Impulse model-based differential expression analysis of time course sequencing data

The global gene expression trajectories of cellular systems in response to developmental or environmental stimuli often follow the prototypic single-pulse or state-transition patterns which can be modeled with the impulse model. Here we combine the continuous impulse expression model with a sequencing data noise model in ImpulseDE2, a differential expression algorithm for time course sequencing experiments such as RNA-seq, ATAC-seq and ChIP-seq. We show that ImpulseDE2 outperforms currently used differential expression algorithms on data sets with sufficiently many sampled time points. ImpulseDE2 is capable of differentiating between transiently and monotonously changing expression trajectories. This classification separates genes which are responsible for the initial and final cell state phenotypes from genes which drive or are driven by the cell state transition and identifies down-regulation of oxidative-phosphorylation as a molecular signature which can drive human embryonic stem cell differentiation.

How to control the coherent oscillations in Landau–Zener–Stueckelberg dynamics of three-level system

Coherent pulse is used to control and measure the quantum state of three-level system in double quantum dots. We analyze the coherent oscillations in Landau–Zener–Stueckelberg (LZS) dynamics by the adiabatic-impulse model, which simplifies the applied pulse as an optical interference device. Under the designed “hat-shape” pulse, the sweeping speed through each avoid crossing can be tuned independently. The coherent oscillations in LZS dynamics of three-level system are optimized by the control pulse. Moreover, we can filter out the coherent oscillations with the unexpected frequency and only keep the coherent oscillations with the frequency we are interested in.