The gene regulation knowledge commons: the action area of GREEKC.

As computational modeling becomes more essential to analyze and understand biological regulatory mechanisms, governance of the many databases and knowledge bases that support this domain is crucial to guarantee reliability and interoperability of resources. To address this, the COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.

SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries.

Background: SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections.

Methods: Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries.

CausalBuilder: bringing the MI2CAST causal interaction annotation standard to the curator.

Molecular causal interactions are defined as regulatory connections between biological components. They are commonly retrieved from biological experiments and can be used for connecting biological molecules together to enable the building of regulatory computational models that represent biological systems. However, including a molecular causal interaction in a model requires assessing its relevance to that model, based on the detailed knowledge about the biomolecules, interaction type and biological context.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling.

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources.