Biosimulation experiments

To reveal this by conventional experiments or trials is almost impossible. However, a biosimulation model, or a set of models, can describe even very complex, interacting pathways and control systems quantitatively and give an estimate of the effect. Moreover, the models can indicate where and how a given metabolic disorder best can be corrected. 

The biosimulation models need not to be large and comprehensive. Large models are difficult to validate, because small changes in the model structure can lead to different outcomes. The models in the following sections are all of the type which could be called virtual experiments because the setup resembles an experimental setup. The advantage is that the biosimulation models easily can analyze hundreds of what if situations within a second or two. 

The development of new medicines is both lengthy and expensive. Many experiments and trials are necessary before the medicine reaches its final form. Integrating biosimulation models in the development can speed up the development process considerably and save much expense. This is instrumental for the development of individualized medicines, because otherwise the sales would be too small to cover the development costs. 

Biosimulation has a dominant role to play in systems biology. In this chapter, we briefly outline two approaches to systems biology and the role that mathematical models has to play in them. Our focus is on kinetic models, and silicon cell models in particular. Silicon cell models are kinetic models that are firmly based on experiment. They allow for a test of our knowledge and identify gaps and the discovery of unanticipated behavior of molecular mechanisms. These models are very complicated to analyze because of the high level of molecular-mechanistic detail included in them. To facilitate their analysis and understanding of their behavior, model reduction is an important tool for the analysis of silicon cell models. We present balanced truncation as one method to perform model reduction and apply it to a silicon cell model of glycolysis in Saccharomyces cerevisiae. 

Reduction, Replacement, and Refinement alternatives are also important in human research and the Three Rs should be considered as a principle guiding this area too. The possibilities of extending the principle of the Three Rs to human experiments are presently being evaluated as part of the objectives of BioSim - an EU Network of Excellence in Biosimulation [15]. 

Biosimulation holds great promise for the future in solving key ethical issues relating to drug development and drug use. Biosimulation allows for a better planning of experiments, a more rational exploitation of the information acquired in each test, and a better prediction of the effect of new medicines before administering them in animals and humans and may contribute to ... 

The experiment set was applied to each virtual patient individually to identify potential phenotypic variability in these patients responses to pathway modulation. The goals of these biosimulations were to predict the clinical impact of modulating... 

In silico analysis, while necessary, is not sufficient to validate a presumptive target. It is necessary that one finalize this process by examining, in vitro and/or in vivo, the assumptions underlying the hypotheses captured in the biosimulation mns. The obvious experiments were recommended based on the in silico predictions captured in table 14.3. Since the PhysioLab platform is transparent and can illustrate the physiological effects and conditions underlying these hypothetical functions, a research plan was specifically designed that described, in detail, the laboratory experiments required to confirm the hypotheses developed above. 

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