Engineering of Polyketide Biosynthetic Pathways for Bioactive Molecules

Polyketides are a large group of structurally diverse natural products that have shown a variety of biological activities. These molecules are synthesized by polyketide synthases (PKSs). PKSs are classified into three types based on their sequence, primary structure, and catalytic mechanism. Because of the bioactivities of polyketide natural products, this study is focused on the engineering of PKS pathways for efficient production of useful bioactive molecules or structural modification to create new molecules for drug development. One goal of this research is to create an efficient method to produce pharmaceutically important molecules. Seven biosynthetic genes from plants and bacteria were used to establish a variety of complete biosynthetic pathways in Escherichia coli to make valuable plant natural products, including four phenylpropanoid acids, three bioactive natural stilbenoids, and three natural curcuminoids. A curcumin analog dicafferolmethane was synthesized by removing a methyltransferase from the curcumin biosynthetic pathway. Furthermore, introduction of a fungal flavin-dependent halogenase into the resveratrol biosynthetic pathway yielded a novel chlorinated molecule 2-chloro-resveratrol. This demonstrated that biosynthetic enzymes from different sources can be recombined like legos to make various plant natural products, which is more efficient (2-3 days) than traditional extraction from plants (months to years). Phenylalanine ammonia-lyase (PAL) is a key enzyme involved in the first biosynthetic step of some plant phenylpropanoids. Based on the biosynthetic pathway of curcuminoids, a novel and efficient visible reporter assay was established for screening of phenylalanine ammonia-lyase (PAL) efficiency in Escherichia coli. The other goal of this research is to characterize and engineer natural product biosynthetic pathways for new bioactive molecules. The biosynthetic gene cluster of the antibacterial compound dutomycin was discovered from Streptomyces minoensis NRRL B-5482 through genome sequencing. Confirmation of the involvement of this gene cluster in dutomycin biosynthesis and creation of a series of new molecules were successfully conducted by rationally modifying the biosynthetic pathway. More importantly, a new demethylated analog of dutomycin was found to have much higher antibacterial activity against Staphylococcus aureus and methicillin-resistant Staphylococcus aureus

Improving controllability of complex networks by rewiring links regularly

Network science have constantly been in the focus of research for the last decade, with considerable advances in the controllability of their structural. However, much less effort has been devoted to study that how to improve the controllability of complex networks. In this paper, a new algorithm is proposed to improve the controllability of complex networks by rewiring links regularly which transforms the network structure. Then it is demonstrated that our algorithm is very effective after numerical simulation experiment on typical network models (Erd\"os-R\'enyi and scale-free network). We find that our algorithm is mainly determined by the average degree and positive correlation of in-degree and out-degree of network and it has nothing to do with the network size. Furthermore, we analyze and discuss the correlation between controllability of complex networks and degree distribution index: power-law exponent and heterogeneit

Connecting Look and Feel: Associating the visual and tactile properties of physical materials

For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin flexible fabric, when draped, tends to look different from a heavy stiff fabric. It also feels different when touched. Using a collection of 118 fabric sample, we captured color and depth images of draped fabrics along with tactile data from a high resolution touch sensor. We then sought to associate the information from vision and touch by jointly training CNNs across the three modalities. Through the CNN, each input, regardless of the modality, generates an embedding vector that records the fabric's physical property. By comparing the embeddings, our system is able to look at a fabric image and predict how it will feel, and vice versa. We also show that a system jointly trained on vision and touch data can outperform a similar system trained only on visual data when tested purely with visual inputs

A Resilience-Oriented Multi-Stage Adaptive Distribution System Planning Considering Multiple Extreme Weather Events

Climate Change May Increase the Risk of an Area Being Hit by Multiple Extreme Weather Events, Which Brings Significant Challenges for Distribution System Planners in an Increasing Renewable Penetration Era. There is an Urgent Need for Planning Approaches to Be More Flexible and Allow for Adaptive Adjustments in the Future to Hedge Against High Uncertainties in Extreme Weather Event Scenarios. in This Work, We Propose a Resilience-Oriented Distribution System Planning Approach that Considers Multiple Extreme Weather Events. a Multi-Stage Hybrid-Stochastic-And-Robust Formulation is Developed to Model Decisions Not Only for Initial Investments, But Also for Adaptive Investments and Emergent Operations in Response to Particular Extreme Events, Meanwhile Considering Both Long-Term and Short-Term Uncertainties. Our Model is Solved by a Novel Progressive Hedging Algorithm that is Embedded with a Nested Column-And-Constraint Generation Method. Case Studies Demonstrate the Benefits of the Proposed Approach in Making Flexible and Affordable Planning Decisions to Protect Distribution Systems Against Multiple Extreme Weather Events