Recent developments in the monitoring, modeling and control of biological production systems

Abstract

Current trends in the development of methods for monitoring, modeling and controlling biological production systems are reviewed from a bioengineering perspective. The ability to measure intracellular conditions in bioprocesses using genomics and other bioinformatics tools is addressed. Devices provided via micromachining techniques and new real-time optical technology are other novel methods that may facilitate biosystem engineering. Mathematical modeling of data obtained from bioinformatics or real-time monitoring methods are necessary in order to handle the dense flows of data that are generated. Furthermore, control methods must be able to cope with these data flows in efficient ways that can be implemented in plant-wide computer communication systems.

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

“…Different strategies for performing fully automated and observable processes based on soft sensor approaches have previously been investigated [4,33,34]. Mathematical modeling alone for prediction of growth and other metabolic activity of the cells is a resourceful strategy but has the drawback that the variability of the complex biological systems makes accurately predictions difficult.…”

Orientation and capturing of antibody affinity ligands: Applications to surface plasmon resonance biochips

“…However, this is compensated for by (1) the increased capturing efficiency of the oriented ligands, and (2) increased stability of the ligand, which favors its long-term use. The latter is of substantial benefit for applications in toxicity testing [45,46] and in bioprocess monitoring and control [47].…”

Bioprocess Modeling and Control

“…In actual AI systems, fuzzy rules are often applied together with different types of models / parameter / state estimators. These fuzzy rules can be regarded as problem specific basis function system [40]. Any variable can be a fuzzy variable, particularly recommended when it is not possible to define its value in a given situation.…”