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  1. Cell state transitions are often triggered by large changes in the concentrations of transcription factors and therefore large differences in their stoichiometric ratios. Whether cells can elicit transitions using modest changes in the ratios of co-expressed factors is unclear. Here we investigate how cells in the Drosophila eye resolve state transitions by quantifying the expression dynamics of the ETS transcription factors Pnt and Yan. Eye progenitor cells maintain a relatively constant ratio of Pnt/Yan protein despite expressing both proteins with pulsatile dynamics. A rapid and sustained two-fold increase in the Pnt/Yan ratio accompanies transitions to photoreceptor fates. Genetic perturbations that modestly disrupt the Pnt/Yan ratio produce fate transition defects consistent with the hypothesis that transitions are normally driven by a two-fold shift in the ratio. A biophysical model based on cooperative Yan-DNA binding coupled with non-cooperative Pnt-DNA binding illustrates how two-fold ratio changes could generate ultrasensitive changes in target gene transcription to drive fate transitions. Thus, coupling cell state transitions to the Pnt/Yan ratio sensitizes the system to modest fold-changes, conferring robustness and ultrasensitivity to the developmental program.

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  2. Abstract

    The RNA programmed non-specific (trans) nuclease activity of CRISPR-Cas Type V and VI systems has opened a new era in the field of nucleic acid-based detection. Here, we report on the enhancement of trans-cleavage activity of Cas12a enzymes using hairpin DNA sequences as FRET-based reporters. We discover faster rate of trans-cleavage activity of Cas12a due to its improved affinity (Km) for hairpin DNA structures, and provide mechanistic insights of our findings through Molecular Dynamics simulations. Using hairpin DNA probes we significantly enhance FRET-based signal transduction compared to the widely used linear single stranded DNA reporters. Our signal transduction enables faster detection of clinically relevant double stranded DNA targets with improved sensitivity and specificity either in the presence or in the absence of an upstream pre-amplification step.

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  3. Abstract Motivation

    Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown.


    We identify and systematically evaluate determinants of performance—including network properties, experimental design choices and data processing—by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets.

    Availability and implementation

    Code is available at

    Supplementary information

    Supplementary data are available at Bioinformatics online.

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