Cellular kinetic constants

Cellular kinetic constants (K- ) were also determined (13) for the inhibition of natural precursor incorporation into the DNA of L-1210 cells by FEAD and FHAD. Using tritium-labe 1 led natural precursors as substrates (thymidine, 2 -deoxyurldine and 2 -deozycytidine), it was found that the FEAU/FHAII ratios were 61, 111, and 8.1, respectively. These results also Indicated that FEAD is a weaker inhibitor of natural nucleoside anabolism than FMAU in these mammalian cells. 

Metabolic networks can be quantitatively and qualitatively studied without enzyme kinetic parameters by using a constraints-based approach. Metabolic networks must obey the fundamental physicochemical laws, such as mass, energy, redox balances, diffusion, and thermodynamics. Therefore, when kinetic constants are unavailable, cellular function can still be mathematically constrained based on the mass and energy balance. Flux balance analysis (FBA) is a mathematical modeling framework that can be used to study the steady-state metabolic capabilities of cell-based physicochemical constraints. ... 

The biosynthesis of a particular enzyme is itself an elaborate and complex process Involving several cellular components. The genetic information for any particular enzyme is carried in a stretch of DNA which is the structural gene for that protein. The pattern is transcribed in a strip of the messenger RNA that dictates the proper sequence of amino acids in the synthesis of the enzyme. Van Dedem and Moo-Young have made an interesting beginning into incorporation of the operon theory of Jacob and Monod into a kinetic model for enzyme syntheses [3]. Indeed even a relatively simple model leads to many unidentifiable kinetic constants. 

If initial solute uptake rate is determined from intestinal tissue incubated in drug solution, uptake must be normalized for intestinal tissue weight. Alternative capacity normalizations are required for vesicular or cellular uptake of solute (see Section VII). Cellular transport parameters can be defined either in terms of kinetic rate-time constants or in terms of concentration normalized flux [Eq. (5)]. Relationships between kinetic and transport descriptions can be made on the basis of information on solute transport distances. Note that division of Eq. (11) or (12) by transport distance defines a transport resistance of reciprocal permeability (conductance). 

While in vivo studies assess absorption rates as process-lumped time constants from blood level versus time data, these rate parameters encompass the kinetics of dosage-form release, GI transit, metabolism, and membrane permeation. The use of isolated tissue and cellular preparations to screen for drug absorption potential and to evaluate absorption rate limits at the tissue and cellular levels has been expanded by the pharmaceutical industry over the past several years. For more detail in this regard, the reader is referred to an article by Stewart et al. [68] for references on these preparations and for additional details on the various experimental techniques outlined below. 

The selective binding of molecules to form productive complexes is of central importance to pharmacology and medicinal chemistry. Although kinetic factors can influence the yields of different molecular complexes in cellular and other non-equilibrium environments,1 the primary factors that one must consider in the analysis of molecular recognition are thermodynamic. In particular, the equilibrium constant for the binding of molecules A and B to form the complex AB depends exponentially on the standard free energy change associated with complexation. 

For limiting nutrients, cellular concentrations are constant under conditions of steady-state growth. To ensure that the limiting nutrient is not diluted in the microbial population, kmt must be greater than the maximal growth rate, /imax. This limiting condition sets a minimum for the value of the Monod constant, Kmd = / max /[7]- Note that while Monod kinetics are more applicable than first-order kinetics for many ecological uptake processes, solutions of the above equations require considerably more a priori information [48]. 

Item (ii) is largely employed in obtaining kinetic parameters such as the substrate limitation and inhibition constants for cellular growth and death (kr and k s, respectively) and substrate inhibition constants for production (k s) maximum specific growth and death rates (px>max an(J kd max), as well as the global yield factors (Yx/s and Yp/s), that may be associated or not to the cellular growth. 

The primary function of the Na+/H+ exchanger is to maintain pHi relatively constant. Its kinetic properties and possible regulation by phosphorylation reactions are ideally suited to protect the cell against both acute and chronic cellular acidosis. 

Often the key entity one is interested in obtaining in modeling enzyme kinetics is the analytical expression for the turnover flux in quasi-steady state. Equations (4.12) and (4.38) are examples. These expressions are sometimes called Michaelis-Menten rate laws. Such expressions can be used in simulation of cellular biochemical systems, as is the subject of Chapters 5, 6, and 7 of this book. However, one must keep in mind that, as we have seen, these rates represent approximations that result from simplifications of the kinetic mechanisms. We typically use the approximate Michaelis-Menten-type flux expressions rather than the full system of equations in simulations for several reasons. First, often the quasi-steady rate constants (such as Ks and K in Equation (4.38)) are available from experimental data while the mass-action rate constants (k+i, k-i, etc.) are not. In fact, it is possible for different enzymes with different detailed mechanisms to yield the same Michaelis-Menten rate expression, as we shall see below. Second, in metabolic reaction networks (for example), reactions operate near steady state in vivo. Kinetic transitions from one in vivo steady state to another may not involve the sort of extreme shifts in enzyme binding that have been illustrated in Figure 4.7. Therefore the quasi-steady approximation (or equivalently the approximation of rapid enzyme turnover) tends to be reasonable for the simulation of in vivo systems. 

Figure 10.11. Regulation of cellular manganese concentration as a function of pMn in Thalassiosira oceanica. Uptake follows Michaelis-Menten kinetics. With decreasing [Mn " ], increases in at constant K, allow cells to maintain relatively constant cellular Mn concentrations. (Data from Sunda and Huntsman, 1985.)...

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