Search

Synthetic biology is a promising tool to study the function and properties of gene regulatory networks. Gene circuits with predefined behaviours have been successfully built and modelled, but largely on a case-by-case basis. Here we go beyond individual networks and explore both computationally and synthetically the design space of possible dynamical mechanisms for 3-node stripe-forming networks. First, we computationally test every possible 3-node network for stripe formation in a morphogen gradient. We discover four different dynamical mechanisms to form a stripe and identify the minimal network of each group. Next, with the help of newly established engineering criteria we build these four networks synthetically and show that they indeed operate with four fundamentally distinct mechanisms. Finally, this close match between theory and experiment allows us to infer and subsequently build a 2-node network that represents the archetype of the explored design space.

James Cotterell1,21Center for Genomic Regulation, Barcelona, Spain; 2Garvan Institute for Medical Research, Sydney, NSW, AustraliaAbstract: Pancreatic cancer is the fourth biggest killer, and has one of the worst prognoses, of any cancer type. Approximately 95% of patients diagnosed with pancreatic cancer will not survive beyond 5 years post diagnosis, and these statistics have barely improved in over 40 years. Here, genomic changes in one particular patient with stage IV metastatic pancreatic cancer were explored to suggest a potential personalized treatment. In particular, exome sequencing of genomic DNA extracted from blood and the cancer biopsy was utilized with the aim of identifying mutational drivers of the cancer. This analysis revealed a splice site mutation in RBCK1 as the most promising driver of the cancer and a therapy based on a pan-cyclin-dependent kinase (pan-CDK) inhibitor, flavopiridol. This study suggests that drugs whose effectiveness is unclear for general populations of cancer sufferers should possibly be reconsidered for specific patients where the drug could be rationally argued to improve outcome.Keyword: personalized medicine, driver mutation identification, next generation sequencing