Additionally, the convergence of the experimentally applied search algorithms depends on the appropriate selection of the fairly algorithms parameters. In the absence of a model to enable reasonable selection of the algo rithms parameters, convergence of these algorithms might be compromised. Furthermore, the experimentally applied algorithms can converge to an optima that is not very robust to small variations in the input signals. Other recent work on identifying drug combination focuses on identifying mixtures of drugs where the the search space is reduced to use only a single concentra tion while the search space is increased by searching through a larger number of potential drugs. The latter example describes an approach and applies it towards finding promising mixtures for lung cancer.

In this work, we achieve the desired goals through the integration of data driven mathematical tools with biological measurements to generate quantitative models of cellular functions. Instead of mapping phy sical interactions, the resulting model is a quantitative model mapping particular signals to their cellular pro cess responses. The responses represent the net change in certain cellular activities caused by signal interactions within a large and complex network. The model is gen erated using a suitable mathematical approximation method, which relies on testing a relatively small subset of all possible signal combinations and is capable of pre dicting the response to the complete set of signal com binations.

Through running in silico experiments, the model enables analyzing the response of the system to various combinations and determining or selecting sub sets of signal combinations that can yield desired cellu lar responses. The determination of these subsets can be achieved using tools such as stochastic search algo rithms and cluster analysis. The proposed approach will facilitate the understanding of fundamental cellular responses, which are system responses reflecting the activity of a complex signaling network controlled by multiple internal and external signals. This approach can promote efficient understanding of cellular func tions without intermediates. In addition, the approach allows multiple cell types or other biological systems to be quantitatively characterized, modeled, and compared in parallel. The maximal difference or similarity can be identified using a computational search.

It can facilitate the development of drug combination therapies for var ious types of cancers. In comparison Entinostat to the approaches previously men tioned, the approach introduced read this in this work overcomes their limitations by identification of the complete response function and carrying out the optimization in silico. The cost of carrying such in silico experiments is significantly less and is generally faster.