in previous works, this problem is here faced by hybridizing a standard mathematical modeling approach with a new data-driven solver accounting for the phenomenological part of the problem, with the aim of finding a solution point, satisfying some constraints, that minimizes a distance to a given data-set. However, unlike such works that are established in a deterministic framework, we use the Mahalanobis distance in order to incorporate statistical second order uncertainty of data in computations, i.e. variance and correlation. We develop the underlying stochastic theoretical framework and establish the fundamental mathematical and statistical properties. The performance of the resulting reliability-based data-driven procedure is evaluated in a simple but illustrative unidimensional problem as well as in a more realistic solution of a 3D structural problem with a material with intrinsically random constitutive behavior as concrete. The results show, in comparison with other data-driven solvers, better convergence, higher accuracy, clearer interpretation, and major flexibility besides the relevance of allowing uncertainty management with low computational demand