Search for: All records

Abstract Glycans are the major components of the cellular membranes and mediate many cellular processes via their interactions with lectins. A kinetic Monte Carlo (kMC) model was proposed previously to incorporate the key features of glycan‐lectin interactions such as multivalency and glycan diffusion, and its accuracy has been validated by experiments. However, computational cost of the kMC model is its major bottleneck. In this study, a hybrid model combining a partial differential equation (PDE) with the kMC model is proposed to greatly reduce the computational cost while preserving the accuracy. Specifically, glycan diffusion is simulated by the PDE for improving computational efficiency since the glycan diffusion execution through the kMC is computationally expensive. The hybrid PDE‐kMC model is employed to simulate the binding dynamics between cholera toxin subunit B and gangliosides on cellular membranes. The accuracy and efficiency of the proposed model was demonstrated by comparing with the sole kMC model. 

Abstract In this work, we propose the integration of Koopman operator methodology with Lyapunov‐based model predictive control (LMPC) for stabilization of nonlinear systems. The Koopman operator enables global linear representations of nonlinear dynamical systems. The basic idea is to transform the nonlinear dynamics into a higher dimensional space using a set of observable functions whose evolution is governed by the linear but infinite dimensional Koopman operator. In practice, it is numerically approximated and therefore the tightness of these linear representations cannot be guaranteed which may lead to unstable closed‐loop designs. To address this issue, we integrate the Koopman linear predictors in an LMPC framework which guarantees controller feasibility and closed‐loop stability. Moreover, the proposed design results in a standard convex optimization problem which is computationally attractive compared to a nonconvex problem encountered when the original nonlinear model is used. We illustrate the application of this methodology on a chemical process example. 

Abstract Over the past few decades, several data‐driven methods have been developed for identifying a model that accurately describes the process dynamics. Lately, sparse identification of nonlinear dynamics (SINDy) has delivered promising results for various nonlinear processes. However, at any instance of plant‐model mismatch or process upset, retraining the model using SINDy is computationally expensive and cannot guarantee to catch up with rapidly changing dynamics. Hence, we propose operable adaptive sparse identification of systems (OASIS) framework that extends the capabilities of SINDy for accurate, automatic, and adaptive approximation of process models. First, we use SINDy to obtain multiple models from historical data for varying input settings. Next, using these models and their training data, we build a deep neural network that is incorporated in a model predictive control framework for closed‐loop operation. We demonstrate the OASIS methodology on the identification and control of a continuous stirred tank reactor. 

Abstract Glycans are the most abundant fundamental biomolecules, but profiling glycans is challenging due to their structural complexity. To address this, a novel glycan detection platform is developed by integrating surface‐enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. Boronic acid receptors bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. Unlike prior studies that focus on designing a boronic acid with high binding selectivity toward a target glycan, this sensor is designed to analyze overall changes in spectral patterns using machine learning algorithms. For proof‐of‐concept, 4‐mercaptophenylboronic acid (4MBA) and 1‐thianthrenylboronic acid (1TBA) are used for glycan detection. The sensing platform successfully recognizes the stereoisomers and the structural isomers with different glycosidic linkages. The collective spectra that combine the spectra from both boronic acid receptors improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. This low‐cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent comparative glycan screening in standard biological laboratories.