Project description:As many as 59% of the transcription factors in Escherichia coli regulate the transcription rate of their own genes. This suggests that auto-regulation has one or more important functions. Here, one possible function is studied. Often the transcription rate of an auto-regulator is also controlled by additional transcription factors. In these cases, the way the expression of the auto-regulator responds to changes in the concentrations of the "input" regulators (the response function) is obviously affected by the auto-regulation. We suggest that, conversely, auto-regulation may be used to optimize this response function. To test this hypothesis, we use an evolutionary algorithm and a chemical-physical model of transcription regulation to design model cis-regulatory constructs with predefined response functions. In these simulations, auto-regulation can evolve if this provides a functional benefit. When selecting for a series of elementary response functions-Boolean logic gates and linear responses-the cis-regulatory regions resulting from the simulations indeed often exploit auto-regulation. Surprisingly, the resulting constructs use auto-activation rather than auto-repression. Several design principles show up repeatedly in the simulation results. They demonstrate how auto-activation can be used to generate sharp, switch-like activation and repression circuits and how linearly decreasing response functions can be obtained. Auto-repression, on the other hand, resulted only when a high response speed or a suppression of intrinsic noise was also selected for. The results suggest that, while auto-repression may primarily be valuable to improve the dynamical properties of regulatory circuits, auto-activation is likely to evolve even when selection acts on the shape of response function only.

| S-EPMC2883594 | biostudies-literature

Editorial: Alternative Splicing Regulation in Plants.

Project description: Not available

| S-EPMC7363971 | biostudies-literature

Editorial: Epigenetic Regulation in Cardiovascular Diseases.

Project description: Not available

| S-EPMC8787130 | biostudies-literature

Multidrug-Resistant Klebsiella pneumoniae Causing Severe Infections in the Neuro-ICU.

Project description:The purpose of this study was the identification of genetic lineages and antimicrobial resistance (AMR) and virulence genes in Klebsiella pneumoniae isolates associated with severe infections in the neuro-ICU. Susceptibility to antimicrobials was determined using the Vitek-2 instrument. AMR and virulence genes, sequence types (STs), and capsular types were identified by PCR. Whole-genome sequencing was conducted on the Illumina MiSeq platform. It was shown that K. pneumoniae isolates of ST14K2, ST23K57, ST39K23, ST76K23, ST86K2, ST218K57, ST219KL125/114, ST268K20, and ST2674K47 caused severe systemic infections, including ST14K2, ST39K23, and ST268K20 that were associated with fatal incomes. Moreover, eight isolates of ST395K2 and ST307KL102/149/155 were associated with manifestations of vasculitis and microcirculation disorders. Another 12 K. pneumoniae isolates of ST395K2,KL39, ST307KL102/149/155, and ST147K14/64 were collected from patients without severe systemic infections. Major isolates (n = 38) were XDR and MDR. Beta-lactamase genes were identified: blaSHV (n = 41), blaCTX-M (n = 28), blaTEM (n = 21), blaOXA-48 (n = 21), blaNDM (n = 1), and blaKPC (n = 1). The prevalent virulence genes were wabG (n = 41), fimH (n = 41), allS (n = 41), and uge (n = 34), and rarer, detected only in the genomes of the isolates causing severe systemic infections-rmpA (n = 8), kfu (n = 6), iroN (n = 5), and iroD (n = 5) indicating high potential of the isolates for hypervirulence.

| S-EPMC8389041 | biostudies-literature

Auto-Selection of an Optimal Sparse Matrix Format in the Neuro-Simulator ANNarchy.

Project description:Modern neuro-simulators provide efficient implementations of simulation kernels on various parallel hardware (multi-core CPUs, distributed CPUs, GPUs), thereby supporting the simulation of increasingly large and complex biologically realistic networks. However, the optimal configuration of the parallel hardware and computational kernels depends on the exact structure of the network to be simulated. For example, the computation time of rate-coded neural networks is generally limited by the available memory bandwidth, and consequently, the organization of the data in memory will strongly influence the performance for different connectivity matrices. We pinpoint the role of sparse matrix formats implemented in the neuro-simulator ANNarchy with respect to computation time. Rather than asking the user to identify the best data structures required for a given network and platform, such a decision could also be carried out by the neuro-simulator. However, it requires heuristics that need to be adapted over time for the available hardware. The present study investigates how machine learning methods can be used to identify appropriate implementations for a specific network. We employ an artificial neural network to develop a predictive model to help the developer select the optimal sparse matrix format. The model is first trained offline using a set of training examples on a particular hardware platform. The learned model can then predict the execution time of different matrix formats and decide on the best option for a specific network. Our experimental results show that using up to 3,000 examples of random network configurations (i.e., different population sizes as well as variable connectivity), our approach effectively selects the appropriate configuration, providing over 93% accuracy in predicting the suitable format on three different NVIDIA devices.

| S-EPMC9169689 | biostudies-literature

Guest editorial: From neuroscience to neuro-rehabilitation: transferring basic neuroscientific principles from laboratory to bedside.

Project description:Several new approaches for treatment of Central Nervous System (CNS) disorders are currently under investigation, including the use of rehabilitation training strategies, which are often combined with electrical and/or pharmacological modulation of spinal locomotor circuitries. While these approaches show great promise in the laboratory setting, there still exists a large gap in knowledge on how to transfer these treatments to daily clinical use. This thematic series presents a cross section of cutting edge approaches with the goal of transferring basic neuroscience principles from the laboratory to the proverbial "bedside".

| S-EPMC3565930 | biostudies-literature

Editorial: Bioinformatics of Genome Regulation, Volume II.

Project description: Not available

| S-EPMC8606529 | biostudies-literature

Editorial: Revisiting immunological roles for bone marrow stromal cell antigen-1; an entero-neuro-immune regulator.

Project description: Not available

| S-EPMC10335351 | biostudies-literature

Editorial: Regulation of Mitochondrial Function on Animal Diseases.

Project description: Not available

| S-EPMC9248764 | biostudies-literature