Aside from, reversine was also proved to block proliferation or to induce progr

A large number of testing samples were used ARQ 197 availability for each pathway prediction and the results indicate an average error of less than 10% for multiple scenarios. In comparison, the aver age error with random predictions was 44%. The average correlation coefficient of the prediction to actual sensi tivity for the 8 sets of experiments was 0. 91. The average correlation coefficient with random predictions was 0. We also report the standard deviation of the errors and for a representa tive example, the 10 percentile of the error was 0. 154 and 90 percentile 0. 051, thus the 80% prediction interval for prediction u was, The results of the synthetic experiments on different randomly generated pathways shows that the approach presented in the paper is able to utilize a small set of training drugs from all possible drugs to generate a high accuracy predictive model.<br><br> In this section, we discuss extensions of the TIM frame work presented earlier. We provide foundational work for incorporating sensitivity prediction via continuous valued analysis of EC50 values of new drugs as well as theoretical work concerning dynamical models generated from the steady state TIMs developed previously. Incorporating continuous target inhibition AZD0530 ic50 values The analysis considered in the earlier sections was based on discretized target inhibition i. e. each drug was denoted by a binary vector representing the targets inhibited by the drug. The framework can predict the sensitivities of new drugs with high accuracy as illustrated by the results on canine osteosarcoma tumor cultures.<br><br> However, the current framework can also be modified to incorporate the continuous AMN-107 641571-10-0 nature of target inhibition and application of different concentrations of a new drug. Let us con sider that a drug i with target set T0 and EC50 profile ei,1, ei,2, ei,n is applied at concentration x nM. For each EC50 value ei,j, we can fit a hill curve or a logistic func tion to estimate the inhibition of target j at concentration x nM. For instance a logistic function will estimate the drug target profiles for a combination of drugs at differ ent concentrations. To arrive at the sensitivity prediction for a new target inhibition profile, we can apply rules sim ilar to Rules 1, 2 and 3 along with searching for closest target inhibition profiles among the training data set.<br><br> The block analysis performed using discretized target inhi bitions can provide smaller sub networks to search for among the target inhibition profiles. Incorporating network dynamics in the TIM formulation The TIM developed in the previous sections is able to predict the steady state behavior of target inhibitor com binations but cannot provide us with the dynamics of the model or the directionality of the tumor pathways. This limitation is a result of the experimental drug perturbation data being from the steady state. Our results show that the proposed approach is highly successful in locating the primary faults in a tumor circuit and predict the possible sensitivity of target combinations at the current time point. However, exten sion of this model to incorporate the directional pathways will require protein or gene expression measurements. The extension refers to steps F1 and F2 in Figure 1.