An engineered genetic circuit for lactose intolerance alleviation

Background Lactose malabsorption occurs in around 68% of the world’s population, causing lactose intolerance (LI) symptoms, such as abdominal pain, bloating, and diarrhea. To alleviate LI, previous studies have mainly focused on strengthening intestinal β-galactosidase activity while neglecting the inconspicuous drop in the colon pH caused by the fermentation of non-hydrolyzed lactose by the gut microbes. A drop in colon pH will reduce the intestinal β-galactosidase activity and influence intestinal homeostasis. Results Here, we synthesized a tri-stable-switch circuit equipped with high β-galactosidase activity and pH rescue ability. This circuit can switch in functionality between the expression of β-galactosidase and expression of L-lactate dehydrogenase in response to an intestinal lactose signal and intestinal pH signal, respectively. We confirmed that the circuit functionality was efficient in bacterial cultures at a range of pH levels, and in preventing a drop in pH and β-galactosidase activity after lactose administration to mice. An impact of the circuit on gut microbiota composition was also indicated. Conclusions Due to its ability to flexibly adapt to environmental variation, in particular to stabilize colon pH and maintain β-galactosidase activity after lactose influx, the tri-stable-switch circuit can serve as a promising prototype for the relief of lactose intolerance.

Comprehensive Model for Evaluating the Performance of Mach-Zehnder-Based Silicon Photonic Switch Fabrics in Large Scale

Building a large-scale Mach-Zehnder-based silicon photonic switch circuit (LS-MZS) requires an appropriate choice of architecture. In this work, we propose, for the first time to our knowledge, a single metric that can be used to compare different topologies. We propose an accurate analytical model of the signal-to-crosstalk ratio (SCR) that highlights the performance limitations of the main building blocks: Mach-Zehnder interferometers (MZI) and waveguide crossings. It is based on the cumulative crosstalk and total insertion loss of the LS-MZS. Four different architectures: Beneš, dilated Beneš, switch and select, double-layer network were studied for the reason that they are mainly referenced in the literature. We compared them using our developed SCR indicator. With reference to the state-of-the-art technology, the analysis of the four architectures using SCR showed that, on a large scale, a high number of waveguide crossings significantly affects the performance of the switch matrix. Moreover, better performance was reached using the double-layer-network architecture. Then, we presented a 2 × 2 MZI using two electro-optic phase shifters and a waveguide crossing realized in LETI’s silicon photonics technology. Measured performances were quite good: the switch circuit had a crosstalk of −31.3 dB and an insertion loss estimated to be less than 1.31 dB.

Inferring a network from dynamical signals at its nodes

We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network.