Xinying Ren

Summary: Biocircuit behavior is usually discovered by drawing uncertain kinetic parameters and simulating, but the scored phenotypes are functions of steady-state species, catalytic fluxes, dominance relations, and local response matrices. We introduce flux–species sampling (FSS), which samples that functional layer directly and algebraically inverts to parameters. This coordinate change turns regulatory modes into simple dominance chambers, gives every full-dimensional behavior-realizing regime positive measure, and can replace the sampling stage of existing pipelines without changing downstream stability or phenotype tests. A sequestration toy shows how a large species regime can collapse into a narrow parameter strip. In an enzymatic feedback motif, FSS rescues free- A * adaptation missed by parameter sampling and identifies the regime as non-Michaelian and complex-dominated. In MultiFate- N , FSS separates a sampling limitation from a structural limitation and rescues parameter sets realizing all 2 ^N states. Highlights: Flux–species sampling (FSS) draws the control/function layer where biocircuit behavior is read FSS boxes intersect every full-dimensional behavior-realizing regime with positive measure FSS is no slower than parameter-space sampling and is a hot fix for existing pipelines FSS rescues non-Michaelian free- A * adaptation missed by parameter sampling FSS rescues the all-2 ^N symmetric MultiFate regime missed by fixed parameter boxes eTOC blurb: This study reframes biocircuit exploration by sampling steady-state species and fluxes rather than raw parameters. The resulting flux–species coordinate exposes regulatory dominance regimes, rescues adaptation and multistability regimes missed by parameter sampling, and gives finite sampling nulls an explicit scope.

Negative feedback is a well-known mechanism for attenuating noise and enhancing robustness in biological systems. When coupled with cell-cell signaling, it provides a strategy for achieving population-level control in multicellular systems. While cell-cell signaling alone tends to reduce cell-to-cell variability by averaging fluctuations across a population, its interplay with negative feedback can produce contrasting effects on noise regulation at the single-cell and population levels. Therefore, to design population-level controllers that achieve robust behaviors with attenuated noise, a systematic understanding of how different noise sources impact gene expression at each level becomes a critical challenge. Here, we investigate noise regulation in a quorum sensing-based negative feedback system, focusing on two extrinsic noise sources: process noise from target gene dynamics and measurement noise from quorum sensing dynamics. Our results reveal that signal-based negative feedback significantly reduces process noise at the population level, especially dynamic noise. However, at the single-cell level, it enhances variability, leading to increased noise levels under conditions of faster signal diffusion and larger population sizes. In contrast, measurement noise is consistently attenuated at both single-cell and population levels through the combined effect of cell-cell signaling and negative feedback, under conditions of faster diffusion and larger population sizes.

Negative feedback is a well-known mechanism for attenuating noise and enhancing robustness in biological systems. When coupled with cell-cell signaling, it provides a strategy for achieving population-level control in multicellular systems. While cell-cell signaling alone tends to reduce cell-to-cell variability by averaging fluctuations across cells, its interplay with negative feedback can produce contrasting effects on noise regulation at single-cell and population levels. Therefore, to design population-level controllers that achieve robust behaviors with attenuated noise, a systematically understanding of how different noise sources impact cell gene expressions at these levels become a critical challenge. Here, we investigate noise regulation in a quorum sensing-based negative feedback system, focusing on two extrinsic noise sources: process noise from target gene dynamics and measurement noise from quorum sensing dynamics. Our results reveal that signal-based negative feedback significantly reduces process noise at the population level, especially for dynamic noise. However, at the single-cell level, it enhances variability, with increased noise levels under faster signal diffusion and higher population density. In contrast, measurement noise is consistently attenuated at both single-cell and population levels through the coupled cell-cell signaling and negative feedback, under conditions of faster diffusion and higher density.

Chromosomal coexpression domains are found in a number of different genomes under various developmental conditions. The size of these domains and the number of genes they contain vary. Here, we define local coexpression domains as adjacent genes where all possible pair-wise correlations of expression data are higher than 0.7. In rice, such local coexpression domains range from predominantly two genes, up to 4, and make up ∼5% of the genomic neighboring genes, when examining different expression platforms from the public domain. The genes in local coexpression domains do not fall in the same ontology category significantly more than neighboring genes that are not coexpressed. Duplication, orientation or the distance between the genes does not solely explain coexpression. The regulation of coexpression is therefore thought to be regulated at the level of chromatin structure. The characteristics of the local coexpression domains in rice are strikingly similar to such domains in the Arabidopsis genome. Yet, no microsynteny between local coexpression domains in Arabidopsis and rice could be identified. Although the rice genome is not yet as extensively annotated as the Arabidopsis genome, the lack of conservation of local coexpression domains may indicate that such domains have not played a major role in the evolution of genome structure or in genome conservation.

Background: White Spot Syndrome Virus, a member of the virus family Nimaviridae, is a large dsDNA virus infecting shrimp and other crustacean species. Although limited information is available on the mode of transcription, previous data suggest that WSSV gene expression occurs in a coordinated and cascaded fashion. To search in silico for conserved promoter motifs (i) the abundance of all 4 through 8 nucleotide motifs in the upstream sequences of WSSV genes relative to the complete genome was determined, and (ii) a MEME search was performed in the upstream sequences of either early or late WSSV genes, as assigned by microarray analysis. Both methods were validated by alignments of empirically determined 5' ends of various WSSV mRNAs. Results: The collective information shows that the upstream region of early WSSV genes, containing a TATA box and an initiator, is similar to Drosophila RNA polymerase II core promoter sequences, suggesting utilization of the cellular transcription machinery for generating early transcripts. The alignment of the 5' ends of known well-established late genes, including all major structural protein genes, identified a degenerate motif (ATNAC) which could be involved in WSSV late transcription. For these genes, only one contained a functional TATA box. However, almost half of the WSSV late genes, as previously assigned by microarray analysis, did contain a TATA box in their upstream region. Conclusion: The data may suggest the presence of two separate classes of late WSSV genes, one exploiting the cellular RNA polymerase II system for mRNA synthesis and the other generating messengers by a new virus-induced transcription mechanism.