6,779 research outputs found
Kinetic behavior of the general modifier mechanism of Botts and Morales with non-equilibrium binding
In this paper, we perform a complete analysis of the kinetic behavior of the
general modifier mechanism of Botts and Morales in both equilibrium steady
states and non-equilibrium steady states (NESS). Enlightened by the
non-equilibrium theory of Markov chains, we introduce the net flux into
discussion and acquire an expression of product rate in NESS, which has clear
biophysical significance. Up till now, it is a general belief that being an
activator or an inhibitor is an intrinsic property of the modifier. However, we
reveal that this traditional point of view is based on the equilibrium
assumption. A modifier may no longer be an overall activator or inhibitor when
the reaction system is not in equilibrium. Based on the regulation of enzyme
activity by the modifier concentration, we classify the kinetic behavior of the
modifier into three categories, which are named hyperbolic behavior,
bell-shaped behavior, and switching behavior, respectively. We show that the
switching phenomenon, in which a modifier may convert between an activator and
an inhibitor when the modifier concentration varies, occurs only in NESS.
Effects of drugs on the Pgp ATPase activity, where drugs may convert from
activators to inhibitors with the increase of the drug concentration, are taken
as a typical example to demonstrate the occurrence of the switching phenomenon.Comment: 19 pages, 10 figure
Unifying and Merging Well-trained Deep Neural Networks for Inference Stage
We propose a novel method to merge convolutional neural-nets for the
inference stage. Given two well-trained networks that may have different
architectures that handle different tasks, our method aligns the layers of the
original networks and merges them into a unified model by sharing the
representative codes of weights. The shared weights are further re-trained to
fine-tune the performance of the merged model. The proposed method effectively
produces a compact model that may run original tasks simultaneously on
resource-limited devices. As it preserves the general architectures and
leverages the co-used weights of well-trained networks, a substantial training
overhead can be reduced to shorten the system development time. Experimental
results demonstrate a satisfactory performance and validate the effectiveness
of the method.Comment: To appear in the 27th International Joint Conference on Artificial
Intelligence and the 23rd European Conference on Artificial Intelligence,
2018. (IJCAI-ECAI 2018
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