Criterios generales para la adecuación de la sala de tomografía

are actually involved in material flow (or fluxes); others are involved in cellular structure or in signal flow. MFA primarily concerns the flow of materials. For all practical purposes, we can account for only carbon- and nitrogen-containing species. The flow of ionic species (e.g., sodium, potassium, phosphate, etc) are not considered in MFA. Even considering only the flow of carbon- and nitrogen-containing compounds, the number of reactions is over a thousand. However, only a portion of these reactions has a significant flux. Overall, the glycolysis pathway has the highest flux for cultured cells. There are only a hundred, or so, reactions whose flux is >5% of that of glycolysis. The vast majority of the reactions have a flux in the order of 1% of glycolysis, or lower. Considering the errors in carbon and nitrogen balances, MFA is best applied to analyze the flux of major arteries in carbon flow. It does not provide accurate estimates for minor reactions, such as glycan distribution in the product protein. To apply MFA for the reactions with minor fluxes, an accurate quantification of the specific formation rate of key reaction intermediates is necessary. The first step in applying MFA to a cell system is to reduce whole cell reaction networks to a manageable and meaningful subset of pathways. In general, those pathways include glucose and amino acid metabolism, and the biosynthesis of building blocks for biomass and product formation. In the course of simplifying the reaction network, different assumptions are made and often a different set of reactions are included in the analysis. The selection of pathways will impact the flux distribution.

Selecting Reactions for Analysis

Applying MFA to entire cell metabolism

• a vastly large number of reactions involving material flux

• most of them have very small fluxxes, much smaller than errors in closing balance of materials

• select the reactions and pathways which are relevant and sufficiently large

• to zoom in on pathways with very small fluxxes, use a root type tracer to examine fluxxes of local reactions

Fig. 8.2: Simplification of metabolic pathways for metabolic flux analysis.

Compartmentalization Cells are compartmentalized to segregate various

reactions; genome replication and RNA synthesis occurs in the nucleus and glycolysis takes place in cytosol. Among the major metabolic reactions, TCA cycle, oxidative phosphorylation, and fatty acid oxidation occur in the mitochondria. Across the boundary of cytosol and mitochondria, pyruvate, glutamine, and components of the malate-aspartate shuttle pass at high fluxes. The flux of the malate- aspartate shuttle transports the reducing equivalent of NADH into the mitochondria, at the same level as pyruvate. The flux of the citrate shuttle generates acetyl CoA in the cytosol and is typically lower, but under some growth conditions it can be rather large. These shuttles and transfers across the mitochondrial membrane pose further constraints on material flow. Imposing those constrains on MFA is important for obtaining a good estimate of fluxes of energy metabolism. The regulation of fluxes across the cytosol and the mitochondria may play a role in shift of metabolism. Therefore, building a flux analysis model based on two compartments of the cytosol and mitochondrion is a worthwhile effort.

Biomass Equations With the exception of cell mass, all outputs of a

cell system have a defined chemical composition. Their production rates are measured and readily used in MFA. In contrast, the cell concentration is often measured by the cell number, and not biomass. Furthermore, the composition of the cell mass is seldom characterized. Under most culture conditions, the vast majority of carbon taken up by cells as nutrients is converted to lactate and carbon dioxide. Only a small fraction is actually incorporated into new cell mass. Because this amount is such a small fraction of the total carbon consumed, the error in the estimation of biomass does not significantly affect the MFA results. However, under an oxidative metabolic state (characterized by reduced specific glucose consumption and very little lactate production or consumption), the amount of carbon and nitrogen channeled to biomass becomes a significant

• Carbohydrate metabolism takes place in cytosol and mitochondria

• lipid metabolism involves even more compartments • intercompartmental traffic of pyruvate and malate-

aspartate shuttle also involve amino acids and TCA cycle intermediates

• flux balance must be met for the intercompartmental traffic

• biomass equation is very difficult to construct and is subject to errors

• under fast growing and lactate production conditions, biomass formation constitutes only a small fraction of total carbon

portion of the overall material flow. Since the components of the biomass are drawn from fluxes leading to building blocks, the composition of cell mass affects the results of flux analysis. Despite their potential influence on the estimation of fluxes, the stoichiometric equations of biomass formation are not often addressed. Based on elemental analysis of C, N, O, and H of a mouse hybridoma cell line, a general compositional formula was given as: CH_1.975N0_.2605O_0.489 . The general range of cellular composition of lipids, proteins, nucleic acids, and polysaccharides is also available. The amino acid composition of cellular proteins has been reported for a number of cell lines. Overall, the available data is very limited. One should be aware of this potential error when applying the literature to the biomass equation.

BIOMASS Proteins

DNA/RNA Lipids/Carbohydrates, etc. Amino acidsGlucose

Biomass Equation Derived from Cellular Components

Macromolecule pg per cell

Protein 300 DNA/RNA 15/30 Lipids/Carbohydrates 55 Total dry weight 400

Elemental composition of cell

C N H O

1 0.2605 1.975 0.489

Solution and Analysis A simplified cellular reaction system may consider

only the metabolism of glucose and amino acids, while simultaneously lumping lipid and nucleotide synthesis into a biomass formation equation. The resulting reaction network consists of about fifty fluxes involving a similar number of compounds. Such a system can be solved using software such as Mathmatica and MatLab, using the least square method to minimize residue. The solution gives a set of values to unknown fluxes, as well as to the specific rates whose measured values are already known. One should compare the values given by the solution and the measured values. It is also prudent to check material balance based on the solution values. If major deviations are seen, the results need to be reevaluated. The results of MFA involve tens of variables depicting the fluxes in the reaction network. Such data are difficult to comprehend, without a proper graphic presentation. To gain insight, it is helpful to link the results of MFA to a metabolic map for visualization. Such visual images greatly enhance our ability to discern shifts in metabolism.

Fig. 8.3: Determining elemental composition for biomass stoichiometric formula

An Example For an example of MFA, a MatLab algorithm is

presented and its solution is included. The reactions considered in the reaction network are listed in the accompanying table. These include reactions for glycolysis, TCA cycle, amino acid degradation pathways, biomass, and antibody synthesis. The metabolic network is compartmentalized into the mitochondria and the cytosol. Reactions in the malate-aspartate shuttle (also known as the NADH shuttle) are included to account for the transfer of the reduction potential of NADH generated in cytosol into the mitochondria, thereby regenerating the levels of cytosolic NAD. Further, three enzymes, including mitochondrial malic enzyme, cytosolic malic enzyme, and pyruvate carboxylase, are also included in the reaction network. The respiratory quotient is assumed to be 1.0; in other words, the carbon dioxide production rate is assumed to be the same as the oxygen uptake rate. The specific rates determined for two metabolic states of NS0 cells: one produces lactate in the exponential growth phase and the other consumes lactate in late growth stage. These rates are used to solve the fluxes in each situation. Upon the solution, the results are displayed in a metabolic chart. The contrast of the two metabolic states is seen, not only in the specific rates of glucose and glutamine, but also in the internal distribution.

# Reaction Compartment Pathway

1 GLCc + 2NADc → 2PYRc + 2NADHc + H2O Cytosol Glycolysis

2 PYRc + NADHc → LACc + NADc Cytosol Lactate Dehydrogenase Reaction

3 PYRm + NADm → AcCoAm + NADHm + CO2 Mitochondria Krebs Cycle

4 OAAm + AcCoAm → CITm Mitochondria Krebs Cycle

5 CITm + NADm + H2O → AKGm + NADHm + CO2 Mitochondria Krebs Cycle

6 AKGm + NADm → SUCCoAm + NADHm + CO2 Mitochondria Krebs Cycle

7 SUCCoAm + H2O → FUMm Mitochondria Krebs Cycle

8 FUMm + H2O → MALm Mitochondria Krebs Cycle

9 MALm + NADm → OAAm + NADHm Mitochondria Krebs Cycle

10 GLNc → GLUc + NH3 Cytosol Glutaminolysis

11 GLUm → AKGm + NH3 Mitochondria Glutaminolysis

12 PYRc + GLUc → ALAc + AKGc Cytosol Alanine Synthesis

13 SERc → PYRc + NH3 Cytosol Amino Acid Degradation

14 GLYc → SERc Cytosol Amino Acid Degradation

15 CYSm → PYRm + NH3 Mitochondria Amino Acid Degradation

16 ASNc → ASPc + NH3 Cytosol Amino Acid Degradation

17 HISm → GLUm + NH3 Mitochondria Amino Acid Degradation

18 ARGm + AKGm → 2GLUm Mitochondria Amino Acid Degradation

19 PROm → GLUm Mitochondria Amino Acid Degradation

20 ILEm + AKGm → SucCoAm + AcCoAm + GLUm Mitochondria Amino Acid Degradation

21 VALm + AKGm → GLUm + CO2 + SucCoAm Mitochondria Amino Acid Degradation

22 METm → SucCoAm Mitochondria Amino Acid Degradation

23 THRm → SucCoAm + NH3 Mitochondria Amino Acid Degradation

24 PHEm → TYRm Mitochondria Amino Acid Degradation

25 TYRm + AKGm → GLUm + FUMm + 2AcCoAm Mitochondria Amino Acid Degradation

26 LYSm + 2AKG → 2GLUm + 2 CO2 + 2AcCoAm Mitochondria Amino Acid Degradation

27 LEUm + AKGm → GLUm + 3AcCoAm Mitochondria Amino Acid Degradation

28   0 .0104GLNc + 0 .0110ALAc + 0 .0050ARGm + 0 .0072ASNc + 0 .0082ASPc

+ 0 .005CYSm + 0 .0107GLUc + 0 .0145GLYc + 0 .0035HISm + 0 .0050ILEm +

0 .0142LEUm + 0 .0145LYSm + 0 .0028METm + 0 .0072PHEm + 0 .0148PROm +

0 .0267SERc + 0 .0160THRm + 0 .0085TYRm + 0 .0189VALm → CH1 .539N0 .2645O0 .314

Cytosol/Mitochondria Antibody Synthesis

29 0 .208GLCc + 0 .0377GLNc + 0 .0133ALAc + 0 .0070ARGm + 0 .0ASNc + 0 .0261ASPc

+ 0 .0004CYSm + 0 .0006GLUc + 0 .0165GLYc + 0 .0033HISm + 0 .0084ILEm +

0 .0133LEUm + 0 .0101LYSm + 0 .0033METm + 0 .005PHEm + 0 .0081PROm +

0 .0099SERc + 0 .0080THRm + 0 .0040TYRm + 0 .0096VALm → CH1 .975N0 .2605O0 .489

Cytosol/Mitochondria Biomass Synthesis

30 CITm + MALc → CITc + MALm Cytosol/Mitochondria Fatty Acid Synthesis

31 CITc → AcCoAc + OAAc Cytosol Fatty Acid Synthesis

32 MALc → MALm Cytosol/Mitochondria Glutaminolysis

33 GLUc → GLUm Cytosol/Mitochondria Glutaminolysis

34 OAAm + GLUm → AKGm + ASPm Mitochondria Malate Aspartate Shuttle

35 OAAc + NADHc → MALc +NADc Cytosol Malate Aspartate Shuttle

36 AKGc + ASPc → OAAc + GLUc Cytosol Malate Aspartate Shuttle

37 ASPm + GLUc → ASPc + GLUm Cytosol/Mitochondria Malate Aspartate Shuttle

38 MALc + AKGm → MALm + AKGc Cytosol/Mitochondria Malate Aspartate Shuttle

39 MALm + NADm → PYRm + CO2 + NADHm Mitochondria Malate Decarboxylation (Malic Enzyme)

40 MALc → PYRc + CO2 Cytosol Malate Decarboxylation (Malic Enzyme)

41 2NADHm + O2 → 2NADm Mitochondria Oxidative Phosphorylation

42 2FADH2 + O2 → 2FAD Mitochondria Oxidative Phosphorylation

43 PYRm + CO2 → OAAm Mitochondria Pyuvate Caboxylation (Pyruvate Carbox-

ylase)

Abbreviation Name

AB Antibody

AcCoA Acetyl Coenzyme A

AKG A-Ketoglutarate ALA Alanine ARG Arginine ASN Asparagine ASP Aspartate BIOMASS Biomass CYS Cysteine CO₂ Carbon Dioxide FUM Fumarate GLC Glucose GLN Glutamine GLU Glutamate GLY Glycine HIS Histidine ILE Isoleucine LAC Lactate LEU Leucine LYS Lysine MAL Malate MET Methionine NH₃ Ammonia OAA Oxaloacetate PHE Phenylalanine PRO Proline PYR Pyruvate SER Serine

SucCoA Succinate Coenzyme A

THR Theonine

TYR Tyrosine

VAL Valine

O₂ Oxygen

NADH Nicotinamide Adenine Dinucleotide

(Reduced)

NAD Nicotinamide Adenine Dinucleotide

(Oxidized)

FADH₂ Flavin Adenine Dinucleotide (Reduced)

FAD Flavin Adenine Dinucleotide (Oxidized)

Subscript Compartment

c Cytosol

m Mitochondria

Concluding Remarks

MFA is an important analytic technique of quantitative physiology. It can provide insight into process optimization and metabolic engineering. A flux balance can be written for each metabolite, within a cellular or metabolic system, to yield the dynamic mass balance equations that interconnect various metabolites. With the knowledge of stoichiometry and steady state assumption, one can obtain the flux estimate for individual reactions. MFA is a powerful tool, but it is invariably based on a simplified reaction network. The assumptions made in simplifying the reaction network will affect the results of the analysis. It is best used for a first approximation to obtain some insights for further exploration of ideas.

Cell Culture Bioreactors

Cell Culture Bioreactors

Introduction . . . .192

Basic Types of Bioreactors . . . .193

Stirred Tank (Well Mixed) Vs . Tubular Reactor (Plug Flow) . . . . 193

CSTR and PFR with Reaction . . . . 194

Implication When Growth or Reaction Occurs in the Reactor . . . . 195

Heterogeneous Reactor- High Solid Content . . . . 196

Operating Mode of Bioreactors . . . .196

Batch and Continuous Processes . . . . 196

Batch Cultures . . . . 197

Fedbatch Cultures . . . . 198

Tissue Culture and Disposable Cell Culture Systems . . . .199

Disposable Culture Systems . . . . 199

Cell Support Systems . . . .202

Suspension Culture vs . Adherent Cultures . . . . 202

Microcarriers . . . . 203

Cell Culture Bioreactors . . . .206

Simple Stirred Tank Bioreactor . . . . 206

Airlift Bioreactor . . . . 207

Fluidized Bed Bioreactor . . . . 207

Membrane Bioreactor . . . . 208

Multiple Membrane Plate Bioreactor . . . . 208

Other Bioreactor Systems . . . .209

Microsphere Induced Cell Aggregates . . . . 209

Agarose Cell Immobilization . . . . 209

Microencapsulation . . . . 210

Membrane Stirred Tank . . . . 210

Spin Filter Stirred Tank . . . . 210

Vibromixer . . . . 212

In document Universidad del Azuay (página 66-69)