Industrial Microbiology INDM 4005 Lecture 9 23/02/04. PROCESS ANALYSIS. Lecture 9 ( 1) Kinetics and models - Predictive microbiology ( 2) Growth kinetics (and product) ( 3) Models - example, Continuous culture model . Overview. Fermentation Kinetics Mathematic models Stoichiometry
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(1) Kinetics and models - Predictive microbiology
(2) Growth kinetics (and product)
(3) Models - example, Continuous culture model
Kinetics and/orModels describe the process or data
(1) Assembly reactions
(2) Polymerisation reactions
(3) Biosynthetic reactions
(4) Fuelling reactions
Macromolecule % of total dry Different kinds
weight of molecules
Protein 55 1050
rRNA 16.7 3
tRNA 3 60
mRNA 0.8 400
DNA 3.1 1
Lipid 9.1 4
Lipopolysaccaride 3.4 1
Peptidoglycan 2.5 1
Glycogen 2.5 1
Metabolic pool 3.9
Data taken from Ingraham et al., (1983)
Bioreactor Performance e.g.
Microbial Kinetics e.g.
To construct a conventional mathematical model we write a set of equations for each control region
2) rates of generation or consumption,
substrate or product across boundaries of the region
Indicates well mixed
Scientific Experimental Engineering
judgement data judgement
Kinetic and Abstracted
Use model for control
process and economic studies
S n P
d[S] = k[S]
where [S] is the concentration of the reactant and k is a rate constant.
where [P] is the concentration of the product and n is the stoichiometric yield constant describing the relationship between the removal of S and formation of P.
where [S] is the substrate concentration, V is the rate of substrate removal, Vmax is the maximum specific rate and Km is the saturation constant.
The Monod Model looks similar to the Michaelis Menten equation.
where µm is the maximum specific growth rate and Ks is a saturation constant.
Model divides cell mass into components (by molecule or by element) and predicts how these components change as a result of growth. These models are very complex and not used very often.
Models presume balanced growth where cell components do not change with time. Much less complex and much more commonly used. Valid for batch growth during exponential growth phase and also for continuous culture during steady state growth.
Pertaining to a process, model, simulation or variable whose outcome, result, or value does not depend upon chance
Applied to processes that have random characteristics
1)STOCHASTIC - considers individual cells (example - the distribution of plasmids within the individual cells in a culture)
2) DETERMINISTIC - considers cell mass, can be;
= CONCEPTUAL MODEL
= EMPIRICAL MODEL
= MECHANISTIC MODEL (example model of penicillin ferm. )