VAPEX: an interactive web server for the deep exploration of natural virus and phage genomes.

MOTIVATION: Studying the genetic makeup of viruses and phages through genome analysis is crucial for comprehending their function in causing diseases, progressing medicine, tracing their evolutionary history, monitoring the environment, and creating innovative biotechnologies. However, accessing the necessary data can be challenging due to a lack of dedicated comparative genomic tools and viral and phage databases, which are often outdated. Moreover, many wet bench experimentalists may not have the computational proficiency required to manipulate large amounts of genomic data. RESULTS: We have developed VAPEX (Virus And Phage EXplorer), a web server which is supported by a database and features a user-friendly web interface. This tool enables users to easily perform various genomic analysis queries on all natural viruses and phages that have been fully sequenced and are listed in the NCBI compendium. VAPEX therefore excels in producing visual depictions of fully resolved synteny maps, which is one of its key strengths. VAPEX has the ability to exhibit a vast array of orthologous gene classes simultaneously through the use of symbolic representation. Additionally, VAPEX can fully analyze user-submitted viral and phage genomes, including those that have not yet been annotated. AVAILABILITY AND IMPLEMENTATION: VAPEX can be accessed from all current web browsers such as Chrome, Firefox, Edge, Safari, and Opera. VAPEX is freely accessible at https://archaea.i2bc.paris-saclay.fr/vapex/.

BAGET 2.0: an updated web tool for the effortless retrieval of prokaryotic gene context and sequence.

MOTIVATION: The retrieval of a single gene sequence and context from completely sequenced bacterial and archaeal genomes constitutes an intimidating task for the wet bench biologist. Existing web-based genome browsers are either too complex for routine use or only provide a subset of the available prokaryotic genomes. RESULTS: We have developed BAGET 2.0 (Bacterial and Archaeal Gene Exploration Tool), an updated web service granting access in just three mouse clicks to the sequence and synteny of any gene from completely sequenced bacteria and archaea. User-provided annotated genomes can be processed as well. BAGET 2.0 relies on a local database updated on a daily basis. AVAILABILITY AND IMPLEMENTATION: BAGET 2.0 befits all current browsers such as Chrome, Firefox, Edge, Opera and Safari. Internet Explorer 11 is supported. BAGET 2.0 is freely accessible at https://archaea.i2bc.paris-saclay.fr/baget/.

Noise Induces the Population-Level Entrainment of Incoherent, Uncoupled Intracellular Oscillators.

Intracellular oscillators entrain to periodic signals by adjusting their phase and frequency. However, the low copy numbers of key molecular players make the dynamics of these oscillators intrinsically noisy, disrupting their oscillatory activity and entrainment response. Here, we use a combination of computational methods and experimental observations to reveal a functional distinction between the entrainment of individual oscillators (e.g., inside cells) and the entrainment of populations of oscillators (e.g., across tissues). We demonstrate that, in the presence of intracellular noise, weak periodic cues robustly entrain the population averaged response, even while individual oscillators remain un-entrained. We mathematically elucidate this phenomenon, which we call stochastic population entrainment, and show that it naturally arises due to interactions between intrinsic noise and nonlinear oscillatory dynamics. Our findings suggest that robust tissue-level oscillations can be achieved by a simple mechanism that utilizes intrinsic biochemical noise, even in the absence of biochemical couplings between cells.

Adaptive hybrid simulations for multiscale stochastic reaction networks.

The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.