27-02-2013, 10:50 AM
IMPACT OF YEAST SYSTEMS BIOLOGY ON INDUSTRIAL BIOTECHNOLOGY
IMPACT OF YEAST SYSTEMS.ppt (Size: 1.24 MB / Downloads: 32)
Introduction
What is systems biology?
Systems biology is the study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life.
Instead of analyzing individual components or aspects of the organism, such as sugar metabolism or a cell nucleus, systems biologists focus on all the components and the interactions among them, all as part of one system.
Aims at understanding all of the genotype-phenotype relationships at the levels of genes, transcripts (RNA’s), peptides, proteins, metabolites, and environmental factors participating in complex cellular networks.
In any systemic study, the typical approach is to first define the system, quantify its components and then elucidate how the components interact to impart the system its properties.
We frequently refer to examples from the unicellular eukaryote, Saccharomyces cerevisiae, to highlight the progress made in systems biology as well as to relate the impact of this field on industrial biotechnology.
Proteome
The word "proteome" is a blend of "protein" and "genome“
Coined by Marc Wilkins in 1994
Describes the set of all the proteins expressed by a genome, cell, tissue or organism.
It is possible to quantify over 2000 proteins (even those occurring in low abundance) in S. cerevisiae proteome.
Using the MUDPIT (Multi Dimensional Protein Identification Technology) method, more recently over 2400 proteins were quantified in yeast.
Yeasts with different ploidy status differ in the levels of the proteins.
Metabolome
Is the collection of all metabolites in a biological organism, which are the end products of its gene expression.
A very low tolerance on the allowable variation in the metabolite concentrations.
Certain metabolites such as ATP or NADH are capable of bringing about significant changes in large parts of the metabolism.
Similar to the transcriptome and proteome, the metabolome also presents a snapshot of the physiological state of the cell.
Metabolite profiling is an important part of systems biology.
Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are the most frequently used methods of detection.
Top-down approach
Top-down approach is data-driven or inductive.
It establishes our knowledge of the system and interconnectivity between different functional modules.
The systemic properties emerge from the collaborative behavior of its components.
The primary goal : to discover new interactions between the subsystems.
The top-down approach uses global approaches with transcriptome, proteome and metabolome data.
Therefore is largely phenomenological and studies the dynamics of the entire network.
Examples : The approach to model the metabolism using genome-scale models, quantifying regulation using transcriptional regulatory networks, approaches to understand the structure and function of proteins, etc.
Systems biology strategy for metabolic engineering
Metabolic engineering is the rational development of cell factories using directed genetic engineering for industrial applications.
For any given product, the most important decision is the choice of the organism.
The characteristics of an ideal organism : high product titers, minimal by-product formation, easy cultivation techniques using cheap raw materials, amenability to genetic modifications and safe to work with.
But sometimes these needs are not fulfilled.
Consequently, it is common to introduce the pathways for these products in well-established model systems such as S. cerevisiae by cloning the appropriate genes.
S. cerevisiae is used to produce recombinant proteins and bulk compounds such as ethanol.
Results and Conclusions
A central goal of systems biology is the elucidation of cell function and physiology through the integrated use of broad based genomic and physiological data.
The recent development of “omics” technology, combined with computational analysis, provides a new avenue for strain improvement and process development by contributing new information extracted from a large number of data, termed as “systems biotechnology”.