Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling.

Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.

Nondestructive Photopatterning of Heavy-Metal-Free Quantum Dots.

Electroluminescence from quantum dots (QDs) is a suitable photon source for futuristic displays offering hyper-realistic images with free-form factors. Accordingly, a nondestructive and scalable process capable of rendering multicolored QD patterns on a scale of several micrometers needs to be established. Here, nondestructive direct photopatterning for heavy-metal-free QDs is reported using branched light-driven ligand crosslinkers (LiXers) containing multiple azide units. The branched LiXers effectively interlock QD films via photo-crosslinking native aliphatic QD surface ligands without compromising the intrinsic optoelectronic properties of QDs. Using branched LiXers with six sterically engineered azide units, RGB QD patterns are achieved on the micrometer scale. The photo-crosslinking process does not affect the photoluminescence and electroluminescence characteristics of QDs and extends the device lifetime. This nondestructive method can be readily adapted to industrial processes and make an immediate impact on display technologies, as it uses widely available photolithography facilities and high-quality heavy-metal-free QDs with aliphatic ligands.