Renewable Syngas Generation via Low-Temperature Electrolysis: Opportunities and Challenges.

The production of syngas (i.e., a mixture of CO and H) via the electrochemical reduction of CO and water can contribute to the green transition of various industrial sectors.

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can be challenging due to a lack of dedicated comparative genomic tools and viral and phage databases, which are often outdated.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.

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.

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.

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.