Genetic programs can be compressed and autonomously decompressed in live cells.

Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes and genetically encoded circuits in live cells . Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity of two bits per nucleotide . In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum .

Model-guided combinatorial optimization of complex synthetic gene networks.

Constructing gene circuits that satisfy quantitative performance criteria has been a long-standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three-gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range.

Digital switching in a biosensor circuit via programmable timing of gene availability.

Transient delivery of gene circuits is required in many potential applications of synthetic biology, yet the pre-steady-state processes that dominate this delivery route pose major challenges for robust circuit deployment. Here we show that site-specific recombinases can rectify undesired effects by programmable timing of gene availability in multigene circuits. We exemplify the concept with a proportional sensor for endogenous microRNA (miRNA) and show a marked reduction in its ground state leakage due to desynchronization of the circuit's repressor components and their repression target.

Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer.

The recognition that colorectal cancer (CRC) is a heterogeneous disease in terms of clinical behaviour and response to therapy translates into an urgent need for robust molecular disease subclassifiers that can explain this heterogeneity beyond current parameters (MSI, KRAS, BRAF). Attempts to fill this gap are emerging. The Cancer Genome Atlas (TGCA) reported two main CRC groups, based on the incidence and spectrum of mutated genes, and another paper reported an EMT expression signature defined subgroup.