Chronic Myeloid Leukemia (CML) represents a paradigm for the wider Fosamprenavir cancer field. pathway or cellular process our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes suggest new combinatorial therapeutic targets and spotlight previously unexplored sensitivities to Interleukin-3. Malignancy is recognized as a highly complex aberrant cellular state where initiating mutations impact either directly or indirectly on a multitude of regulatory pathways. Chronic Myeloid Leukaemia (CML) represents a paradigm for malignancy both in terms of understanding the nature of the Rabbit Polyclonal to FGFR3. molecular lesion as well as the ability to develop targeted therapies. Whilst the development of targeted drugs has revolutionized the treatment of CML patients drug resistance is an inevitable consequence of this therapeutic approach. Hence devising strategies to delay or overcome drug resistance becomes a major challenge calling for systematic screening of multiple drug targets and their combinations. Traditionally biological and medical research has focused on the study of individual genes and proteins in isolation from other elements that comprise the entire system in which they interact and function. While this reductionist approach has been effective in elucidating specific characteristics of particular biological processes scientific discovery is progressively limited rather than guided by reductionist principles because the functionality of biomolecules critically depends on interactions with many other biomolecules1. Fosamprenavir Importantly innovations in high-throughput data generation and automation have set the scene for more integrative methods2. No less important than the generation of data describing biological functional associations is our ability to interpret this data. Mechanistic diagrams have been commonplace in biology but these static representations fail to capture variations in associations over time and the sheer scale of the systems represented often proves these to be too unwieldy. Modeling and especially computational modeling has thus Fosamprenavir become a powerful tool in this endeavor. While mathematical models can be simulated through translating mathematics to Fosamprenavir algorithms computational models are immediately executable allowing for larger-scale simulation of biological systems3. In addition analysis techniques common in computer science and formal verification can be directly applied to such models. One such technique model checking involves analyzing all possible executions of the model but without actually executing all these possibilities4. This analysis allows for rapid and thorough comparison of the computational Fosamprenavir model with experimental data; a cyclic process is thus able to be realized in which a draft model is composed model checking is applied the model is assessed to see if it fits with experimental data and a revised model is produced. Boolean networks pioneered by Kauffman as a model for genetic regulatory networks have already been used in interpretation of large data sets as well as for drug discovery5 6 7 In this formalism relationships are represented in a dynamic network with discrete time steps. Genes in this type of networks represented by nodes can have two states (hence a Boolean network) and edges are directed and may be activating or inhibitory. In this study we use the (QNs) generalization of Boolean Networks8 to model the gene regulatory network of CML. CML has been extensively mathematically modeled on a cell population level but not at the level of a genetic network9 10 11 CML represents an ideal model for the genetic study of malignancy since it is linked to a consistent molecular event the translocation between chromosomes 9 and 22 which gives rise to the so-called Philadelphia chromosome expressing the oncogenic fusion protein Bcr-Abl. If untreated CML has a well-defined and mostly-uniform progression from the relatively manageable chronic phase (CP) to its terminal blast crisis (BC) phase12. In this work we first integrated the current body of knowledge on the molecular pathways involved in CML into a gene regulatory network via manual inspection of the relevant literature. We then constructed a Qualitative Network executable model of CML progression using the BMA tool (freely available at http://biomodelanalyzer.research.microsoft.com/) based on the CML network curated from the literature. The analysis of our CML network-model had generated novel.