PMC:4331679 / 1537-3095 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331679","sourcedb":"PMC","sourceid":"4331679","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331679","text":"The objective of anti-cancer therapeutics is to kill cancer cells with minimum damage to the healthy cells. To this end, a solid understanding of the cell fate decisions (e.g. apoptosis, proliferation) of different cells under various conditions would be required. It is well known that signalling pathways play crucial roles in the regulation of cancer cell fate [1]. However, it is challenging to understand the dynamics of signal transduction at systems level, due to non-linearity of the network dynamics, e.g. feedback and crosstalk. In cancer cells, this becomes even more complicated due to various types of alterations (e.g. DNA mutations, genome rearrangement, epigenetic changes, and pathway alterations). These alterations allow cancer cells to adapt to new conditions and evolve drug resistance. Therefore, to find effective anti-cancer therapies, cancer-specific alterations in the signalling pathways must be taken into account. Moreover, it is desirable to understand how cancer cells respond to different combinations of drugs, and how drug sensitivity can be enhanced. Genomic and proteomic data of cellular responses to drugs, synchronized with cell fate observations, would shed light on cancer drug effects at systems level. However, even if sufficient data are available, it is challenging to construct a model of cell signalling to explain the data and make accurate predictions. As more \"omics\" data about cancer are available recently, computational methods for modelling and discovery of cancer cell fate are becoming more important.","tracks":[]}