Criteria for good medium • It will produce the maximum yield of product or biomass per gram of substrate used • It will produce the maximum concentration of biomass or product • It will permit the maximum rate of product formation • There will be minimum yield of undesired products • It will be of consistent quality and available throughout the year • It will cause minimal problems during medium sterilization • Other aspects of production process such as aeration, agitation, downstream processing, waste treatment
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Criteria for good medium• It will produce the maximum yield of product or biomass per gram of substrate used
• It will produce the maximum concentration of biomass or product
• It will permit the maximum rate of product formation
• There will be minimum yield of undesired products• It will be of consistent quality and available throughout the year
• It will cause minimal problems during medium sterilization
• Other aspects of production process such as aeration, agitation, downstream processing, waste treatment
Medium designed will affect the design of fermenter ex oxidation of hydrocarbons highly aerobic process –air lift reactor
Problems will be encountered in scaling up. Since large reactors will have low mass transfer rate
High viscous medium will consume more power.
Besides growth and product formation medium will influence the pH variation, foam formation, morphological form of organism etc.,
Use of complex nutrients will influence downstream processing
Variation in complex nutrients will result in batch to batch variations.
Medium cost has to be considered depending on the product type. Eg. For single cell protein production medium cost is more than 50 % of production cost. In the case of pencillin it is 30% and in recombinant products it is less than 10 %.
Medium formulationMedium formulation is essential stage in
manufacturing process
Carbon & Nitrogen other
Energy + sources + O2 + nutrients
Sources Biomass + products + CO2 +H2O +heat
Elemental composition of microorganisms may be taken as guide
Design of medium will influence the oxygen requirements
In olden days mineral content is important- High Ca for dark beers- High carbonate for stouts
Nowadays- Deionisation of water
Reuse of water is important- It reduces water cost by 50%- Effluent treatment cost by 10 fold
Carbon sources
Factors influencing the carbon source- Cost of the product- rate at which it is metabolized- geographical locations- government regulations- cellular yield coefficientMethane - 0.62Alkanes - 1.03Glucose - 0.51Acetate - 0.34
Examples of carbon sourcesExamples of carbon sourcesCarbohydrates
Starch – max 2%
Molasses (Beet – sucrose 48.5% Raffinose 1.0% Invert sugar 1.0% same in cane molasses 33.4%, 0%, 21.2%)
Sucrose
Glucose
Malt (Barley grains germinated and heat treated)
Other materials of plant origin like soy bean meal, pharmedia
Oils and fatsOils are first used as antifoams and later used as carbon sources (soya oil, olive oil, maize oil, linseed oil etc.,)
Factors favouring oil2.4 times energy than glucoseHence volume advantage of 4 times.some organisms can use only oils for efficient production Eg. antibiotics (Methyl oleate is used in cephalosporin)
Hydrocarbons and their derivatives
Now it is expensivetwo times carbon and three times energy than that of carbohydrates
Ammonium salts produces acid conditions when ammonia is utilised. pH drift
Sodium nitrate produces alkaline drift
Organic
Organic nitrogen may be supplied by amino acids, protein, urea
Growth will be faster. These are commonly added as complex nitrogen sources such as soy bean meal, corn steep liquor etc., (During storage these sources are affected by moisture, temperature and ageing)
Factors influencing choice of nitrogen Factors influencing choice of nitrogen sourcesource
- Nitrate reductase enzyme is repressed by ammonium ion. Hence ammonia or ammonium salts are preferred
- Ammonium ions represses amino acid uptake in fungal cultivations
- also ammonia regulates acid and alkaline protease production
- antibiotic production by many fungi is influenced by the nitrogen source.
- soy bean meal is preferred in polyene antibiotics production due to slow hydrolysis which prevents ammonia accumulation and in turn aminoacid repression by it
- in gibberellin production, nitrogen source influence production of gibberellins
- some complex nitrogen sources may not be utilised by some microorganisms which may cause problem in downstream processing
MineralsMineralsAll microorganisms require minerals for growth and product formation
Magnesium, phosphorus, potassium, sulphur, calcium, chlorine are essential components
Cobalt, copper, manganese, iron, molybdenum, zinc are also essential but in traces.
Also depending on product analysis apart from biomass minerals will be decided. E.g sulphur in pencillins, cephalosporins, chlorine in chlortetracyclin etc.,
Concentration of phosphate in medium is normally required in excess for buffering the medium. Phosphate concentration in the medium are critical in antibiotic production since some enzymes of biosynthesis are influenced by phosphate
Other metal ions influence the production of secondary metabolites
The functions of each vary from serving in coenzyme functions to catalyze many reactions, vitamin synthesis, and cell wall transport.
Citric acid & Penicillin production – Fe, Zn, Cu
Protease production – Mn
ChelatorsChelatorsMany media cannot be prepared without precipitation during autoclaving. Hence some chelating agents are added to form complexes with metal ions which are gradually utilised by microorganism
Examples of chelators: EDTA, citric acid, polyphosphates etc.,
It is important to check the concentration of chelators otherwise it may inhibit the growth.
In many media these are added separately after autoclaving Or yeast extract, peptone complex with these metal ions
Mandel and Weber, 1969 (g l-1)Urea = 0.3 g(NH4)2 SO4 = 1.4 g
K2HPO4 = 2 g
MnSO4. 7H2O = 1.6 mg
CoCl2.6H2O = 2 mg
CaCl2. 2H2O = 0.4 g
Mg SO4.7H2O = 0.3 g
FeSO4. 7H2O = 5 mg
ZnSO4. 7H2O = 1.4 mg
Peptone = 1 g Yeast extract = 0.25 g Maize / steep liquor= 10 gCellulose = 2 g
Growth FactorsGrowth Factors• Some microorganisms cannot synthesize a full complement of cell components and therefore require preformed compounds called growth factors
• Eg.: vitamins, aminoacids, fatty acids or sterols
• Complex media sources contain most of these compounds. Careful blending of these will give the required growth factors.
• For vinegar production – Calcium Pantothenate
• For Glutamic acid – Biotin
PrecursorsPrecursors
• Some chemicals when added to certain fermentations are directly incorporated into the desired product.
• Eg: Improving the yields of Pencillin production
InhibitorsInhibitors• When certain inhibitors are added to fermentation more of a specific product may be produced
• Eg : Glycerol fermentation• Glycerol production depends on modifying ethanol fermentation by removing acetaldehyde
• Addition of sodium bisulphite forms acetaldehyde bi sulphite. Acetaldehyde is no longer available and dihydroxy acetone is formed.
InducersInducers• Majority of the enzymes are inducible
• Substrates or substrates analogues are used as inducers.
• Enzymes are produced in response to the presence of these compounds in the environment.
• Heterologous protein production in E.coli, yeast etc.,
AntifoamsAntifoams• Most fermentations foaming is major problem.
• It may be due to component in the medium or some factor produced by the microorganism.
• Foaming can be controlled by• Modification of medium• Mechanical foam breakers• Chemical agents antifoams are added Eg: Fatty acids, silicones, PPG 2000
• Antifoams are surface active agents reducing the surface tension in the foam and destabilising the protein films
• An ideal antifoam should have the following properties• Disperse readily and have fast action• Active at low concentrations• Long acting in preventing new foam• Should not be metabolized• Should not be toxic to m.o, humans etc• Cheap, should not cause problem in fermentation
Medium OptimizationMedium Optimization
When considering the biomass growth
phase in isolation, it must be
recognized that efficiently grown
biomass produced by an ‘optimized’ high
productivity growth phase is not
necessarily best suited for its
ultimate purpose, such as synthesizing
the desired product.
Classical designClassical designChanging one variable at timeChanging one variable at timeTotal no of experiments will be xTotal no of experiments will be xnn
x – no of levelx – no of level n - no of variables or factorsn - no of variables or factors For ex 3 levels and 6 variables For ex 3 levels and 6 variables have to be tested then the number have to be tested then the number of experiments will be 3of experiments will be 366=729=729
Optimization through modellingOptimization through modelling
Design of Experiments (DOE)oHelp you improve your processes. You
can screen the factors to determine
which are important for explaining
process variation.
oAfter you screen the factors, Minitab
/ Design expert software helps you
understand how those factors interact
and drive your process.
Plackett Burman designPlackett Burman designMore than five variables it is usefulMore than five variables it is usefulIt will be useful in screening the It will be useful in screening the most important variablemost important variable
Here n no of experiments will be Here n no of experiments will be conducted for n-1 variablesconducted for n-1 variables
Where n is the multiples of 4 like Where n is the multiples of 4 like 8,12,16,20…1008,12,16,20…100
Authors give a series of experimental Authors give a series of experimental design known as balanced incomplete design known as balanced incomplete blocksblocks
Variables which is not having Variables which is not having influence in the process is influence in the process is designated as dummy variablesdesignated as dummy variables
Dummy variables are required to Dummy variables are required to estimate the error in the estimate the error in the experimentationexperimentation
Minimum one or two dummy Minimum one or two dummy variables should be included in variables should be included in the experimental setthe experimental set
More can be included if the real More can be included if the real variables are lessvariables are less
When to use PBWhen to use PBScreening multi components at 2 levelsScreening multi components at 2 levels It will give the range at which you have It will give the range at which you have
to optimize the experiments furtherto optimize the experiments further
Limitations:Limitations:
It will not give optimum concentration of It will not give optimum concentration of the variablethe variable
Response surface methodology is a Response surface methodology is a method of optimization using statistical method of optimization using statistical techniques based upon the special techniques based upon the special factorial design of Box and Behnken etc.,factorial design of Box and Behnken etc.,
It is a scientific approach to determine the It is a scientific approach to determine the optimum conditions which combines the optimum conditions which combines the special experimental designs and Taylor special experimental designs and Taylor first order and second order equationfirst order and second order equation
Sequential nature of RSMSequential nature of RSM
How to ProceedHow to Proceed Select critical factors and regions to be Select critical factors and regions to be testedtested
Design the experiment based on box Design the experiment based on box behnken or central composite designbehnken or central composite design
Do the experimentDo the experiment Fit the data to Taylor series, determine Fit the data to Taylor series, determine coefficients to build modelcoefficients to build model
Validate model by selecting values in the Validate model by selecting values in the region testedregion tested
Draw the contour plot and find optimum Draw the contour plot and find optimum concentrationconcentration
Design of experimentsDesign of experiments
Variable 1
Vari
able
2
Low High
High
Low
Coding the variablesCoding the variablesValue of the variable - Middle pointValue of the variable - Middle point
• Using the actual values makes it easy to calculate the response from the coefficients since it is not necessary to go through coding process
• The reason for coding the variables is to eliminate the effect that the magnitude of the variable has on the regression coefficient
• Prob>F is less than 0.05 indicated significant model terms
• The standard error of estimate yields information concerning the reliability of the values predicted by the regression equation. The greater the standard error of estimate, the less reliable the predicted value.
• Coefficient of variation less than 10 % indicate high degree of precision and reliability of experimental values
• The mathematical model is reliable with R2 value. Closer the value to 1 is the more reliable the model.
• R2 value 0.9529 suggests that the model was unable to explain 4.71% variations occurred
• R2 Value can be increased by including model terms. Sometimes even higher value may result in poor predictions.
• Adj R2 value will be verified. If this value differs dramatically then insignificant model terms have been included in the model
Contour plotContour plot• A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format.
• That is, given a value for z, lines are drawn for connecting the (x,y) coordinates where that z value occurs.
Stationary ridge
RISING RIDGERISING RIDGE
Y = β0+β1* X1+β2* X2+β11* X12+β22* X22+β12* X1*X2
Y = β0 + X’ b + X’ B X
X= X1 b = β1 B = β11 β12/2X2 β2 β12/2 β22
∂y/∂x =0
Xs = -1/2 B-1b
Application of response surface Application of response surface methodology to cell methodology to cell
immobilizationimmobilizationfor the production of palatinosefor the production of palatinose
Design based on Alpha factor = 1
• Optimum alginate concentration, cell loading and bead diameter were 5%, 15 g /l and 2.25 mm, respectively.
• R2 value of 0.9259
• A very low value of coefficient of the variation (C.V.) (4.46%)
