Understanding cancer phenomena using a thermodynamic-based approach

We seek to address fundamental questions in cancer biology by an experimental-theoretical approach based on physicochemical laws. We have recently pioneered the application of the thermodynamic-based surprisal analysis, which has been previously applied to systems in chemistry and physics, to biological processes. We have shown that through the accurate resolution of the protein networks that deviate the cancer system from its balanced state, various biological phenotypes can be predicted. For example, we have demonstrated that using a thermodynamic-based proteomic analysis in varying cell-cell distances, the direction of movement of brain cancer cells can be predicted and experimentally manipulated. Here we present single cell and bulk proteomic methods integrated with thermodynamic-derived information theory. We demonstrate how complex biological phenomena, such as cellular tumor architectures or inter-tumor variability can be modeled using a limited number of key physical parameters. Furthermore we show how these parameters are used to predict cellular architectures or to design high-precision, patient-specific drug cocktails. Generally speaking, this approach provides a framework that models biological systems in order to learn how to predict and manipulate their behavior