Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific -associated pathways.

Intercellular adhesion molecule 1 (ICAM-1) is a critical molecule responsible for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1, that SARS-CoV-2 shares several features with these viruses via interactions between cells, and that SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. From these previous arguments, it is assumed that ICAM-1 can be related to SARS-CoV-2 cell-to-cell transmission in COVID-19 patients.

Incorporating Time Delays in Process Hitting Framework for Dynamical Modeling of Large Biological Regulatory Networks.

Modeling and simulation of molecular systems helps in understanding the behavioral mechanism of biological regulation. Time delays in production and degradation of expressions are important parameters in biological regulation. Constraints on time delays provide insight into the dynamical behavior of a Biological Regulatory Network (BRN).

ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks.

Background: This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,.

Analyzing resilience properties in oscillatory biological systems using parametric model checking.

Automated verification of living organism models allows us to gain previously unknown knowledge about underlying biological processes. In this paper we show how parametric time model checking can be applied to define the time behavior of biological oscillatory systems more precisely. In particular, we focus on the resilience properties of such systems.

Learning delayed influences of biological systems.

Boolean networks are widely used model to represent gene interactions and global dynamical behavior of gene regulatory networks. To understand the memory effect involved in some interactions between biological components, it is necessary to include delayed influences in the model. In this paper, we present a logical method to learn such models from sequences of gene expression data.