Immune digital twins for complex human pathologies: applications, limitations, and challenges.

Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins.

WikiPathways 2024: next generation pathway database.

WikiPathways (wikipathways.org) is an open-source biological pathway database. Collaboration and open science are pivotal to the success of WikiPathways.

SyBLaRS: A web service for laying out, rendering and mining biological maps in SBGN, SBML and more.

Visualization is a key recurring requirement for effective analysis of relational data. Biology is no exception. It is imperative to annotate and render biological models in standard, widely accepted formats.

fCoSE: A Fast Compound Graph Layout Algorithm with Constraint Support.

Visual analysis of relational information is vital in most real-life analytics applications. Automatic layout is a key requirement for effective visual display of such information. This article introduces a new layout algorithm named fCoSE for compound graphs showing varying levels of groupings or abstractions with support for user-specified placement constraints.

Newt: a comprehensive web-based tool for viewing, constructing and analyzing biological maps.

Motivation: Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries.

Results: We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF.

Efficient methods and readily customizable libraries for managing complexity of large networks.

Background: One common problem in visualizing real-life networks, including biological pathways, is the large size of these networks. Often times, users find themselves facing slow, non-scaling operations due to network size, if not a "hairball" network, hindering effective analysis. One extremely useful method for reducing complexity of large networks is the use of hierarchical clustering and nesting, and applying expand-collapse operations on demand during analysis.