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Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions. 

Systems that depend on matching often exhibit scale economies, whereby increased participation leads to improved performance for all users. This paper examines the presence of such increasing returns to scale in carpool matching. Data from Scoop, a carpooling app, is used to demonstrate this phenomenon across various markets using regression. As the number of requests to carpool in a certain market rises, the share of proposed matches that users accept rises, while the extra distance traveled to accommodate these carpools declines. These relationships hold in four specifications of the regression model, and they suggest there are increasing returns to scale in matching. 

Model reduction methods usually focus on the error performance analysis; however, in presence of uncertainties, it is important to analyze the robustness properties of the error in model reduction as well. This problem is particularly relevant for engineered biological systems that need to function in a largely unknown and uncertain environment. We give robustness guarantees for structured model reduction of linear and nonlinear dynamical systems under parametric uncertainties. We consider a model reduction problem where the states in the reduced model are a strict subset of the states of the full model, and the dynamics for all of the other states are collapsed to zero (similar to quasi‐steady‐state approximation). We show two approaches to compute a robustness guarantee metric for any such model reduction—a direct linear analysis method for linear dynamics and a sensitivity analysis based approach that also works for nonlinear dynamics. Using the robustness guarantees with an error metric and an input‐output mapping metric, we propose an automated model reduction method to determine the best possible reduced model for a given detailed system model. We apply our method for the (1) design space exploration of a gene expression system that leads to a new mathematical model that accounts for the limited resources in the system and (2) model reduction of a population control circuit in bacterial cells.

 

Monolayer hexagonal boron nitride (hBN) has been widely considered a fundamental building block for 2D heterostructures and devices. However, the controlled and scalable synthesis of hBN and its 2D heterostructures has remained a daunting challenge. Here, an hBN/graphene (hBN/G) interface‐mediated growth process for the controlled synthesis of high‐quality monolayer hBN is proposed and further demonstrated. It is discovered that the in‐plane hBN/G interface can be precisely controlled, enabling the scalable epitaxy of unidirectional monolayer hBN on graphene, which exhibits a uniform moiré superlattice consistent with single‐domain hBN, aligned to the underlying graphene lattice. Furthermore, it is identified that the deep‐ultraviolet emission at 6.12 eV stems from the 1s‐exciton state of monolayer hBN with a giant renormalized direct bandgap on graphene. This work provides a viable path for the controlled synthesis of ultraclean, wafer‐scale, atomically ordered 2D quantum materials, as well as the fabrication of 2D quantum electronic and optoelectronic devices.

 

Ultrawide‐bandgap semiconductors such as AlN, BN, and diamond hold tremendous promise for high‐efficiency deep‐ultraviolet optoelectronics and high‐power/frequency electronics, but their practical application has been limited by poor current conduction. Through a combined theoretical and experimental study, it is shown that a critical challenge can be addressed for AlN nanostructures by using N‐rich epitaxy. Under N‐rich conditions, the p‐type Al‐substitutional Mg‐dopant formation energy is significantly reduced by 2 eV, whereas the formation energy for N‐vacancy related compensating defects is increased by ≈3 eV, both of which are essential to achieve high hole concentrations of AlN. Detailed analysis of the current−voltage characteristics of AlN p‐i‐n diodes suggests that current conduction is dominated by hole‐carrier tunneling at room temperature, which is directly related to the activation energy of Mg dopants. At high Mg concentrations, the dispersion of Mg acceptor energy levels leads to drastically reduced activation energy for a portion of Mg dopants, evidenced by the small tunneling energy of 67 meV, which explains the efficient current conduction and the very small turn‐on voltage (≈5 V) for the diodes made of nanoscale AlN. This work shows that nanostructures can overcome the dopability challenges of ultrawide‐bandgap semiconductors and significantly increase the efficiency of devices.