Chapter 1
Traditionally biochemical engineers had an important function in the design and scale up of
bioprocesses. Today they are heavily involved also in the very early design phase of a new process,
as it has become of utmost importance to apply an integrated process design wherein the
prospective production organism is made fit for large scale operation even at the early stages of
laboratory strain development.
Thus, biochemical engineers have been very active in the rapid
progress of metabolic engineering. Teams of engineers and biologists will be responsible for the
implementation of an integrated approach to process design. It is therefore important that main
stream biologists obtain some insight into quantitative analysis of cellular function and bioreactor
operation, and that biochemical engineers continue to learn more about fundamental biological
Besides their role in process design and in metabolic engineering, biochemical engineers must also
play an increasing part in fundamental biological research. The genome of a large number of
organisms has been completely sequenced, and it has become a major research goal both to assign
function to all genes in the genome, referred to as
Junctional genomics,
and to understand how all
the components within the cellular system interact. This can only be done through the use of
complex mathematical models, and this field is referred to as
systems biology
(see Fig. 1.1).
System reconstructed
Figure LI Schematic representation of systems biology.
Based on empirical data and knowledge of cellular function a mathematical model is proposed. The
model is used to simulate the overall cell function, and model simulations are compared with
experimental data. Experimental data may be obtained from: 1) Genomics; information about the
genomic sequence; 2) Transcriptomics; data on the expression of all genes obtained by measurement of
the complete mRNA pool using DNA arrays; 3) Proteomics; data on all available proteins in the cell
obtained by 2D-gel electrophoresis or protein chips; 4) Metabolomics; data on the metabolite profiles
inside the cells are obtained using different analytical techniques; and 5) Fluxomics; fluxes through all
the cellular reactions are quantified. If there is a good fit between experimental data and model
simulations the model is likely to be a good representation of the biological system, which can therefore
be reconstructed from its essential parts. A poor fit shows that the model needs to be revised, and often
the discrepancy between model simulations and the experimental data will point to where the model
needs to be revised [Adapted from Nielsen and Olsson (2002)].
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