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Chapter 3 : A Top-Down Systems Biology Approach for the Identification of Targets for Fungal Strain and Process Development
Category: Microbial Genetics and Molecular Biology; Fungi and Fungal Pathogenesis
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This chapter discusses approaches to select targets for improvement of production processes, with a special focus on the application of functional genomics technologies as an unbiased approach towards target selection. The development of a fungal production process starts with the selection of a strain that produces the compound of interest or with the construction of such a strain. Often only the obvious targets for metabolic engineering are addressed. In this chapter such a systems biology approach, based on the information gathered with functional genomics technologies and in combination with multivariate data analysis tools, is discussed as a method to achieve unbiased selection and ranking of targets for both strain improvement and bioprocess optimization. Besides classical approaches for strain development, such as screening for protease mutants and targeted disruption of known protease genes, a top-down systems biology approach was applied to further elucidate the proteolytic system and its regulation in A. niger. The ultimate goal is to identify new targets for further improvement of the fungal cell factory for heterologous protein production. The available selection methods for relevant targets for fungal strain and process development, or for that matter any microbial production process, have been very successful in numerous cases. Recently introduced functional genomics technologies in combination with multivariate data analysis (MVDA) tools enable an open and comprehensive top-down systems biology approach towards target selection. Due to its unbiased nature, a successful top-down systems biology approach will provide a new boost in the ongoing cycle of bioprocess optimization.
Iterative cycle of strain improvement and/or process optimization.
Key conditions and their relation to a successful systems biology study. In top-down systems biology, three interlinked factors are crucial for success: (i) the biological question, (ii) the experimental design, and (iii) data analysis.
Full factorial designs, with two factors (A) or three factors (B) investigated at two different levels.
The effect of mean scaling, autoscaling, or range scaling of metabolomics data sets on PCA data results. The data sets are derived from research related to induction of cellulase activity in T. reesei (van der Werf et al., unpublished data). The metabolomes of three groups of samples (no enzyme production, increasing productivity, and decreasing productivity) were analyzed and pretreated with these three different approaches and subsequently analyzed by PCA.
Full factorial design of the experiments for the top-down systems biology approach to study the regulation of the proteolytic system of A. niger. Four factors were varied at two different levels.
Representation of different protease phenotypes during a glucose-based batch culture. (A) Protease activity (■) and specific protease activity (□). (B) Protease productivity (●) and specific protease productivity (○). Time points at which the biomass (☓) and each different phenotype reached its maximum value are indicated by upward arrows.