Network biochemical

The dopamine system constitutes the cellular and biochemical network that is involved in the synthesis, release, and response to dopamine. In general, this involves cells that express significant levels of tyrosine hydroxylase (TH) and limited amounts of dopamine (3-hydioxylase [1]. Dopamine-responsive cells express receptors specifically activated by this neurotransmitter, which are known as dopamine Dl, D2, D3, D4, and D5 receptors [2, 3]. 

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Reactive MPC dynamics should prove most useful when fluctuations in spatially distributed reactive systems are important, as in biochemical networks in the cell, or in situations where fluctuating reactions are coupled to fluid flow. 

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Similar to generalized mass-action models, lin-log kinetics provide a concise description of biochemical networks and are amenable to an analytic solution, albeit without sacrificing the interpretability of parameters. Note that lin-log kinetics are already written in term of a reference state v° and S°. To obtain an approximate kinetic model, it is thus sometimes suggested to choose the reference elasticities according to simple heuristic principles [85, 89]. For example, Visser et al. [85] report acceptable result also for the power-law formalism when setting the elasticities (kinetic orders) equal to the stoichiometric coefficients and fitting the values for allosteric effectors to experimental data. 

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Table 1.2 Models of Biochemical Networks Based on the Cyclic Enzyme System... 

To connect several basic systems into a biochemical network and examine the performance of various networks as a function of the connectivity between the basic systems and their operational parameters. To this end, analytical models for each network type will be developed. 

To reveal the similarities and differences between the biochemical networks developed in this study and artihcial neural networks described in the literature. 

The basic system considered in this study relies on well-dehned enzymic reactions and is designed to function as a node or biochemical neuron in biochemical networks. This system involves two enzyme-catalyzed reactions, coupled to one another by the use of a cofactor, the latter being cycled continuously between the two. In addition, the two consumable substrates are fed into the system continuously at predetermined concentrations and rates. Also considered in this work was an extension of the basic system termed the extended basic system. The extended system relies on the same reactions as those in the basic system in addition, an external compound, inhibitory to one of the enzymes, is fed into the system. 

In the second stage of the research, a higher level of organization of the biosystems was considered. To this aim, the basic system presented above was used to construct biochemical networks. This was achieved by connecting a number of basic systems according to the principles of neural networks. This part of the research allowed us to delineate the rules for connecting the basic systems into functional biochemical networks and to study the type of information processing that can be achieved in a defined network. 

Neural networks are systems built of basic, mutually interacting elements, called neurons. The two key features of a neural network model that are of interest to us here are the properties of each neuron and the connectivity between neurons. In this section the construction of biochemical networks... 

Table 4.5 Numerical Values of Operational Parameters Used in Simnlations of Biochemical Networks...

The networks considered in this study are of three main types (identified as A, B, and C), differing from one another by the mode of connection between the participating biochemical neurons (see Table 5.1). For each network considered, an analytical model was written describing the performance of the network in kinetic terms. As the first stage in this program, analytical models were developed for the case when the reactions of the biochemical networks take place in fed-batch reactors. It is envisaged that these models will be extended to packed bed reactors in the future. 

COMPARING ARTIFICIAL NEURAL NETWORKS WITH BIOCHEMICAL NETWORKS... 

To learn the characteristic properties of the biochemical systems considered in this study and to assess their ability to perform as ANNs, a direct comparison between the two is made here. In so doing it should be noted that there is no universally accepted definition of an artificial neural network. Therefore, we refer here to the characteristics of ANNs summarized from some of the definitions available in the literature [17-22]. The next step is to examine if the characteristics mentioned above can also be found in the biochemical networks proposed in this study. These characteristics are compared one by one in Table 5.2. 

Table 5.2 Comparison Between Artificial Neural Networks and Biochemical Networks...

The entire biochemical network can be seen as the hardware component and does not need attached software in order to function. 

The biochemical network is built of a number of processing elements (i.e., the biochemical neurons). These are the enzymic basic systems. The term elementary is not an absolute one. However, the processing based on a few enzymic reactions is less complex than the processing of electrical signals as achieved by natural nerve cells. 

In the biochemical network each biochemical neuron works only with the substrates required for the specific reactions involved and is not affected by the reactions that take place in other neurons, unless they share a particular component. 

In this study we showed that the biochemical networks function according to the mode of connection between the basic systems (e.g., network A, B, or C), and also according to the processing performed at each neuron (i.e., reaction mechanism or kinetic constants). For the biochemical systems, the strengths of connection between basic elements (i.e., synaptic weights) is represented by the concentration of the component that is shared between the neurons. 

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