Biochemical reaction networks, cellular

Although the importance of a systemic perspective on metabolism has only recently attained widespread attention, a formal frameworks for systemic analysis has already been developed since the late 1960s. Biochemical Systems Theory (BST), put forward by Savageau and others [142, 144 147], seeks to provide a unified framework for the analysis of cellular reaction networks. Predating Metabolic Control Analysis, BST emphasizes three main aspects in the analysis of metabolism [319] (i) the importance of the interconnections, rather than the components, for cellular function (ii) the nonlinearity of biochemical rate equations (iii) the need for a unified mathematical treatment. Similar to MCA, the achievements associated with BST would warrant a more elaborate treatment, here we will focus on BST solely as a tool for the approximation and numerical simulation of complex biochemical reaction networks. 

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. 

The intricate network of biochemical reactions occurring within the restricted volume of the liver cell requires subtly controlled metabolic regulatory mechanisms. (28) These occur on four levels (7.) at the molecular level, (2.) within the organelles, (3.) at the cellular level, and (4.) at the organ level. 

Elaborate models have been developed to account for the behavior of cellular biochemical networks. Boolean network models use a set of logical rules to illustrate the progress of the network reactions [10]. These models do not take into explicit account the participation of specific biochemical reactions. Models that account for the details of biochemical reactions have been proposed [11,12]. The behavior of these models depends on the rate constants of the chemical reactions and the concentrations of the reactants. Measurements like those described below of reaction fluxes and reactant concentrations will be able to test such network models. In the following sections, we will use simple examples to illustrate the characteristic steady-state behavior and propose an approach to measure fluxes and concentrations. 

The life of a cell is maintained by the continuous activity of a myriad of biochemical reactions that provide metabolic energy, synthesize (and degrade) structural and functional molecules such as proteins, nucleic acids and lipids, and drive cellular dynamic functions such as contraction, locomotion, and cytokinesis. These reactions are organized into networks or modules that have specific functions such as protein synthesis or production of energy by oxidative phosphorylation. Then, when a cell is in a stable state, these reaction networks must also operate stably i.e., energy is continuously generated and... 

Conservation of solvent capacity. Localization of the multitudinous biochemical reactions in microcompartments expedites the extensive network of metabolic processes with minimal taxation of the carrying capacity of the aqueous cytosol. Although subtle in its interpretation, this factor may have played an important role in the early evolution of cellular metabolism (Atkinson, 1977). There is a naive tendency—whether it be biochemists viewing the dumping of metabolic products into the cell s aqueous bulk or... 

The manifestation of adverse effects in biological systems is the result of perturbations of normal function or homeostasis, which involves a complex network of biochemical reactions that have evolved to maximize efficiency at the cellular, organ, tissue and whole organism level. These reactions are governed by the fundamental chemical principles that are part of typical chemistry curricula. Therefore, it is a natural extension to disclose opportunities to demonstrate the relationship between structure and toxicity. 

Genetic circuits must integrate specific components or network motifs that make them robust to fluctuations in the kinetics of biochemical reactions. Gene expression tends to be noisy because of the stochastic nature of the constituent biochemical reactions (Elowitz et al., 2002 McAdams and Arkin, 1997). In addition, fluctuations in environmental conditions, such as temperature and nutrient levels, affect cellular metabolism and consequently the operation of genetic circuits. Circuits that achieve reproducible, reliable behavior must do so despite components whose behavior fluctuates considerably. Mitigating the effects of gene expression noise will probably require a solution that incorporates positive and negative feedback loops. 

The perception of flavor is a fine balance between the sensory input of both desirable and undesirable flavors. It involves a complex series of biochemical and physiological reactions that occur at the cellular and subcellular level (see Chapters 1-3). Final sensory perception or response to the food is regulated by the action and interaction of flavor compounds and their products on two neur networks, the olfactory and gustatory systems or the smell and taste systems, respectively (Figure 1). The major food flavor components involved in the initiation and transduction of the flavor response are the food s lipids, carbohydrates, and proteins, as well as their reaction products. Since proteins and peptides of meat constitute the major chemical components of muscle foods, they will be the major focus of discussion in this chapter. 

Protein function can be described on three levels. Phenotypic function describes the effects of a protein on the entire organism. For example, the loss of the protein may lead to slower growth of the organism, an altered development pattern, or even death. Cellular function is a description of the network of interactions engaged in by a protein at the cellular level. Interactions with other proteins in the cell can help define the lands of metabolic processes in which the protein participates. Finally, molecular function refers to the precise biochemical activity of a protein, including details such as the reactions an enzyme catalyzes or the ligands a receptor binds. 

In a linear chemical reaction system, there is a unique steady state determined by the chemical constraints that establish the NESS. For nonlinear reactions, however, there can be multiple steady states [6]. A network comprised of many nonlinear reactions can have many steady states consistent with a given set of chemical constraints. This fact leads to the suggestion that a specific stable cellular phenotypic state can result from a specific NESS in which the steady operation of metabolic reactions maintains a balance of cellular components and products with the expenditure of biochemical energy [4]. Similarly, the network of chemical and mechanical signals that regulate the metabolic network must also be in a steady state. Important problems, then, are to determine the variety of steady states available to a system under a given set of chemical constraints and the mechanisms by which cells undergo... 

Finally, Fd like to stress the dynamic aspects which are of utmost importance for an understanding of biochemical processes. Biochemical processes are essentially open in nature. This means that all enzymes are constantly, stochastically or periodically activated by substrates which are produced by the cellular environment or by precursor enzymes and transformed so that products are picked up by other enzymes within a reaction sequence. Biochemical processes are controlled by their cyclic design, by allosteric feedback and by electrochemical coupling. The discovery of the principle of cyclic processes induced the notion that the dynamic coupling of networks of integrated biochemical processes must be extremely complex and nonlinear. Indeed, today we observe a large variety of coupled dynamic states and time pattern formations in biochemical processes from simple periodic reactions to the most complex chaotic states of biochemical turnover. 

The addition of new biochemical pathways, or the modification of existing pathways, is likely to affect the rest of the cellular metabolism. The new or altered pathways may compete with other reactions for intermediates or cofactors. To precisely predict the impact of the manipulation of a metabolic network is virtually impossible since it would require a perfect model of all enzyme kinetics and of the control of gene expression. Flowever, attempts have been made to develop modeling techniques to predict the behavior of altered organisms [29]. 

Fig. 2. Distribution of Metabolic Pathways according to the Cellular Processes. Neglecting the Transport Reactions, the Lipid Metabolism shows the highest no. of intracellular reactions signifying the abundance of the lipid biochemical networks.

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