In recent years, several industrial yeasts, owing to their robust growth and certain other characteristics, have been developed as recombinant host systems for commercial production of heterologous proteins. One such yeast Pichia pastoris has proven to be an excellent host for production of secreted and intracellular proteins (Cereghino and Cregg. 2000). The increasing popularity of this particular expression system can be attributed to several factors, most importantly: (1) the simplicity of techniques needed for the molecular genetic manipulation of Pichia pastoris and their similarity to those of Saccharomyces cerevisiae, one of the most well-characterized experimental systems in modern biology;(2) the ability of Pichia pastoris to produce foreign proteins at high levels, either intracellularly or extracellularly; and (3) the capability of performing many eukaryotic post-translational modifications, such as glycosylation, disulfide bond formation and proteolytic processing. The expression level for a given recombinant protein produced by Pichia pastoris seems to be determined largely by its inherent properties such as amino acid sequence, the tertiary structure and the site for expression (Sreekrishna et al.,1997). The attempts on increasing the protein expression levels by far are focused on genetic manipulations to enhance the gene expression and protein stability. Although this is crucial, there is ample scope to improve the productivity of Pichia pastoris fermentations by undertaking a systematic program of optimizing the entire fermentation process. This work aims at undertaking such a program by focusing on strategy to identify and to characterize trends in the behavior of the system. It can be expected that by addressing the process as a whole, rather than narrowly focusing on the protein expression alone, the methodology proposed here can simplify process scale-up and can be applied to several products made by the same host. Pichia pastoris is methylotrophic yeast. In the Pichia pastoris fermentation, the limiting carbon source is glycerol, method or mixture of both. It can grow on methanol as a sole carbon and energy source. It possesses a highly inducible methanol utilization pathway. The first step in the metabolism of methanol is the oxidation of methanol to formaldehyde using molecular oxygen by alcohol oxidase (AOX). AOX, the first enzyme of the pathway, accounts for up to 35% of the total protein in cells grown on limited amounts of methanol. The enzymes undetectable in cells grown on glucose, ethanol or glycerol. There are two genes in Pichia pastoris that code for AOX: AOXI. The AOXI gene product accounts for the majority of alcohol oxidase activity in the cell. This highly inducible and stringently regulated AOXI promoter has been used to construct expression vectors for the production of heterologous proteins in Pichia pastoris. Although some foreign proteins have expressed well in shake-flask cultures, expression levels are typically low compared to fomenter cultures. There are several key aspects of Pichia pastoris fermentations: 1. Fed-batch operation – Controlled addition of glycerol, methanol or mixture thereof. In general, strains are grown initially in a defined medium containing glycerol as its carbon source (growth phase). During this phase, biomass accumulates but heterogonous gene expression is fully repressed. Upon depletion of glycerol, a transition phase is initiated in which additional glycerol is fed to the culture at a growth-limiting rate. Finally, method a mixture of glycerol and methanol is fed to the culture to induce expression (induction phase). The duration of individual substrate feeds, the amount and mode of feeding are critical to optimal fermentation performance. 2.Online measurement and control-One of the most important key parameters in Pichia pastor is expression system is the methanol concentration. Monitoring and controlling this variable are important because high levels of this inductor substrate can be toxic to the cells and low levels of methanol may not be enough to initiate the AOX transcription (Cereghino and Cregg, 2000) This research work aims at investigation the above mentioned aspects by conduction an in depth engineering analysis of the Pichia Pastoris fermentations.

This thesis concerns Precision Agriculture (PA) technology which involves methods developed to optimise grain yield by examining data quality and modelling protein/yield relationship of wheat and sorghum fields in central and southern Queensland. An important part of developing strategies to optimisise grain yield is the understanding of PA technology. This covers major aspects of PA which includes all the components of Site- Specific Crop Management System (SSCM). These components are 1. Spatial referencing, 2. Crop, soil and climate monitoring, 3. Attribute mapping, 4. Decision suppport systems and 5. Differential action. Understanding how all five components fit into PA significantly aids the development of data analysis methods. The development of PA is dependent on the collection, analysis and interpretation of information. A preliminary data analysis step is described which covers both non-spatial and spatial data analysis methods. The non-spatial analysis involves plotting methods (maps, histograms), standard distribution and statistical summary (mean, standard deviation). The spatial analysis covers both undirected and directional variogram analyses. In addition to the data analysis, a theoretical investigation into GPS error is given. GPS plays a major role in the development of PA. A number of sources of errors affect the GPS and therefore effect the positioning measurements. Therefore, an understanding of the distribution of the errors and how they are related to each other over time is needed to complement the understanding of the nature of the data. Understanding the error distribution and the data give useful insights for model assumptions in regard to position measurement errors. A review of filtering methods is given and new methods are developed, namely, strip analysis and a double harvesting algoritm. These methods are designed specifically for controlled traffic and normal traffic respectively but can be applied to all kinds of yield monitoring data. The data resulting from the strip analysis and double harvesting algorithm are used in investigating the relationship between on-the-go yield and protein. The strategy is to use protein and yield in determining decisions with respect to nitrogen managements. The agronomic assumption is that protein and yield have a significant relationship based on plot trials. We investigate whether there is any significant relationship between protein and yield at the local level to warrent this kind of assumption. Understanding PA technology and being aware of the sources of errors that exist in data collection and data analysis are all very important in the steps of developing management decision strategies.