Monday, May 3, 2021

A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19


SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels.

Checkout the Blue Brain Portal for details on how the movie was made:


Thursday, April 22, 2021

Blue Brain BioExplorer

I have just released the Blue Brain BioExplorer, a tool for scientists to extract and analyse scientific data from visualization. BBBE is built on top of Blue Brain Brayns, the Blue Brain rendering platform.




The BBBE application is built on top of Brayns, the Blue Brain rendering platform. The role of the application is to use the underlying technical capabilities of the rendering platform to create large scale and accurate 3D scenes from Jupyter notebooks.

General components


Assemblies are groups of biological elements, such as proteins, membranes, glycans, etc. As an example, a virion is made of a lipid membrane, spikes proteins, an RNA sequence, etc, and all those elements belong to the same object. That’s why they need to belong to the same container, the assembly. Assemblies can have different shapes: Sphere, Cube, etc, that are automatically generated according to the parameters of individual components.


Proteins are loaded from PDB files. Atoms, non-polymer chemicals and bonds can be loaded and displayed in various colour schemes: chain id, atom, residue, etc. Proteins also contain the amino acid sequences of the individual chains. Sequences that can be used to query glycosylation sites, or functional regions of the protein.


Glycans are small proteins that are attached to an existing protein of the assembly. Individual glycan trees are loaded from PDB files and attached to the glycosylation sites of the specified protein. By default, glycans are attached to all available glycosylation sites, but a set of specific sites can be specified.

RNA sequence

An RNA sequence can be loaded from a text sequence of codons. Various shapes can be selected to represent the RNA sequence: Trefoil knot, torus, star, etc. This allows the sequence to be efficiently packed into a given volume. A different color is assigned per type of codon.

Mesh-based membranes

Mesh-based membranes create membranes based on 3D meshes. This allows the construction of complex membranes where mesh faces are filled with proteins.

Python SDK

A simple API if exposed via the BBBE python library. The API allows scientists to easily create and modify assemblies, according the biological parameters. The BBBE programming language is not necessarily reflecting the underlying implementation, but is meant to be as simple as close as possible to the language used by the scientists to describe biological assemblies.


See here for detailed documentation of the source code.


BBBE binaries are publicaly available as docker images. BBE is designed to run in distributed mode, and is composed of 3 modules: A server, a python SDK, and a web user interface. This means that there are 3 docker images to be downloaded on run. Those images can of course run on different machines.

In this example, we will expose the server on port 5000, the python SDK jupyter notebooks on port 5001, and the user inferface on port 5002. One is free to change those ports at will.


docker run -ti --rm -p 5000:8200 bluebrain/bioexplorer

Python SDK

docker run -ti --rm -p 5001:8888 bluebrain/bioexplorer-python-sdk

Web User Interface

docker run -ti --rm -p 5002:8080 bluebrain/bioexplorer-ui