Functional analysis of the Arabidopsis TETRASPANIN gene family in plant growth and development

TETRASPANIN (TET) genes encode conserved integral membrane proteins that are known in animals to function in cellular communication during gamete fusion, immunity reaction and pathogen recognition. In plants, functional information is limited to one of the 17 members of the Arabidopsis TET gene family and to expression data in reproductive stages. Here, the promoter activity of all 17 Arabidopsis TET genes was investigated by pAtTET::NLS-GFP/GUS reporter lines throughout the life cycle, which predicted functional divergence in the paralogous genes per clade. However, partial overlap was observed for many TET genes across the clades, correlating with few phenotypes in single mutants and therefore requiring double mutant combinations for functional investigation. Mutational analysis showed a role for TET13 in primary root growth and lateral root development, and redundant roles for TET5 and TET6 in leaf and root growth through negative regulation of cell proliferation. Strikingly, a number of TET genes were expressed in embryonic and seedling progenitor cells and remained expressed until the differentiation state in the mature plant, suggesting a dynamic function over developmental stages. cis-regulatory elements together with transcription factor binding data provided molecular insight into the site, conditions and perturbations that affect TET gene expression, and positioned the TET genes in different molecular pathways; the data represent a hypothesis-generating resource for further functional analyses

Artificial in its own right

Artificial Cells, , Artificial Ecologies, Artificial Intelligence, Bio-Inspired Hardware Systems, Computational Autopoiesis, Computational Biology, Computational Embryology, Computational Evolution, Morphogenesis, Cyborgization, Digital Evolution, Evolvable Hardware, Cyborgs, Mathematical Biology, Nanotechnology, Posthuman, Transhuman

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

Real-time optimization of an integrated production-inventory-distribution problem.

In today\u27s competitive business environment, companies face enormous pressure and must continuously search for ways to design new products, manufacture and distribute them in an efficient and effective fashion. After years of focusing on reduction in production and operation costs, companies are beginning to look into distribution activities as the last frontier for cost reduction. In addition, an increasing number of companies, large and small, are focusing their efforts on their core competencies which are critical to survive. This results in a widespread practice in industry that companies outsource one or more than one logistics functions to third party logistics providers. By using such logistics expertise, they can obtain a competitive advantage both in cost and time efficiency, because the third party logistics companies already have the equipment, system and experience and are ready to help to their best efforts. In this dissertation, we developed an integrated optimization model of production, inventory and distribution with the goal to coordinate important and interrelated decisions related to production schedules, inventory policy and truckload allocation. Because outsourcing logistics functions to third party logistics providers is becoming critical for a company to remain competitive in the market place; we also included an important decision of selecting carriers with finite truckload and drivers for both inbound and outbound shipments in the model. The integrated model is solved by modified Benders decomposition which solves the master problem by a genetic algorithm. Computational results on test problems of various sizes are provided to show the effectiveness of the proposed solution methodology. We also apply this proposed algorithm on a real distribution problem faced by a large national manufacturer and distributor. It shows that such a complex distribution network with 22 plants, 7 distribution centers, 8 customer zones, 9 products, 16 inbound and 16 outbound shipment carriers in a 12-month planning period can be redesigned within 33 hours. In recent years, multi-agent simulation has been a preferred approach to solve logistics and distribution problems, since these problems are autonomous, distributive, complex, heterogeneous and decentralized in nature and they require extensive intelligent decision making. Another important part in this dissertation involved a development of an agent-based simulation model to cooperate with the optimal solution given by the optimization model. More specifically, the solution given by the optimization model can be inputted as the initial condition of the agent-based simulation model. The agent-based simulation model can incorporate many other factors to be considered in the real world, but optimization cannot handle these as needed. The agent-based simulation model can also incorporate some dynamics we may encounter in the real operations, and it can react to these dynamics in real time. Various types of entities in the entire distribution system can be modeled as intelligent agents, such as suppliers, carriers and customers. In order to build the simulation model more realistic, a sealed bid multiunit auction with an introduction of three parameters a, ß and y is well designed. With the help of these three parameters, each agent makes a better decision in a simple and fast manner, which is the key to realizing real-time decision making. After building such a multi-agent system with agent-based simulation approach, it supports more flexible and comprehensive modeling capabilities which are difficult to realize in a general optimization model. The simulation model is tested and validated on an industrial-sized problem. Numerical results of the agent-based simulation model suggest that with appropriate setting of three parameters the model can precisely represent the preference and interest of different decision makers

Grapevine Vein Clearing Virus: Epidemiological Patterns and Construction of a Clone

Grapevine vein clearing virus (GVCV) is a recently discovered virus belonging to the Badnavirus genus. Characteristic to its name, the virus is associated with a disease where symptoms manifest as pronounced vein-clearing, resulting in severe berry deformation and vine decline in susceptible grape varieties. Sustainable production of wine is dependent on healthy plants. The associated disease is mainly found in Midwest vineyards. Attempts were made in this thesis to provide evidence of causality of the virus to the associated disease and to infer the historical path and migration pattern of GVCV. Conclusions and discussions will provide grape producers with the latest information in designing management strategies to prevent the disease. The results support that GVCV is likely a native endemic virus, which has recently cultivated grapevines. This evidence is crucial in establishing quarantine protocols to prevent the spread of GVCV into new territories and to avoid pandemic in grape-growing regions worldwide

Assignment of new roles for malectin-like domains to understand their divergent evolution

Malectin is a highly-conserved animal lectin from the endoplasmic reticulum (ER), with a quality control function in the N-Glycosylation process. It has a β-sandwich core with long loops connecting the β-sheets. Malectin binding-pocket is in the loops region. Several carbohydrate-binding modules (CBMs) discovered in other domains of life that shared sequence homology with the malectin, were classified and grouped as a novel CBM57 family by Carbohydrate-Active Enzymes (CAZy) database. The members of this family are expected to have a highly conserved β-sandwich core, but high variance in the binding-pocket residues. To investigate if the specificity of these modules is the same as the malectin, a bioinformatic analysis was performed with 315 members of the CBM57 family found in CAZy database. Several programs were used to predict the protein architecture and to analyse the conservation of amino acids sequences, especially in the binding-pocket. Based on this analysis, we predict animal CBM57 modules to have the same specificity as malectin. However, bacterial CBM57 modules in bacteria domain are predicted, after highlighting the modules associated with glycoside hydrolases from family 2, to have various specificities, and thus different biological functions. For verifying these assumptions, a total of 7 CBMs (family 57 and homologous) associated with glycoside hydrolases from family 2 and belonging to the human gut microbiome – Bacteroides ovatus and Bacteroides thetaiotaomicron- were chosen for characterization studies. A re-cloning was initially performed for the recombinant DNAs, changing the His-tag position. Afterwards, expression tests were realized, in which 2 CBMs of different bacteria were expressed in soluble form. The production of the proteins was then performed at a larger scale, followed by affinity chromatography purification. By the analysis of the gels, the eluted samples had high purity and were suitable for characterization studies. Glycan microarrays were performed for determining the binding-specificities of the 2 CBM modules. The CBM module from B.thetaiotaomicron revealed high specificity for pectin polysaccharides, possible recognizing α 1-3 linked galacturonic acid and ramnose. For structural characterization by X-ray crystallography, several crystallization trials were performed. Crystals were obtained for the B.thetaiotaomicron CBM module, which diffracted to high resolution. The structure is, yet, to be solved

Deep Reinforcement Learning for Distribution Network Operation and Electricity Market

The conventional distribution network and electricity market operation have become challenging under complicated network operating conditions, due to emerging distributed electricity generations, coupled energy networks, and new market behaviours. These challenges include increasing dynamics and stochastics, and vast problem dimensions such as control points, measurements, and multiple objectives, etc. Previously the optimization models were often formulated as conventional programming problems and then solved mathematically, which could now become highly time-consuming or sometimes infeasible. On the other hand, with the recent advancement of artificial intelligence technologies, deep reinforcement learning (DRL) algorithms have demonstrated their excellent performances in various control and optimization fields. This indicates a potential alternative to address these challenges. In this thesis, DRL-based solutions for distribution network operation and electricity market have been investigated and proposed. Firstly, a DRL-based methodology is proposed for Volt/Var Control (VVC) optimization in a large distribution network, to effectively control bus voltages and reduce network power losses. Further, this thesis proposes a multi-agent (MA)DRL-based methodology under a complex regional coordinated VVC framework, and it can address spatial and temporal uncertainties. The DRL algorithm is also improved to adapt to the applications. Then, an integrated energy and heating systems (IEHS) optimization problem is solved by a MADRL-based methodology, where conventionally this could only be solved by simplifications or iterations. Beyond the applications in distribution network operation, a new electricity market service pricing method based on a DRL algorithm is also proposed. This DRL-based method has demonstrated good performance in this virtual storage rental service pricing problem, whereas this bi-level problem could hardly be solved directly due to a non-convex and non-continuous lower-level problem. These proposed methods have demonstrated advantageous performances under comprehensive case studies, and numerical simulation results have validated the effectiveness and high efficiency under different sophisticated operation conditions, solution robustness against temporal and spatial uncertainties, and optimality under large problem dimensions