Quantification of transcription noise's impact on cell fate commitment with digital resolutions.

Motivation: Gene transcription is a random and noisy process. Tremendous efforts in single-cell studies have been mapping transcription noises to phenotypic variabilities between isogenic cells. However, the exact role of the noise in cell fate commitment remains largely descriptive or even controversial.

Incompatible and sterile insect techniques combined eliminate mosquitoes.

The radiation-based sterile insect technique (SIT) has successfully suppressed field populations of several insect pest species, but its effect on mosquito vector control has been limited. The related incompatible insect technique (IIT)-which uses sterilization caused by the maternally inherited endosymbiotic bacteria Wolbachia-is a promising alternative, but can be undermined by accidental release of females infected with the same Wolbachia strain as the released males. Here we show that combining incompatible and sterile insect techniques (IIT-SIT) enables near elimination of field populations of the world's most invasive mosquito species, Aedes albopictus.

The nonlinear dynamics and fluctuations of mRNA levels in cell cycle coupled transcription.

Gene transcription is a noisy process, and cell division cycle is an important source of gene transcription noise. In this work, we develop a mathematical approach by coupling transcription kinetics with cell division cycles to delineate how they are combined to regulate transcription output and noise. In view of gene dosage, a cell cycle is divided into an early stage [Formula: see text] and a late stage [Formula: see text].

Linking dynamical complexities from activation signals to transcription responses.

The transcription of inducible genes involves signalling pathways that induce DNA binding of the downstream transcription factors to form functional promoter states. How the transcription dynamics is linked to the temporal variations of activation signals is far from being fully understood. In this work, we develop a mathematical model with multiple promoter states to address this question.

Use of age-stage structural models to seek optimal Wolbachia-infected male mosquito releases for mosquito-borne disease control.

Due to the lack of vaccines and effective clinical cures, current methods to control mosquito-borne viral diseases such as dengue and Zika are primarily targeting to eradicate the major mosquito vectors. However, traditional means, including larval source reduction and applications of insecticides etc, are not sufficient to keep vector population density below the epidemic risk threshold. An innovative and operational strategy is to release Wolbachia-infected male mosquitoes into wild areas to sterilize wild female mosquitoes by cytoplasmic incompatibility.

Wolbachia spread dynamics in multi-regimes of environmental conditions.

Mosquito-borne diseases such as dengue fever and Zika kill more than 700,000 people each year in the world. A novel strategy to control these diseases employs the bacterium Wolbachia whose infection in mosquitoes blocks virus replication. The prerequisite for this measure is to release Wolbachia -infected mosquitoes to replace wild population.

Wolbachia spreading dynamics in mosquitoes with imperfect maternal transmission.

Mosquitoes are primary vectors of life-threatening diseases such as dengue, malaria, and Zika. A new control method involves releasing mosquitoes carrying bacterium Wolbachia into the natural areas to infect wild mosquitoes and block disease transmission. In this work, we use differential equations to describe Wolbachia spreading dynamics, focusing on the poorly understood effect of imperfect maternal transmission.

Wolbachia spread dynamics in stochastic environments.

Dengue fever is a mosquito-borne viral disease with 100 million people infected annually. A novel strategy for dengue control uses the bacterium Wolbachia to invade dengue vector Aedes mosquitoes. As the impact of environmental heterogeneity on Wolbachia spread dynamics in natural areas has been rarely quantified, we develop a model of differential equations for which the environmental conditions switch randomly between two regimes.

The nonlinear dynamics and fluctuations of mRNA levels in cross-talking pathway activated transcription.

Gene transcription is a stochastic process, and is often activated by multiple signal transduction pathways. In this work, we study gene transcription activated randomly by two cross-talking pathways, with the messenger RNA (mRNA) molecules being produced in a simple birth and death process. We derive the analytical formulas for the mean and the second moment of mRNA copy numbers and characterize the nature of transcription noise.

Transcription dynamics of inducible genes modulated by negative regulations.

Gene transcription is a stochastic process in single cells, in which genes transit randomly between active and inactive states. Transcription of many inducible genes is also tightly regulated: It is often stimulated by extracellular signals, activated through signal transduction pathways and later repressed by negative regulations. In this work, we study the nonlinear dynamics of the mean transcription level of inducible genes modulated by the interplay of the intrinsic transcriptional randomness and the repression by negative regulations.

The mean and noise of protein numbers in stochastic gene expression.

Gene expression is the central process in cells, and is stochastic in nature. In this work, we study the mean expression level of, and the expression noise in, a population of isogenic cells, assuming that transcription is activated by two sequential exponential processes of rates κ and λ. We find that the mean expression level often displays oscillatory dynamics, whereas most other models suggest that it always grows monotonically.

Temporal profile of gene transcription noise modulated by cross-talking signal transduction pathways.

Gene transcription is a central cellular process and is stochastic in nature. The stochasticity has been studied in real cells and in theory, but often for the transcription activated by a single signaling pathway at steady-state. As transcription of many genes is involved with multiple pathways, we investigate how the transcription efficiency and noise is modulated by cross-talking pathways.

Modulation of gene transcription noise by competing transcription factors.

Sequence specific transcription factors (TFs) are critical to ensuring that genes are transcribed in the right cell at the right time. Often, the gene promoter is flanked by multiple binding sites, some of which can be bound by different types of TFs in the cell. To investigate how the transcription noise is modulated by the competition of these TFs at their shared binding sites, we model gene transcription as a renewal process where the time spent in each transcription cycle is assumed to be independently and identically distributed.

The mean frequency of transcriptional bursting and its variation in single cells.

The recent in vivo RNA detection technique has allowed real-time monitoring of gene transcription in individual living cells, revealing that genes can be transcribed randomly in a bursting fashion that short periods of rapid production of multiple transcripts are interspersed with relatively long periods of no production. In this work, we utilize the three state model to study how environmental signals and the intrinsic cellular contexts are combined to regulate stochastic gene transcription. We introduce a system of three master equations to model the stochastic occurrence of transcriptional bursting.

The mean and noise of stochastic gene transcription.

Gene transcription in single cells is inherently a probabilistic process. Even in a hypothetically homogeneous intracellular environment, the stochasticity of transcription would produce fluctuations in the number of transcripts, constituting the phenotypic heterogeneity in cell population. Noise, the variance normalized by the square of the mean, has typically been utilized to quantify the heterogeneity of transcript distribution.