Constructing the Simulated Cell
- Turbine’s Simulated Cell is 10x the size of the largest published signaling network usable for dynamic simulations. It currently incorporates 1300+ proteins and other second messenger molecules and their over 3300 interactions. The network incorporates 38 important signaling pathways acting in cancerous cells, including MAPK, ErbB, Notch and Hedgehog, describing all hallmarks of cancer.
- The network itself is a directed and weighted signaling network, where the vast majority of the links represent direct protein interactions and nodes represent unique protein isoforms.
- Every node and interaction undergoes rigorous manual curation. The functional behavior of the Simulated Cell was tested on dozens of well-described mechanisms, which the cell displayed without error.
Customizing the Simulated Cell to cancer type and patient
- The genomic and transcriptomic characteristics of cancer cells vary widely not just among patients, but often inside the same tumor as well. Turbine uses available OMICS data to customize the cell model to a cell line or patient sample, enabling simulations to closely mimic the actual, in vitro or in vivo interactions.
- Genetic mutations, either congenital or acquired, play a key role both in the development and the propagation of cancer. Both gain and loss of function mutations and their unique characteristics are taken into account.
- The Simulated Cell also incorporates transcriptomic information from any given human tissue or cell line to make predictions even more accurate. The given sample’s expression profiles are compared to transcriptomic datasets from a wide variety of healthy tissues. This identifies their relative abundance or scarcity in the given cell type, which is then applied to the Simulated Cell.
Compiling the simulation’s drug library
- In simulated trials where cancer cells' reaction to a drug or a combination of drugs is relevant, the available drugs and their targets, with relative affinity (if available) are mapped to the Simulated Cell’s nodes.
- A drug library can be compiled from an in-house compound set, approved treatments for any disease, or from other available public sources. Turbine doesn’t require the structure of the given compound, only the protein target profiles and relative affinity of each protein to the compound based on earlier bioactivity data from experiments or chemoinformatic modeling.
- It’s important to note that Turbine’s artificial intelligence is not limited to already available treatments or molecules, and can identify novel drug targets or target combinations if simulations show that new compounds targeting these could boost tumor response.
Modeling biological activity with the Turbine simulator
- The customized cancer cell model is brought to life by Turbine’s dynamic network simulator. The simulator models the behavior of the cell, by biologically accurately representing how signals propagate in the cell’s protein network. In addition to signaling pathway activity, Turbine's cell comes alive through representations of detailed molecular behavior, gene expression layers and phenotypic changes all integrated in one system. Turbine’s simulator incorporates interactions resulting in both translational and post-translational modifications.
- The simulator is unique because it combines the detailed simulation of cellular activities, the resulting readouts of phenotypic attractor states, and complex layers of molecular characteristics. This sheds light on the actual molecular processes of cancer cells.
- Turbine's artificial intelligence finds the defining molecular events to help understand the reasons behind the phenotypic behavior of cancer cells. The AI creates a timeline of key events on the molecular level and forecasts points of cellular evolution. Armed with this knowledge, it makes predictions about therapy design, therapy response, biomarker identification, and acquired therapy resistance.