Kinetic analysis complex biological systems

Finally, neither the effect of external noise, which affects nonequilibrium transitions in chemical and biological systems (Lefever, 1981 Horsthemke Lefever, 1984 Lefever Turner, 1986), nor the stochastic aspects of these transitions (Nicolis, Baras Malek-Mansour, 1984) are considered - with the exception of the glycolytic system (chapter 2). Such a simplification, justified in the first approximation by the absence of systematic noise in the biological systems considered, permits us to avoid complicating from the outset the analysis of systems whose kinetics is already complex. 

Some tours deforce of these methods have been presented in several publications, (see [6,7] and references therein). The studies of Tyson and coworkers are focused on the kinetic analysis of the budding yeast cell cycle. The molecular mechanism of cell cycle control is known in more detail for budding yeast, Saccharomyces cerevisiae, than for any other eukaryotic organism. Many experiments have been done on this system over many years there are about 125 references cited in [6]. The biological details are second to stressing the enormity of this task. The model has nearly twenty variables and that many kinetic equations, and there are about fifty parameters (rate coefficients, binding constants, thresholds, relative efficiencies). A fair number of assumptions need to be made in the cases of absence of any substantiating experimental evidence, and a fair number of approximations need to be made to simplify the kinetic equations. The complexity of this system is indicated in fig. 13.3 and its caption. 

From the point of view of kinetic analysis these are all problems in the resolution of systems of consecutive reactions, similar to several encountered above. The spediic lessons to be learned from the way different biological systems have been successfully investigated are that it is important to observe each phenomenon in terms of a range of parameters and that each of them illustrates additional monitors which help to overcome the, apparently insurmountable, complexities of the problem. While much emphasis is put on the elucidation of the number of identifiable steps on a reaction pathway, sometimes it is of importance to demonstrate that a pair of events are tightly coupled (concomitant), as in the example of force development and the change in configuration of myosin heads. 

While these calculations provide information about the ultimate equilibrium conditions, redox reactions are often slow on human time scales, and sometimes even on geological time scales. Furthermore, the reactions in natural systems are complex and may be catalyzed or inhibited by the solids or trace constituents present. There is a dearth of information on the kinetics of redox reactions in such systems, but it is clear that many chemical species commonly found in environmental samples would not be present if equilibrium were attained. Furthermore, the conditions at equilibrium depend on the concentration of other species in the system, many of which are difficult or impossible to determine analytically. Morgan and Stone (1985) reviewed the kinetics of many environmentally important reactions and pointed out that determination of whether an equilibrium model is appropriate in a given situation depends on the relative time constants of the chemical reactions of interest and the physical processes governing the movement of material through the system. This point is discussed in some detail in Section 15.3.8. In the absence of detailed information with which to evaluate these time constants, chemical analysis for metals in each of their oxidation states, rather than equilibrium calculations, must be conducted to evaluate the current state of a system and the biological or geochemical importance of the metals it contains. 

In order to understand these complex metabolic interactions more fully and to maximize the information obtained in these studies, we developed a detailed kinetic model of zinc metabolism(, ). Modeling of the kinetic data obtained from measurements of biological tracers by compartmental analysis allows derivation of information related not only to the transient dynamic patterns of tracer movements through the system, but also information about the steady state patterns of native zinc. This approach provides data for absorption, absorption rates, transfer rates between compartments, zinc masses in the total body and individual compartments and minimum daily requirements. Data may be collected without disrupting the normal living patterns of the subjects and the difficulties and inconveniences of metabolic wards can be avoided. 

---