Functional Reasoning and Functional Modelling

A car that will not start on a cold winter day and one that will not start on a hot summer day usually indicate two very different situations. When pressed to explain the difference, we would give a winter account- Oil is more viscous in cold conditions, and that causes . . .\u27\u27 -and a summer story- Vapor lock is a possibility in hot weather and is usually caused by . . .\u27\u27 How do we build such explanations? One possibility is that understanding how the car works as a device gives us a basis for generating the explanations. But that raises another question: how do people understand devices? Model-based reasoning is a subfield of artificial intelligence focusing on device understanding issues. In any model-based-reasoning approach, the goal is to model\u27\u27 a device in the world as a computer program. Unfortunately, model\u27\u27 is a loaded term-different listeners understand the word to mean very different concepts. By extrapolation, model-based reasoning\u27\u27 can suggest several different approaches, depending on the embedded meaning of model.\u27\u2

A domain-specific design architecture for composite material design and aircraft part redesign

Advanced composites have been targeted as a 'leapfrog' technology that would provide a unique global competitive position for U.S. industry. Composites are unique in the requirements for an integrated approach to designing, manufacturing, and marketing of products developed utilizing the new materials of construction. Numerous studies extending across the entire economic spectrum of the United States from aerospace to military to durable goods have identified composites as a 'key' technology. In general there have been two approaches to composite construction: build models of a given composite materials, then determine characteristics of the material via numerical simulation and empirical testing; and experience-directed construction of fabrication plans for building composites with given properties. The first route sets a goal to capture basic understanding of a device (the composite) by use of a rigorous mathematical model; the second attempts to capture the expertise about the process of fabricating a composite (to date) at a surface level typically expressed in a rule based system. From an AI perspective, these two research lines are attacking distinctly different problems, and both tracks have current limitations. The mathematical modeling approach has yielded a wealth of data but a large number of simplifying assumptions are needed to make numerical simulation tractable. Likewise, although surface level expertise about how to build a particular composite may yield important results, recent trends in the KBS area are towards augmenting surface level problem solving with deeper level knowledge. Many of the relative advantages of composites, e.g., the strength:weight ratio, is most prominent when the entire component is designed as a unitary piece. The bottleneck in undertaking such unitary design lies in the difficulty of the re-design task. Designing the fabrication protocols for a complex-shaped, thick section composite are currently very difficult. It is in fact this difficulty that our research will address

Functional Representation and Reasoning About the F/A-18 Aircraft Fuel System

Functional reasoning, a subfield of model-based reasoning, is discussed. This approach uses abstractions of a device\u27s purpose to index behaviors that achieve that purpose. Functional modeling, a variation on this method, also uses simulation as a core reasoning strategy. The complex causal knowledge of a device along functional lines is decomposed, then a causal story of how the device will operate in a particular situation given stated boundary conditions is composed. The application of the functional approach to modeling the fuel system of a F/A-18 aircraft is described. The representation of the F/A-18 fuel system includes 89 component devices, 92 functions, 118 behaviors, and 181 state variables

Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized.Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy.Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure

Functional characterization of the PHT1 family transporters of foxtail millet with development of a novel Agrobacterium-mediated transformation procedure

Phosphate is an essential nutrient for plant growth and is acquired from the environment and distributed within the plant in part through the action of phosphate transporters of the PHT1 family. Foxtail millet (Setaria italica) is an orphan crop essential to the food security of many small farmers in Asia and Africa and is a model system for other millets. A novel Agrobacterium-mediated transformation and direct plant regeneration procedure was developed from shoot apex explants and used to downregulate expression of 3 members of the PHT1 phosphate transporter family SiPHT1;2 SiPHT1;3 and SiPHT1;4. Transformants were recovered with close to 10% efficiency. The downregulation of individual transporters was confirmed by RT-PCR. Downregulation of individual transporters significantly reduced the total and inorganic P contents in shoot and root tissues and increased the number of lateral roots and root hairs showing they have non-redundant roles. Downregulation of SiPHT1;2 had the strongest effect on total and inorganic P in shoot and root tissues. Complementation experiments in S. cerevisiae provide evidence for the ability of SiPHT1;1, 1;2, 1;3, 1;7 and 1;8 to function as high affinity Pi transporters. This work will aid development of improved millet varieties for global food security

Zea mays iRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species

Environmental risk assessments for transgenic crops producing output trait enzymes

The environmental risks from cultivating crops producing output trait enzymes can be rigorously assessed by testing conservative risk hypotheses of no harm to endpoints such as the abundance of wildlife, crop yield and the rate of degradation of crop residues in soil. These hypotheses can be tested with data from many sources, including evaluations of the agronomic performance and nutritional quality of the crop made during product development, and information from the scientific literature on the mode-of-action, taxonomic distribution and environmental fate of the enzyme. Few, if any, specific ecotoxicology or environmental fate studies are needed. The effective use of existing data means that regulatory decision-making, to which an environmental risk assessment provides essential information, is not unnecessarily complicated by evaluation of large amounts of new data that provide negligible improvement in the characterization of risk, and that may delay environmental benefits offered by transgenic crops containing output trait enzymes

Differential Detection of Genetic Loci Underlying Stem and Root Lignin Content in Populus

In this study, we established a comprehensive genetic map with a large number of progeny from a three-generation hybrid Populus intercross, and phenotyped the lignin content, S/G ratio and 28 cell wall subcomponents both in stems and roots for the mapping individuals. Phenotypic analysis revealed that lignin content and syringyl-to-guaiacyl (S/G) ratio using pyrolysis molecular beam mass spectroscopy (pyMBMS) varied among mapping individuals. Phenotypic analysis revealed that stem lignin content is significantly higher than that in root and the quantified traits can be classified into four distinct groups, with strong correlations observed among components within organs. Altogether, 179 coordinating QTLs were detected, and they were co-localized into 49 genetic loci, 27 of which appear to be pleiotropic. Many of the detected genetic loci were detected differentially in stem and root. This is the first report of separate genetic loci controlling cell wall phenotypes above and below ground. These results suggest that it may be possible to modify lignin content and composition via breed and/or engineer as a means of simultaneously improving Populus for cellulosic ethanol production and carbon sequestration

An Insect Herbivore Microbiome with High Plant Biomass-Degrading Capacity

Herbivores can gain indirect access to recalcitrant carbon present in plant cell walls through symbiotic associations with lignocellulolytic microbes. A paradigmatic example is the leaf-cutter ant (Tribe: Attini), which uses fresh leaves to cultivate a fungus for food in specialized gardens. Using a combination of sugar composition analyses, metagenomics, and whole-genome sequencing, we reveal that the fungus garden microbiome of leaf-cutter ants is composed of a diverse community of bacteria with high plant biomass-degrading capacity. Comparison of this microbiome's predicted carbohydrate-degrading enzyme profile with other metagenomes shows closest similarity to the bovine rumen, indicating evolutionary convergence of plant biomass degrading potential between two important herbivorous animals. Genomic and physiological characterization of two dominant bacteria in the fungus garden microbiome provides evidence of their capacity to degrade cellulose. Given the recent interest in cellulosic biofuels, understanding how large-scale and rapid plant biomass degradation occurs in a highly evolved insect herbivore is of particular relevance for bioenergy

Expression of Trichoderma reesei β-Mannanase in Tobacco Chloroplasts and Its Utilization in Lignocellulosic Woody Biomass Hydrolysis

Lignocellulosic ethanol offers a promising alternative to conventional fossil fuels. One among the major limitations in the lignocellulosic biomass hydrolysis is unavailability of efficient and environmentally biomass degrading technologies. Plant-based production of these enzymes on large scale offers a cost-effective solution. Cellulases, hemicellulases including mannanases and other accessory enzymes are required for conversion of lignocellulosic biomass into fermentable sugars. β-mannanase catalyzes endo-hydrolysis of the mannan backbone, a major constituent of woody biomass. In this study, the man1 gene encoding β-mannanase was isolated from Trichoderma reesei and expressed via the chloroplast genome. PCR and Southern hybridization analysis confirmed site-specific transgene integration into the tobacco chloroplast genomes and homoplasmy. Transplastomic plants were fertile and set viable seeds. Germination of seeds in the selection medium showed inheritance of transgenes into the progeny without any Mendelian segregation. Expression of endo-β-mannanase for the first time in plants facilitated its characterization for use in enhanced lignocellulosic biomass hydrolysis. Gel diffusion assay for endo-β-mannanase showed the zone of clearance confirming functionality of chloroplast-derived mannanase. Endo-β-mannanase expression levels reached up to 25 units per gram of leaf (fresh weight). Chloroplast-derived mannanase had higher temperature stability (40°C to 70°C) and wider pH optima (pH 3.0 to 7.0) than E.coli enzyme extracts. Plant crude extracts showed 6–7 fold higher enzyme activity than E.coli extracts due to the formation of disulfide bonds in chloroplasts, thereby facilitating their direct utilization in enzyme cocktails without any purification. Chloroplast-derived mannanase when added to the enzyme cocktail containing a combination of different plant-derived enzymes yielded 20% more glucose equivalents from pinewood than the cocktail without mannanase. Our results demonstrate that chloroplast-derived mannanase is an important component of enzymatic cocktail for woody biomass hydrolysis and should provide a cost-effective solution for its diverse applications in the biofuel, paper, oil, pharmaceutical, coffee and detergent industries