Presentation on theme: "Engineering of Biological Processes Lecture 4: Production kinetics Mark Riley, Associate Professor Department of Ag and Biosystems Engineering The University."— Presentation transcript:
Engineering of Biological Processes Lecture 4: Production kinetics Mark Riley, Associate Professor Department of Ag and Biosystems Engineering The University of Arizona, Tucson, AZ 2007
Objectives: Lecture 4 Investigate production kinetics and limiting factors. Characterize product formation based on yields
Production kinetics Classified based on the relationship between product synthesis and energy generation in the cell –Growth associated –Non-growth associated –Mixed-growth associated
Products Growth-associated –produced at the same time as cell growth constitutive enzymes (ones that are normally present) –glucose isomerase metabolic intermediates –pyruvate, citrate, acetate Non-growth-associated –takes place during the stationary phase ( =0) secondary metabolites –antibiotics Mixed - growth associated –takes place during growth and stationary phases metabolic byproducts –lactate, ethanol secondary metabolites
Protein production Antibody (MAb) production by mammalian cells (hybridomas)? –Growth associated –Non-growth associated –Other? In 1990, Suzuki and Ollis (NCSU) developed a structured model that suggested "negatively growth associated" MAb production kinetics. Biotechnol Prog. 1990 May-Jun;6(3):231-6. Suzuki E, Ollis DF.
Hybridoma cultures where growth was slowed by either a DNA synthesis inhibitor or by a selective inhibitor of initiation of nonantibody protein exhibited 50-130% MAb production rate enhancement for growth slowed up to 50%. Experiments inconsistent with this approach showed other behavior: general inhibition of protein chain elongation (by cycloheximide) or inhibition of ribosomal RNA (rRNA) synthesis (by actinomycin D) each slowed both growth and the specific MAb production rate, leading to net "positive" growth associated MAb production rates.
How can we account for this behavior? Generalized production equation
Direct coupling to energy metabolism For products formed in pathways which generate ATP, rate of production is related to cellular energy demand. Growth is usually the major energy-requiring function of cells; therefore, if production is coupled to energy metabolism, product will be formed whenever there is growth.
Direct coupling to energy metabolism The above is a gross over-simplification of production rates.
Maintenance ATP is also required for other activities called maintenance. –cell motility –turnover of cellular components –adjustment of membrane potentials and internal pH
Kinetic expressions require growth-associated and maintenance-associated production Growth assoc.Non-growth assoc. Y P/X is the theoretical yield of product from biomass, m p is the specific rate of product formation due to maintenance, and x is biomass concentration.
Result = Y’ p/x is higher than anticipated based on growth alone ObservedTheoretical
Effect of incorporating maintenance terms Gives observed yields rather than theoretical yields Accounts for unusual behavior –negative association with growth
Cell growth stages in a batch culture Limited by the depletion of a resource (nutrient, space, oxygen).
Product formation indirectly or not coupled to energy metabolism Product Formation Indirectly Coupled With Energy Metabolism –Relationship between product formation and growth can be complicated. Beyond the scope here. Product Formation Not Coupled With Energy Metabolism –Production not involving energy metabolism is difficult to relate to growth because growth and product synthesis are dissociated. –Rate of formation of non-growth-associated product can be directly proportional to biomass concentration, constant q p q p = complex function of growth rate –empirical equations derived from experiment.
Substrate uptake Used for: –making biomass (x) –making product (p) –maintenance (m s ) Assumes substrate used only to make product (no x or m s )
Substrate uptake Xqr ss = Result = r s is higher than anticipated based on only growth and product formation Cell growth Product formation Maintenance
Lot's of parameters to estimate Need values for:
To determine the metabolic parameters Need data on: –substrate uptake with time with and without product formation –product generation with time with and without cell growth –cell growth with time
So, what do these yields yield? Basic estimation of nutritional requirements Targets for manipulation –Growth rates Maintenance terms –m s, m p Fudge factors to explain why Y p/s > Y’ p/s
Maximize production Growth assoc.Non-growth assoc.
How do we alter these parameters? Control nutrient and oxygen supply Cultivation methods – fed batch Strain selection – high producers Strain optimization –Recombinant DNA techniques –Metabolic engineering
"Growth, metabolic, and antibody production kinetics of hybridoma cell culture: Effects of serum concentration, dissolved oxygen, and pH in a batch reactor." The effects of serum, dissolved oxygen (DO) concentration, and medium pH on hybridoma cell growth, viability, cell density, carbohydrate and amino acid metabolism, respiration and energy production rates, and antibody production rates were studied. Cell growth was enhanced and cell death was decreased by increasing the serum level. The growth rates followed a Monod-type model with serum being the limiting component. Specific glucose, glutamine, and oxygen uptake rates and specific lactate and ammonia production rates did not change with serum concentrations. Amino acid metabolism was slightly influenced by the serum level. Oxidative phosphorylation accounted for about 60% of total energy production. This contribution, however, increased at low pH values to 76%. The specific antibody production rate was not growth associated and was independent of serum and DO concentrations. A 2-fold increase in specific antibody production rates was observed at pH values below 7.2. Higher concentrations of antibody were obtained at high serum levels, between 20% and 40% DO, and at pH 7.20 due to higher viable cell numbers obtained. Biotechnol Prog. 1991 Nov-Dec;7(6):481-94. Ozturk SS, Palsson BO.