GAME9 regulates the biosynthesis of steroidal alkaloids and upstream isoprenoids in the plant mevalonate pathway

Steroidal glycoalkaloids (SGAs) are cholesterol-derived molecules produced by solanaceous species. They contribute to pathogen defence but are toxic to humans and considered as anti-nutritional compounds. Here we show that GLYCOALKALOID METABOLISM 9 (GAME9), an APETALA2/Ethylene Response Factor, related to regulators of alkaloid production in tobacco and Catharanthus roseus, controls SGA biosynthesis. GAME9 knockdown and overexpression in tomato and potato alters expression of SGAs and upstream mevalonate pathway genes including the cholesterol biosynthesis gene STEROL SIDE CHAIN REDUCTASE 2 (SSR2). Levels of SGAs, C24-alkylsterols and the upstream mevalonate and cholesterol pathways intermediates are modified in these plants. Delta(7)-STEROL-C5(6)-DESATURASE (C5-SD) in the hitherto unresolved cholesterol pathway is a direct target of GAME9. Transactivation and promoter-binding assays show that GAME9 exerts its activity either directly or cooperatively with the SlMYC2 transcription factor as in the case of the C5-SD gene promoter. Our findings provide insight into the regulation of SGA biosynthesis and means for manipulating these metabolites in crops

Organizational capital and firm performance. Empirical evidence for European firms

The paper assesses the impact of Organizational Capital (OC) on firm perfor- mance for a sample of European firms. OC is proxied by capitalizing an income statement item (SGA expenses). A rationale for this methodology is provided. Results are robust and show the strong effect of OC on firm performance.Intangibles, Knowledge-based resources, Organizational capital,R&D capital stock, Translog production function

Study on Genetic Algorithm Improvement and Application

Genetic Algorithms (GAs) are powerful tools to solve large scale design optimization problems. The research interests in GAs lie in both its theory and application. On one hand, various modifications have been made on early GAs to allow them to solve problems faster, more accurately and more reliably. On the other hand, GA is used to solve complicated design optimization problems in different applications. The study in this thesis is both theoretical and applied in nature. On the theoretical side, an improved GA�Evolution Direction Guided GA (EDG-GA) is proposed based on the analysis of Schema Theory and Building Block Hypothesis. In addition, a method is developed to study the structure of GA solution space by characterizing interactions between genes. This method is further used to determine crossover points for selective crossover. On the application side, GA is applied to generate optimal tolerance assignment plans for a series of manufacturing processes. It is shown that the optimal tolerance assignment plan achieved by GA is better than that achieved by other optimization methods such as sensitivity analysis, given comparable computation time

Modeling And Applying Biomimetic Metaheuristics To Product Life Cycle Engineering

Due to its potential for significant impact, interest continues to grow in the assessment of products from a life cycle perspective. As the nature of products shifts from mechanized and Newtonian to more adaptive and complex, the behavior of products more closely resembles biological organisms in community. The change in product nature is increasingly mirrored at the component level. The work presented in this dissertation is twofold. First, the research proposes a general, systematic and holistic classification of life cycle data to transform the design problem into an optimization problem. Second, the research proposes two new metaheuristics (bio-inspired and socio-inspired) to solve optimization problems to produce grouped solutions that are efficient, evolvable and sustainable. The bio-inspired approach is schooling genetic algorithms (SGA), while the socio-inspired approach is referred to as genetic social networks (GSN). SGA is an approach that combines fish schooling concepts with genetic algorithms (GAs) to enable a dynamic search process. The application of GA operators is subject to the perception of the immediate local environment by clusters of candidate solutions behaving as schools of fish. GSN is an approach that adds social network concepts to GAs, implementing single and dyadic social interactions of social groups (clusters of similar candidate solutions) with GA operators. SGA and GSN both use phenotypic representations of a hypothetical product or system as input. The representations are derived from the proposed life cycle engineering (LCE) data classification. The outputs of either method are the representations that are more than likely to perform better, longer, and more autonomously within their environment during their life cycle. Both methods can also be used as a decision making tool. Both approaches were tested on product design problems with differing parametric relations, underlying solution space, and problem size

Understanding urban sub-centers with heterogeneity in agglomeration economies-Where do emerging commercial establishments locate?

This paper investigates the formation of employment sub-centers from a new perspective of heterogeneity in agglomeration economies. Using highly granular commercial and residential land-use data (2001–2011) in Chicago, we measure how the locations of jobs, population, quality-of-life amenities, and transportation networks shape specific and heterogenous sub-centers. First, the results suggest that the CBD as it was traditionally defined is no longer the primary source of agglomeration externalities for the new economic sectors; sub-centers with sector-specific positive agglomeration externalities have stronger correlations with new commercial establishments. Secondly, residents appear to give the highest weight to quality-of-life amenities in choosing where to live. Both trends imply dis-incentives for CBD agglomeration. These findings connect the heterogeneous production theories with land use planning and urban design, through new empirical insights into how urban sub-centers grow. Furthermore, we put forward a method for forecasting of future sub-center growth through measuring changes in the probability of commercial development, and discuss its practical implications for planning and design in Chicago

Optimization Algorithms for Computational Systems Biology

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology

Methylobacterium Genome Sequences: A Reference Blueprint to Investigate Microbial Metabolism of C1 Compounds from Natural and Industrial Sources

Methylotrophy describes the ability of organisms to grow on reduced organic compounds without carbon-carbon bonds. The genomes of two pink-pigmented facultative methylotrophic bacteria of the Alpha-proteobacterial genus Methylobacterium, the reference species Methylobacterium extorquens strain AM1 and the dichloromethane-degrading strain DM4, were compared. Methodology/Principal Findings The 6.88 Mb genome of strain AM1 comprises a 5.51 Mb chromosome, a 1.26 Mb megaplasmid and three plasmids, while the 6.12 Mb genome of strain DM4 features a 5.94 Mb chromosome and two plasmids. The chromosomes are highly syntenic and share a large majority of genes, while plasmids are mostly strain-specific, with the exception of a 130 kb region of the strain AM1 megaplasmid which is syntenic to a chromosomal region of strain DM4. Both genomes contain large sets of insertion elements, many of them strain-specific, suggesting an important potential for genomic plasticity. Most of the genomic determinants associated with methylotrophy are nearly identical, with two exceptions that illustrate the metabolic and genomic versatility of Methylobacterium. A 126 kb dichloromethane utilization (dcm) gene cluster is essential for the ability of strain DM4 to use DCM as the sole carbon and energy source for growth and is unique to strain DM4. The methylamine utilization (mau) gene cluster is only found in strain AM1, indicating that strain DM4 employs an alternative system for growth with methylamine. The dcm and mau clusters represent two of the chromosomal genomic islands (AM1: 28; DM4: 17) that were defined. The mau cluster is flanked by mobile elements, but the dcm cluster disrupts a gene annotated as chelatase and for which we propose the name “island integration determinant” (iid).Conclusion/Significance These two genome sequences provide a platform for intra- and interspecies genomic comparisons in the genus Methylobacterium, and for investigations of the adaptive mechanisms which allow bacterial lineages to acquire methylotrophic lifestyles.Organismic and Evolutionary Biolog

Shadow Price Guided Genetic Algorithms

The Genetic Algorithm (GA) is a popular global search algorithm. Although it has been used successfully in many fields, there are still performance challenges that prevent GA’s further success. The performance challenges include: difficult to reach optimal solutions for complex problems and take a very long time to solve difficult problems. This dissertation is to research new ways to improve GA’s performance on solution quality and convergence speed. The main focus is to present the concept of shadow price and propose a two-measurement GA. The new algorithm uses the fitness value to measure solutions and shadow price to evaluate components. New shadow price Guided operators are used to achieve good measurable evolutions. Simulation results have shown that the new shadow price Guided genetic algorithm (SGA) is effective in terms of performance and efficient in terms of speed