Growth - Overview Microbial Growth –Overview of terms:

Growth - Overview Microbial Growth –Overview of terms: exponential growth u td productivity Substrate limitation of metabolism Link between metabolism and growth

Growth - Overview Microbial Growth I
Energy metabolism overview: glycolysis, TCA cycle, respiration chain, ATP synthase Growth medium components, energy, carbon, nitrogen, phosphorus sources , minerals, trace elements, buffer Growth rate, specific growth rate, exponential growth, semilog plot, maximum and total productivity, lag phase

Growth - Overview Microbial Growth II – Substrate limitation
Michaelis Menten model of substrate dependent substrate uptake rate vmax, km The yield coefficient connects the Michaelis Menten Model to the Monod model of substrate dependent growth rate, umax, ks Yield coefficient measurements Yield coefficient is not constant Maintenance coefficient Pirt model contains 4 growth constants, ms, Ymax

Growth - Overview Microbial Growth III – Maintenance coefficient Microbial Growth IV Growth in batch culture Microbial Growth V Chemostat Microbial Growth VI Determining growth constants Biomass retention

1 Growth Medium Ingredients
1.1 The rationale of media recipes Bacterial cells typically grow by cell division into two daughter cells. To do this they require a suitable growth medium. Growth media recipes in the literature vary widely and it can be confusing to students to discriminate between essential ingredients and replaceable ones. Rather than blindly following recipes, it would be more useful for microbiologists to be able to design own media, or to modify or optimise exiting media. For this it is useful to understand the generic microbial growth requirements. The ingredients of a typical growth medium satisfy a number of principal needs of growing cells by providing a source of Energy, carbon, nitrogen, phosphate, sulfur, minerals, trace elements, vitamins, growth factors, buffer capacity. 1.2 Energy source Microbial growth (assimilation) is an endergonic process and requires energy input for the conversion of ingredients from the growth medium into biomass. This energy is derived from the energy source component of the growth medium. Typically an energy source consists of a suitable electron donor and electron acceptor. It is the transfer of electrons from the electron donor (a redox couple that of a more negative potential than that of the electron accepting redox couple) to the acceptor that liberates energy which is conserved as ATP. The ATP is typically generated during this electron transfer via electron transport phosphorylation (electron carriers, electron transport chain, proton gradient, ATP synthase). For most bacteria the electron donor is an organic compound being oxidised to CO2 and the electron acceptor is oxygen, which is supplied by allowing air access to the growht container (e.g. petri dishes or shake flasks). However many bacteria use inorganic reduced chemical species as the electron donor, such as ferrous iron, sulfide, ammonia, or hydrogen gas which supply electrons by being oxidised to ferric iron, sulfate, nitrite or protons, respectively. Electron accepting species alternative to oxygen are can be ferric iron, nitrate, sulfate, CO2, etc. which accept electrons by being reduced to ferrous iron, N2, sulfide and methane respectively. When supplying specific electron acceptors it needs to be considered that the presence of multiple potential electron acceptors can cause mutual inhibition. For example the presence of oxygen inhibits the reduction of nitrate, sulfate and CO2 while the presence of nitrate inhibits the reduction of sulfate and CO2. The use of external electron acceptors other than oxygen is still a respiration (anaerobic respiration). If a suitable electron donor and electron acceptor is provided this enables the bacteria to generate ATP by harnessing the energy liberated from the flux of electrons from electron donor to electron acceptor.

1.3 Carbon source The carbon source for microbial growth is typically an organic compound and is often identical to the electron donor. For example an aerobic bacterium growing by oxidising sugar to CO2 for ATP generation will also use the same sugar as a starting material for biomass synthesis. The sugar may be partly degraded via glycolysis to pyruvate or even to Acetyl-CoA to use parts of the TCA cycle for biomass synthesis purposes (compare to pathway of glutamate formation later in this text). The ratio of carbon that is used as energy source (catabolism, dissimilation) or as carbon source for biomass synthesis (anabolism, assimilation) determines the bacterial yield coefficient. Aerobic bacteria degrading a sugar use about 40 to 50% of the carbon for assimilation and the rest for energy generation. Hence a yield coefficient of 0.4 to 0.5 (g of microbial cells formed per g or carbon source used) is commonly observed. In rare but interesting cases no formal carbon source needs to be provided to the growth medium. This is the case for autotrophic bacteria. Similar to plants and algae, autotrophic bacteria use CO2 as the carbon source which can be obtained from the air supply. However the additional supply of CO2 via a bicarbonate buffer (HCO3- + H+  H2CO3  CO2 + H2O) helps the growth of autotrophs by minimising CO2 limiting conditions. Examples of autotrophic bacteria with industrial and environmental significance include: nitrifying bacteria that use NH3 as electron acceptor and Fe2+ and sulfur oxidising bacteria. These bacteria of the genera Nitrosomonas and Thiobacillus are used for nitrogen removal from wastewater and bioleaching of ores respectively. 1.4 Nitrogen source Nitrogen is next to carbon, hydrogen and oxygen (the latter two are sourced from water) the quantitative most important element. Nitrogen is needed for the synthesis of enzymes and other proteins. In minimal media it is typically supplied as ammonium or nitrate salts, in rich media it is supplied as an organic nitrogen source (e.g. part of yeast extract or peptone based media). With nitrate as the nitrogen source the bacteria need to first reduce the nitrate to ammonia (assimilative nitrate reduction) followed by ammonia assimilation into biomass. Some bacteria are capable of using the relatively inert (triple bond) N2 from air as the nitrogen source. Hence selective media for such nitrogen fixing bacteria do not include any nitrogen source.

1.3 Phosphate source The next most important element for microbial growth is phosphorous. In contrast to the other elements in biomass, phosphorus does not undergo oxidation or reduction and stays in its phosphate status (hence phosphate source). Phosphate needs to be present in all microbial growth media, without it growth cannot occur. Phospholipids and ATP are two examples of essential phosphate containing cell components. Rich media produced from biomass hydrolysates (e.g. yeast extract or peptone) contain organic phosphate compounds. Many media use manifold more phosphate than necessary for biosynthetic purposes (e.g. about 50 mM). Here phosphate serves as the buffer species for pH control. While phosphate buffers are typically recommended to be added in two components, the more acidic phosphate (KH2PO4) and the more basic phosphate (K2HPO4) to result in a precise pH of the final media, it can also be provided by other phosphate sources followed by adjustment of the pH to a precise setpoint by any suitable acid or base. This pH adjustment will result in producing the same ratio of hydrogen and dihydrogen phosphate as suggested by the original recipe. 1.4 Sulfur source Sulfur is needed for protein synthesis and hence essential to all growth media. For aerobic media sulfur is added as sulfate, which the bacteria reduce (assimilatory sulfate reduction) to sulfide prior to assimilation into amino acids. Alternative sulfur sources are sulfide (for anaeroboic media) or organic sulfur sources such as yeast extract or cysteine.

OUR- Growth medium for microbes
Components of Growth Medium: Energy source (electron donor and acceptor) C-source (e.g. sugar) N-source (e.g. NaNO3) P-source (e.g. KH2PO4) other minerals (e.g. Na+Mg+, SO42-, Trace elements (e.g. Co, Mn, Fe, etc) Vitamins (e.g. cyanocobalamin) Buffer (e.g. carbonate or phosphate buffer)

Cell Growth- Overview of Energy Metabolism =Dissimilation glucose
simplifying FAD and ATP genration in TCA glucose glucolysis TCA cycle 1 ATP  3 H+ ATP synthase 2 ATP + 12 NADH Cell Overall: 38 ATP allowing growth ETC each NADH  9 H+ O2

Growth- Simplified Scheme of Energy preservation as ATP
Important Quantities: ATP-synthase: 3H+  1 ATP ETC: 1 NADH  3*3 = 9 H+ 2 NADH reduce 1 O2 1 NADH = 2 electron equivalents 1 O2 accepts 4 electron equivalents glycolysis: 1 glucose  12 NADH 1 glucose  12*9=108 H+ = 36 ATP + 2 ATP from glycolysis via substrate level phosphorylation = 38 ATP

Growth- Simplified Scheme of Energy preservation as ATP
Minor corrections not needed for exams: During TCA cycle not only NADH is produced but also FAD. FAD translocates only 2 H+ rather than 3  hence 2 less ATP. However TCA also generates 2 ATP not mentioned in simplified balance.

Growth- Exponential Multiplication by binary fission: 0 min 30 min

Growth- Exponential (split… split… split ) The resulting seqeunce in numbers is exponential ( 2, 4, 8, 16, 32)

Growth- Exponential (split… split… split ) The resulting sequence in numbers is exponential ( 1,2, 4, 8, 16, 32). Not only the biomass (X) increases exponentially but also the rate at which it is produced (calculate from above) growth rate is NOT constant in batch culture (similar to OTR not being constant) needed: a constant that describes the speed of binary fission (similar to kLa in oxygen transfer) Plotting the growth rate as a function of time will reveal

Growth- Exponential (split… split… split ) Not only biomass (X) increases exponentially, but also the rate at which it is produced. Thus: dX/dt X , dX/dt = factor • X ˜ The proportionality factor is μ the specific growth rate dX/dt = μ * X (g/L.h) (h-1) (g/L) Xt = Xoeμt Integration gives Take ln of both sides μ indicates how much more biomass is produced per biomass present (g/h/g) = (h-1) ln(Xt) = ln(Xo) + μt μ = (In(Xt) – In(Xo)) t

not examinable: dX/dt = μ * X dx = u *n X * dt dx/X = u * dt ∫dx/x = ∫ udt ∫1/x * dx = ∫ udt lnx = ut +c x = e ut+c x = e ut *ec for t= 0: x=xo xo = e ut *ec Hence: x = e ut *xo

Growth- Estimation of u from single interval
μ = InXt – ln Xo t = In 8 – In 2 90 min = 2.77 – 0.693 90 min = 90 min 2.077 = min-1 = h-1 In 2 μ 0.69 1.38 h-1 Doubling time = = = 0.5 h

Growth- Estimation of doubling time from semilog plot
When plotting the log of cell mass versus time a straight line is obtained. The slope of the line reveals the doubling time. The specific growth rate can be calculated from the doubling time by: Advantage of plot: averaging out, avoiding outliers

Growth- Limitation and growth phases
Growth in batch culture can not continue forever Typical industrial growth curve incl.: preparation time (clean, sterilise, fill) lag phase log phase stationary phase decay phase X (g/L) Time (h)

Growth- Limitation and growth phases
lag log

Growth- Productivity in industrial batch cultures
Most important to industry:productivity of the process (g.L-1.h-1). Productivity is the overall product (here biomass X) concentration produced per time required. The process can be stopped for maximum productivity or maximum product concentration (total productivity) Choice depends on cost of operation and product maximum productivity X (g/L) total productivity Time (h)

Growth- Substrate Limitation
In most environmental and many industrial bioprocesses (e.g. chemostat), the growth rate is limited by substrate availability. Substrate uptake rate at different substrate concentrations is important (limitation and saturation of substrate) substrate saturation Substrate (g/L) substrate limitation Time (h)

Growth- Substrate Limitation
Substrate uptake rate at different substrate concentrations is important substrate saturation v (g/L/h) substrate limitation Substrate (g/L)

S v = vmax ------- kM S + kM Growth- Michaelis Menten model
What is the relationship between substrate concentration (S) and its uptake rate (v) ? vmax (h-1) v (h-1) v = vmax kM S + S substrate limitation Described by Michaelis-Menten kinetics (standard biochemistry knowledge) S (g/L) kM Growth- Michaelis Menten model

Michalis Menten Model:
Growth- Relationship between Michaelis Menten kinetics and and Monod kinetics Michalis Menten Model: predicts substrate uptake fromsubstrate concentration Monod Model: predicts specific growth rate from substrate concentration Under substrate limitation:  Substrate concentration   Substrate uptake rate (SUR)   ATP production rate   rate of producing new cells (u) Growth- Michaelis Menten model

S v = vmax ------- kM S + kS Growth- Michaelis Menten model
What is the relationship between substrate concentration (S) and its uptake rate (v) ? µmax (h-1) (h-1) v = vmax kM S + S substrate limitation Described by Michaelis-Menten kinetics (standard biochemistry knowledge) kS S (g/L) Growth- Michaelis Menten model

Growth- Michaelis Menten model
Growth- Relationship between Michaelis Menten kinetics and and Monod kinetics What is the relationship between specific growth rate (µ) and specific substrate uptake rate (v) Relationship is given by the yield coefficient Y (g of X formed per g of S degraded). v= substrate uptake rate (SUR) but can also be OUR Note: unlike µmax and kS, Y is not a true growth constant. kS and kM are equivalent v = vmax kM S + S µ = Y * v S µ = Y * vmax S + kS Growth- Michaelis Menten model

Substrate limitation of microbial growth
(h-1) Substrate (g/L) substrate limitation kS µmax The two curves are described by two properties: The maximum specific growth rate obtained with no substrate limitation (umax (h-1)) and the half saturation constant (Michaelis Menten constat), giving the substrate concentratation at which half of the maximum u is reached (ks (g/L)). Growth- Michaelis Menten model

µ = µmax * kS S + S (h-1) Typically there are low and high substrate specialists and ecological “substrate niches” for the specialists to outcompete each other kS kS Substrate (g/L) Growth- Michaelis Menten model

µ = µmax * kS S + S µmax (h-1) (h-1) To be most competitive against other microbes a low ks value and a high umax value are important. This simplified growth model only uses 2 out of 4 growth constants. substrate limitation kS Substrate (g/L) Growth- Michaelis Menten model

µ = µmax * kS S + S (h-1) There is also room for medium substrate “allround” specialists kS kS Substrate (g/L) Growth- Michaelis Menten model

µ = µmax * kS S + S (h-1) With the same ks the organism with a higher µmax will always win. kS Substrate (g/L) Growth- Michaelis Menten model

µ = µmax * kS S + S (h-1) With the same same umax the organism with a lower ks will always win. kS Substrate (g/L) Growth- Michaelis Menten model

Conclusions – substrate limitation
Substrate limitation slows down metabolism Slowed metabolism slows growth (how? via Y!) The limitation effect can be quantified (S/(S+kS)) The quantifier term has values between 0 and 1 e.g. if S=kS then u is half of umax different microbes have different kS competition between microbes is determined by kS and umax What is missing -- maintenance, death, Ymax Growth- Michaelis Menten model

Microbial Growth Comparison of μmax and kS for competition under
Substrate limitation Which of the two growth constants influences to a larger extent The growth of an organism under substrate limitation (substrate Concentration approaches zero) Approach 1. μ = μmax • S ks + S For S approaching zero the μmax term approaches zero. Thus it appears that μ would be mainly influenced by kS (Textbook explanation). Growth- Michaelis Menten model

Microbial Growth Comparison of μmax and kS for competition under
Substrate limitation Approach 2. Question is doubling of μmax (strain A) or halving of kS (strain B) having a larger effect on μ? μmax(A) • S μmax(B) • S μ(A) = μ(B) = ks(A) + S ks(B) + S To compare growth rate of strain A and B: μ(A) = μ(B) μmax(A) • S μmax(B) • S = ks(A) + S ks(B) + S μmax(A) μmax(B) = ks(A) + S ks(B) + S Growth- Michaelis Menten model

Growth- Michaelis Menten model
2 1 = > At all substrate concentrations μmax is more important than kS Growth- Michaelis Menten model

Microbial Growth Dependence of Biomass concentration on substrate used
(Yield Coefficient) - Intro Final X in several batch cultures with increasing [S] X (g/L) Substrate Concentration (g/L) Growth ceased because of lack of substrate Growth ceased because of endproduct inhibition Substrate inhibition Growth- Yield Coefficient

Microbial Growth Dependence of Biomass concentration on substrate used
(Yield Coefficient) - Intro In the absence of inhibition the biomass formed is correlated to the substrate used (X) X (g/L) The correlation factor is the Yield Coefficent (dimensionless, X/S) [S] (g/L) Typical Y for aerobes on glucose: 0.4 to 0.5 Growth- Yield Coefficient

Microbial Growth Yield Coefficient – Role in Establishing Correct Mass Balance The biomass yield coefficient is essential to establish a complete mass balance in a fermentation: E.g. Substrate + Oxygen → Products + Biomass The empirical formula for biomass must be known: CH1.8O0.5N0.2 Growth- Yield Coefficient

Growth- Yield Coefficient
E.g. Gluconate degradation by Klebsiella a. By resting Cells (non growing): Ideal biocatalyst 1 gluconate → 1.5 acetate ethanol + 2 formate b. By growing cells: 1 gluconate NH H2O → 1.4 acetate ethanol formate CH1.8O0.5N0.2 Thus: Growing cells incorporate 14.5 % of carbon from Gluconate into cell growth resulting in increased acetate/ethanol ratio. Growth- Yield Coefficient

Microbial Growth Significance of Special Yield Coefficients X (g/L) Only works for same substrate, pathway YS = S (g/L) X (g/L) Molar yield coefficient S (mol/L) Works only for aerobes and for same ATP/O2 X (g/L) YO2 = O2 (mol/L) + works for unknown or complex substrates (e.g. cornsteep liquor, wastes Growth- Yield Coefficient

Microbial Growth Significance of Special Yield Coefficients X (g/L) Similar to YO2 but works also for other electron acceptors Ye = Mole of reducing equivalents respired X (g/L) Works also for fermenting Bacteria, and unknown Substrates and pathways YkJ = kJ of heat of combustion For scientific purposes under N or P limitation X (g/L) YN, YP = Mole of N or P Growth- Yield Coefficient

Microbial Growth YATP X (g/L) YATP = Mole of ATP + completely comparable between different physiological types + can compare efficiency of growth for aerobes and anaerobes requires knowledge about how much ATP is gained Note: Moles of ATP generated can be estimated for many pathways: e.g. glycolysis to pyruvate 2 ATP consumption of 1 mole of O2 ~ 2 NADH ~ 4 electrons  6 ATP For rich media where all cell building blocks are provided (e.g. Yeast Extract): YATP = 10.5 g/mol Growth- Yield Coefficient

Microbial Growth YATP Comparison of YS and YATP for glucose fermenting bacteria YATP gX/ mol ATP ATP Yield (mol ATP/ mol substance) Organism YS Streptococcus lactis 19.5 2 9.8 Lactobacillus plantarum 18.5 2 9.4 Saccharomyces cerevisiae 18.5 2 9.4 Zymomonas mobilis 9 1 9 Aerobacter aerogenes 29 3 9.6 E. coli 26 3 8.6 The literature valuefor YATP is given as 10.5 g biomass/ mol ATP (Baushop and Elsden 1960) Growth- Yield Coefficient

Microbial Growth Calculation and Inconsistencies of YATP ATP gained per mole of substrate can be estimated for bacteria growing in rich media from Ys if the YATP is known (e.g g/ATP) E.g. If Ys = 21 g/mole of substrate → about 2 mole of ATP generated per mol of substrate Although the YATP is more consistent than any other way of expressing the yield coefficient it can also vary: Not constant for all microbes (4.7 to 21) in rich media Experimental YATP < theoretical YATP (30 g/mol ATP) Low YATP on minimal media YATP dependent on growth conditions (ease of life) ↑ temperature → ↓ YATP (thermal denaturation of proteins) Unsuitable growth conditions → ↓ YATP Likely Reason for less cells formed: Higher ATP usage for cell maintenance rather than cell growth  Maintenance coefficient Growth- Yield Coefficient

Microbial Growth Maintenance Coefficient
1 mole of ATP generated during catabolism allows • theoretically → synthesis of 32 g cells • in praxis → g cells The maintenance coefficient (ms) is the reason for 2/3 being “wasted” Substrate transport into cell (e.g. against diffusion gradient) Osmotic work Motility Intracellular pH Replacement of thermally denatured proteins (↑ T → ↑ ms) Leakage of H+ ions across membrane (uncoupling) ms influences Y, μ and the metabolic activity of the cells and is thus important to be considered in bioprocesses. ms = S (mg) X (mg) • time (h) Growth- maintenenace

Effect of Maintenance Coefficient on Growth Rate
What is maintenance coefficient? The energy supply rate needed to maintain the life functions of a non growing cell. Units? strictly speaking: mol ATP/ cell/ h mostly used: g substrate / g biomass / h = (h-1) Growth- maintenenace

Effect of Maintenance Coefficient on Growth Rate
What does the maintenance coefficient (mS) affect? ms is the reason for Y not being constant. ms   Y hence”: ms   u (compare slide) Why? Because some substrate is taken up just for maintenance, not for growth. Effect is more apparent in slow growing cultures than in fast growing cultures. Slow growing cultures can have a very low Y . mS (gS/gX/h) * Ymax (gX/gS) = Decay rate (h-1) Growth- maintenenace

Effect of maintenance coefficient on growth rate
Effect of mS on Y? Y is the observed yield coefficient. The maximum yield coefficient Ymax is approached only when u = umax Ymax is one of four growth constants Ymax Y (gX/ gS)) Specific Growth rate u (h-1) Growth- maintenenace

mS and Ymax can be combined: mS (gS/gX/h) * Ymax (gX/gS) = Decay rate (h-1) The Pirt equation of growth includes all four growth constants: µ = µmax * mS *Ymax S + kS S Growth- maintenenace

ms the respiration activity used to just stay alive u = (v – mS) *Ymax S u = ( vmax * mS) * Ymax S + kS S µ = Ymax * vmax * mS *Ymax S + kS µ = µmax * mS *Ymax S + kS S substrate uptake Growth- maintenenace

Effect of Maintenance Coefficient (mS) on growth Rate
ignoring mS µ = µmax * kS S + S (h-1) including mS S(g/L) µ = µmax * mS *Ymax S + kS S

Sm = critical substrate concentration  growth is zero (h-1) mS.kS.Ymax sm= umax- ms.Ymax S(g/L) Sm Growth- maintenenace

The negative specific growth rate (µ) observed in the absence of substrate (when S = 0) (cells are starving, causing loss of biomass over time) is the decay rate mS*Ymax (h-1) S(g/L) - mS*Ymax

Points about ms ms  more heat produced (e.g. uncoupler)
when S= 0 and u is negative (decay rate) then any oxygen uptake is via endogenous respiration. Growth- maintenenace

How to obtain microbial growth constants from chemostat runs
µ = µmax * mS *Ymax S + kS Formulae used to determine ms and Ymax ms Y D Ymax --- = Growth- maintenenace

Formulae used to determine ms and Ymax ms Y D Ymax --- = _____1_______ ms D Ymax Y = Y = Ymax + u/ms Ymax = Y - u/ms Growth- maintenenace

Chemostat 1 How to increase growth of microbial culture?
1. Increase initial substrate concentration problem: substrate inhibition 2. Add more substrate during growth (Fed-batch) problem: endproduct inhibition 3. Replace old medium including endproducts by fresh medium at given intervals (semi continuous) 4. Automate semicontinuous culture by applying constant inflow and outflow (continuous culture, chemostat)

Transition from Batch to Chemostat
1 Drop of Inflow of feed medium  increases X by a small amount 1 Drop of reactor volume flows out  removes biomass (X) If the Feed drop allows more X to grow than is taken out by the reactor drop  X in reactor will increase slightly Repeat the loop and there will be gradual increase in X  However, as the X increases each drop of outflow will contain more X which is removed from the reactor  rate of out put increases as a consequence the biomass will reach a certain steady state level. The flow rate can be varied but has little effect on the level of biomass concentration. The specific flowrate (i.e. flowrate (L/h) per reactor volume (L) is called the Dilution rate Assumptions: Reactor volume stays constant (Use learning activity on chemostat to visualise how it works) (use bioprosim simulation to demonstrate chemostat versus batch) (Could use a modelling spreadsheet to demonstrate the steady state)

Effect of Increasing the Dilution rate
After a steady state is reached from continuously adding and removing drops (continuous dilution rate) a steady state is reached consisting of a constant high biomass level (X) and a very low substrate level (S) because the biomass degrades the substrate down to limiting concentrations. If the dilution rate is doubled :  More substrate per time is supplied  bacteria grow faster  bacteria are washed out faster  steady state is reached again with a slightly higher substrate steady state concentration, slightly lower biomass level bacteria growth rate being twice as high bacteria growth rate u compensating for the Dilution rate D

Effect of loading rate increase (R = X * D)
Chemostat 4 Effect of loading rate increase (R = X * D) 1 Double F  and D  doubles μ Almost doubles R Affects X only a little Double SR  doubles X doubles R Affects μ only a little SR = 2 g/L SR = 2 g/L h h SR = 2 g/L SR = 4 g/L SR = 2 g/L h h X and μ can be set independently, X by SR and μ by D

Key features of steady states 1:
Inflow rate = Outflow rate Dilution rate= specific growth rate u S limitation of growth X stays constant over wide range of D If D approaches umax  washout (Dcrit) Beyond (Dcrit) S = SR SR X Steady State Concentration R S D Dcrit Effect of Dilution rate on chemostat steady state concentrations X= biomass, S= substrate, SR= substrate in Reservoir R=productivity

Open system, time factor excluded Allows to study microbial behaviour at constant growth rate µ of culture can be controlled by changing D D  S  µ but not X (because of washout) How can X be controlled? SR X Steady State Concentration R S D Dcrit Effect of Dilution rate on chemostat steady state concentrations X= biomass, S= substrate, SR= substrate in Reservoir R=productivity

X can be controlled by SR (dotted line using more dilute feed) Doubling SR doubling of X and of R (to a point) The level of X also depends on Y SR X Steady State Concentration R S D Dcrit Effect of Dilution rate on chemostat steady state concentrations X= biomass, S= substrate, SR= substrate in Reservoir R=productivity

R = X * D g/L/h = g/L * h-1 R is largely a function of D until washout occurs R can be increased by: D   µ but not X SR  X but not  u Such control does not exist in batch culture. SR X Steady State Concentration R S D Dcrit Effect of Dilution rate on chemostat steady state concentrations X= biomass, S= substrate, SR= substrate in Reservoir R=productivity

Steady State Concentration
X S D Steady State Concentration R Dcrit SR Productivity R : R = X * D g/L/h = g/L * h-1 As R =D*X and u=D R can be increased by D, Y, SR kS note: ms is ignored for high D and Y is assumed to be ~Ymax Where is the max chemostat productivity? SR – kS.D R = D.Y µmax -D

Max chemostat productivy (Rm):
D at which R= Rm is called Dm Dm can be calculated from growth constants: Rm = X* Dm SR X Rm Steady State Concentration R Dm = µmax. ( kS/(SR- kS) S D Dcrit Note: at Dm some substrate wastage occurs  Less conversion efficiency than at lower D Operator can aim for: high productivity or high conversion efficiency (of SX) Dm is dangerously close to Dcrit (especially when ks is low)

Chemostat productivity no lag phase, no preparation phase
but only highly concentrated cell suspensions with close to exponential growth  very high productivity compared to batch total or maximum productivity. SR X (g/L) chemostat batch maximum productivity batch total productivity Time (h)

Comparison of productivity batch vs chemostat:
Chemostats: no lag phase, no preparation phase but only highly concentrated cell suspensions with close to exponential growth  very high productivity compared to batch total or maximum productivity. SR X (g/L) chemostat batch maximum productivity batch total productivity Time (h)

X S Key features of steady states 2: R is largely a function of D until washout occurs µ of culture can be controlled by changing D X can be controlled by changing SR ( Steady State Concentration R D Dcrit Effect of Dilution rate on chemostat steady state concentrations X= biomass, S= substrate, R=productivity

Comparison Chemostat / Batch Culture
Productivity (R) – Comparison Chemostat / Batch Culture Batch culture t prep = preparation time since last run (cleaning, sterilizing, lag phase etc.) Xmax - Xo R = tprep + tlog t log = running time under full growth R = DX (1 - tprep / tlog) Chemostat for long runs R = DX The productivity of chemostats can be several times higher than in batch culture

Chemostat 10 Oxygen limitation
The classical chemostat theory is based on the concept of substrate limitation. In practice the oxygenation capacity of a bioreactor may become limiting. This results in the deviation of the classical chemostat. Chemostat under oxygen limitation S-limited O2-limited S-limited D ks kLa X = Y (SR )( ) μmax -D D 1 1) Low D  no O2 limitation 2 X 2)  D  O2 limitation  μ<D   X 3)  X   O2 limitation 3 4 D

The Critical Dilution Point
Chemostat The Critical Dilution Point The critical dilutino point Dc results in washout of biomass (X = 0, S = SR)  μmax   Dc  ks   Dc SR little affect on Dc μmax SR Dc = Ks + SR Neglecting ms Dilution rates > μmax can be used to determine μmax: If D > μmax  logarithmic biomass washout In Xt -In Xo μmax = + D t X μmax = (In Xt - In Xo) t + D Washout kinetics Fixed D D > μmax Time

Advantages and disadvantages compared to batch
Chemostat Advantages and disadvantages compared to batch Chemostat +  prodctivity + constant requirement for cooling, O2 transfer, labour, etc Heat requirements for batch cultures Heat Cool Heat

Run a chemostat at equilibrium making sure that oxygen is not limiting Note down the substrate in the reservoir (SR) Calculate the Dilution rate from the Flowrate and the reactor Volume: D= F/V. check that units cancel to h-1 Note down the steady state values for S, X and DO Determine the observed yield coefficient Y by dividing the biomass formed (X) by the amount of substrate degraded (SR-S) Change the dilution rate by about 10% steps upward and downward. Wait for equilibrium each time. Tabulate your values of X, S, SR, DO for each D in a spreadsheet

Tabulate your values of X, S, SR, DO for each D in a spreadsheet Considering that at steady state u=D, produce additional columns that calculate 1/Y and 1/u Plot 1/Y against 1/u and obtain the linear equation with the slope. Make sure you are clear about the units of both axes and the slope Obtain the ms and Ymax constants from the slope and the intercept respectively Alternatively using simultaneous equations allows you to calculate the constants from the equation

Tabulate the inverse of the substrate concentration present in the chemostat (S that determines u) Tabulate 1/(u +ms*Ymax) Use the double inverse plot of the two items above. This is similar to the Lineweaver Burk Plot Read 1/u from the Y axis intercept and -1/ks from the x axis intercept All 4 of the growth constants are now obtained

Effect Double inverse plot of Y and u from chemostat runs (u=D)
Graphical determination of 2 growth constants from chemostat steady state data 1/y mS 1/Ymax 1/µ Dcrit Effect Double inverse plot of Y and u from chemostat runs (u=D)

some activity will be used exclusively for maintenance without growth.
The relationship between the rate of substrate uptake (= OUR) and growth rate some activity will be used exclusively for maintenance without growth. If qS is > ms  growth will occur. qS 1/Ymax ms Relationship between specific metabolic acitity (qS) and specific growth rate qS = v = substrate uptake rate / X

Microbial Growth Ms at [S] = 0
• Even if [S] = 0, some metabolism is required to stay alive. • The substrate used from either energy stores (e.g. PßHB) or biomass itself. Thus: Net growth = total growth – biomass consumed Exogenous Respiration + S q e.g. SPOUR Endogenous Respiration Time For aerobic organisms the maintenance coefficient can be measured via the endogenous respiration rate.

Formulas not to be used:
R = X* µmax * mS *Ymax S + kS

A chemostat is the preferred culture method …
A chemostat is the preferred culturing method… …when the process wants to select for the fasted growing strain (single cell protein, degradation of pollutants) …when substrate limitation (e.g. substrate toxicity) is desired …contamination or mutation does not play a role (e.g. extreme conditions of temperature, pH, etc.) …for studying metabolic behavior at specified conditions (e.g. pH, cell density, substrate concentration, product concentration, specific growth rate) (remember u and X can be set constant separately by selecting D and SR) … for studying effects of shocks (e.g. toxic substances) or minute disturbances of equilibrium (pH change, DO change)

Chemostats are not suitable for…
production of recombinant products (tendency of backmutation to the wild strain “contamination from inside”) aseptic cultures with high tendency of contamination (continuous sterile supply of feed and harvest is difficult) where traditional methods play a role where there is a need for changing conditions (e.g. preventing respiratory deficient mutants in brewing, feast and famine regime the production of secondary metobolites (produced after growth)

Chemostat productivity (R = X*D) can be increased by
production of recombinant products (tendency of back-mutation to the wild strain “contamination from inside”) aseptic cultures with high tendency of contamination (continuous sterile supply of feed and harvest is difficult) where traditional methods play a role where there is a need for changing conditions (e.g. preventing respiratory deficient mutants in brewing, feast and famine regime) the production of secondary metabolites (produced after growth)

Chemostat productivity (R = X*D) can be increased by
maximising the dilution rate (D) maximising the biomass concentration in the reactor (via substrate in the feed (SR) or via organism with high Ymax) Highest biomass productivity (g of cells produced L-1 h-1)  highest product formation rate of primary metabolites  highest substrate degradation rate  highest oxygen uptake rate

Advantages and disadvantages compared to batch
Chemostat Advantages and disadvantages compared to batch + less labour + facilitates automation + constant output + easy for problem monitoring + - selects for best growing strain - more susceptible to contamination from outside longer running times  higher probability continuous sterilisation difficult - contamination from inside (back mutation to wild type) - breaking tradition of batch processes (e.g. beer)

Chemostat Main applications
1. Where the fastest growing strain is required, e.g. single cell protein (SCP), effluent, strain selection (e.g. higher affinity). 2. Generating functional understanding (not empirical observation) e.g. process optimisation, research. 2.1. Time is excluded  every part of a batch process can be studied over extended periods. E.g. to investigate problem; set chemostat to exactly these conditions. 2.2. Permits to vary μ by changing D under otherwise constant conditions.

Chemostat Main applications
2.3. Study of environmental changes (pH, temperature, salinity, [S] etc.) at constant μ 2.4. Allows to study growth and metabolism under substrate limitation (ecological studies, pollution, waste treatment) 2.5. Allows to study effect of 1 parameter only 3. Batteries of small research chemostats to complement to sophisticated industrial batch reactor

Why not continuous beer brewing?
1. Contamination from inside and outside (e.g. respiratory deficient mutants (RMD)) 2. Start up time until state gives low quality beer 3. Although HRT is 5 to 10 times less than for batch process, only small economic benefit is achieved in comparison with the cleaning condition and packaging of the product.

Chemostat Biomass Feedback Justification for biomass feedback
High [X] is essential for high metabolic activity -ds/dt = μ X/Y Usually X can be controlled by SR in reaction For waste water treatment (e.g. activated sludge) SR can not be controlled and is very low Biomass feedback can keep X high, S extremely low and achieve high R Idea Prevent X from being washed out

Technique Physically retain or return biomass resulting in longer biomass (solid) retention time SRT than liquid retentiontime (HRT) Options Immobilization of cells (e.g. fixed bed reactors, fluidised bed reactors, rotating disk contractor (RDC)) Internal recycle Eternal recycle

Chemostat Biomass Feedback 2
The most simple idea to retain biomass: Filter Feed Chemostat Filter Outflow Because the filter clogging (fouling) simple bacterial filters cannot be used Cross flow filtration, and filter capillaries are used for the separation of expensive products

Biomass feedback by internal sedimentation of biomass → Flocculation necessary (Stroke’s Law)

External biomass feedback in activated sludge treatment Feed Outflow Settler Reactor Biomass Recycle Biomass feed back: Ú X, Ú R, Ú Dc, Æ S proportionally to the efficiency of feedback

Retaining biomass in a chemostat
Preventing biomass from washout allows buildup of higher biomass concentration when is this useful? When feed concentration (SR) can not be concentrated (eg. feed toxicity or fixed feed (e.g. waste water)) When only very little biomass growth will be obtained (e.g. mineral media, autotrophs, extreme conditions such a bioleaching)

Retaining biomass in a chemostat
How can biomass be retained? In theory a filter that allows liquid outflow but no biomass outflow works In practice: Filter fouling Alternatives (Cross flow filtration, Inflow Outflow

Practical Biomass retention inside the reactor
Biofilm reactors (a) fixed bed reactor (trickle reactor) (b) fluidised bed reactor (c) sludge blanket reactor (settling biomass flocs) (e.g. sequencing batch reactor (Feed-React-Settle-Decant Problems: mass transfer (e.g. oxygen), channeling Inflow Outflow

Practical Biomass retention via biomass feedback
Centrifuging of recycle liquid Membrane filtration of recycle liquid Flocculation Gravity settling of flocculated biomass Inflow Outflow Recycle (Feedback)

Effect of biomass feedback (here 3 fold):
Dotted line no feedback: Washout occuring early 3-fold Feedback approximately: 3*X 3*R  1/3* S allows 1/3 reactor size to do same work Feedback essential for pollutant removal. Can be used 100-fold  100-fold smaller treatment plant Note: same assumed feed concentration (SR) SR X Steady State Concentration R S D Dcrit

Effect of increased μmax Effect of increased Y
Effects of growth constants on steady state concentrations of biomass and substrate in a chemostat as a function of dilution rate (x-axis) Effect of ms Effect of decrease ks Effect of increased μmax Effect of increased Y

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