Accurate prediction of gene feedback circuit behavior from component properties

A basic assumption underlying synthetic biology is that analysis of genetic circuit elements, such as regulatory proteins and promoters, can be used to understand and predict the behavior of circuits containing those elements. To test this assumption, we used time‐lapse fluorescence microscopy to quantitatively analyze two autoregulatory negative feedback circuits. By measuring the gene regulation functions of the corresponding repressor–promoter interactions, we accurately predicted the expression level of the autoregulatory feedback loops, in molecular units. This demonstration that quantitative characterization of regulatory elements can predict the behavior of genetic circuits supports a fundamental requirement of synthetic biology

Traversal time for electron tunneling in water

The traversal time for tunneling is a measure of the time during which the transmitted particle can be affected by interactions localized in the barrier. The Buttiker-Landauer approach, which estimates this time by imposing an internal clock on the system, has been applied so far for relatively simple 1-dimensional models. Here we apply this approach to estimate the traversal time for electron tunneling through a realistic 3-dimensional model of a water layer. Observed structure in the energy dependence of times computed reflects the existence of transient tunneling resonances associated with instantaneous water structures.Comment: 9 pages, 3 figures. Submitted to the Journal of Chemical Physic

Oscillations and variability in the p53 system

Understanding the dynamics and variability of protein circuitry requires accurate measurements in living cells as well as theoretical models. To address this, we employed one of the best-studied protein circuits in human cells, the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We measured the dynamics of fluorescently tagged p53 and Mdm2 over several days in individual living cells. We found that isogenic cells in the same environment behaved in highly variable ways following DNA-damaging gamma irradiation: some cells showed undamped oscillations for at least 3 days (more than 10 peaks). The amplitude of the oscillations was much more variable than the period. Sister cells continued to oscillate in a correlated way after cell division, but lost correlation after about 11 h on average. Other cells showed low-frequency fluctuations that did not resemble oscillations. We also analyzed different families of mathematical models of the system, including a novel checkpoint mechanism. The models point to the possible source of the variability in the oscillations: low-frequency noise in protein production rates, rather than noise in other parameters such as degradation rates. This study provides a view of the extensive variability of the behavior of a protein circuit in living human cells, both from cell to cell and in the same cell over time

Monitoring Attention in ADHD with an Easy-to-Use Electrophysiological Index

Attention deficit hyperactivity disorder (ADHD) involves characteristic electroencephalographic (EEG) activity. We developed a single-channel EEG marker for attention: the Brain Engagement Index (BEI’). In this study, we evaluated the use of BEI’ for distinguishing between ADHD patients and controls, and for monitoring the effect of pharmacological treatment on ADHD patients. The BEI’ values of 20 ADHD patients and 10 controls were measured using a 1-min auditory oddball paradigm and a continuous performance test (CPT) task. We showed that CPT BEI’ is trait-specific and separates controls from ADHD patients. At the same time, oddball BEI’ is state-specific and identifies differences in attention level within the two groups of ADHD participants and controls. The oddball BEI’ also associates with response to treatment, after distinguishing between treatment effect and learning/time effect. The combined use of this marker with common computerized tests holds promise for research and clinical use in ADHD. Further work is required to confirm the results of the present study

RL Workshop

This project includes materials from a reinforcement learning seminar that took place at August 2020. It contains slides, tutorials, and videos. Please contact us for further information