The first step in inference is prediction of sensitivity val ues for target com

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 The first step in inference is prediction of sensitivity val ues for target com Empty The first step in inference is prediction of sensitivity val ues for target com

Post  huwan123456 on Thu Apr 03, 2014 7:28 am

We are currently experimenting with pharma ceutical drug library consisting of more than 300 small molecule inhibitors. We expect that the use of larger number of drugs will increase the accuracy further and generate maps with greater robustness. The scope of the present article is concentrated around steps B, C and D of Figure 1. For future research, we will consider multiple data sources to increase ARN-509 溶解度 the robustness of the designed maps. As explained in Figure 1, we can use RAPID siRNA screens to validate single points of failures predicted by our TIM approach. Furthermore, RNAseq and protein phosphoarray data can be used to further revise the cir cuit. Finally, time series data can be used to incorporate dynamics in the modeling framework.

For combination AUY922 溶解度 therapy design, we can use the TIM framework to formu late control strategies with various constraints. Some pos sibilities are minimal toxicity, anticipating evolving drug resistance, and success over a family of TIMs representing variations of a tumor. For case, we can assume that the toxicity of a drug or drug combination is proportional to the number of targets being inhibited by the drug and search for the drug combination with high sensitivity but low set of target inhibitions. For case, we would want to avoid resistance and thus would like to inhibit more than one independent blocking path way such that for the scenario when resistance to one of the blocking pathways develops, the other independent pathway can still keep the tumor under check.

In other words, we would be ATP-competitive ALK 阻害剤 interested in selecting a set of tar gets that can be divided into two or more non intersecting sets such that the sensitivity of each set is higher than a threshold. For case, the goal is to design control policies for the scenario when the exact pathway is not known but it belongs to a collection of pathways. The uncertainty can arise when the experimental data is not sufficient enough to produce a unique pathway map or the current pathway may evolve into one of the different path ways obtained from tissues with same type of cancer. This can approached from a worst case perspective or a Bayesian perspective, In conclusion, the proposed framework provides a unique input output based methodology to model a can cer pathway and predict the effectiveness of targeted drugs. This framework can be developed as a viable approach for personalized cancer therapy.

To aide in the usage of our framework, we have developed a Graphical User Interface which implements in an easy to use way the algorithms and equations presented in this paper. It is built in MATLAB, but is distributed as a compiled exe cutable; as such, it is usable in a Windows environment by downloading the MATLAB Compile Runtime Environment, which is free to download and requires no MATLAB installation. It is available online under the Tar get Inhibition Map approach to inference of cancer path ways heading. Glioblastoma is the most common primary central nervous system tumor and accounts for approximately 40% of all primary malignant brain tumors. GB is a het erogeneous group of tumors associated with a very poor clinical prognosis.


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