If you're a #Student or #Postdoc excited about #Viruses, #Bioinformatics, or both—reach out!
We’re building something new at @ucl and there’s room at the table 👾
📩 DMs open
🔁 RTs appreciated

Our lab values:
✔️ #Inclusivity
✔️ #ScientificCuriosity
✔️ #Mentorship & #CareerDevelopment (whether in #academia, #industry, or elsewhere)
Every lab member will grow—and be supported in the process.

We approach these problems by studying how viral infections work at the molecular level using:
🔹 #StructuralBioinformatics
🔹 #MachineLearning
🔹 #NetworkBiology
… and we collaborate with experimentalists worldwide 🌍 #TogetherStronger

We focus on 3 main research areas:
1️⃣ Assessing the epidemic potential of newly discovered viruses
2️⃣ Predicting host susceptibility to infections
3️⃣ Designing personalized therapies and antivirals informed by host & viral genetics

The #LassoLab sits at the intersection of #ComputationalBiology and #Virology.
As viral spillover and #Outbreaks become more frequent due to #Globalization and #ClimateChange, we aim to develop tools to predict and prevent future #Pandemics.

Grateful for the constructive feedback from Cell Host & Microbe reviewers, which strengthened our paper. Their fair and thoughtful criticism was instrumental. 10/10

Huge thanks to our amazing coauthors—experts from viral ecology, molecular virology and computational biology across 9 countries. Special shoutouts to Michael Grodus, Estefania Valencia, @jangralab.bsky.social, @chandranlab.bsky.social, and Simon Anthony. #TogetherStronger 9/10

These findings provide actionable insights for targeted surveillance strategies, helping identify hosts of high-risk filoviruses. This can reduce spillover risks and enhance public health preparedness. 8/10

The model also applies to newly discovered filoviruses, bats in non-endemic regions and it can also be used to assess zoonotic potential—crucial for evaluating risks to human health. 7/10

Our model captures receptor-binding principles and predicts host susceptibility across filoviruses and bat species. By combining receptor binding with #biogeographic data, we identify bats acting as potential hosts for Ebola virus. 6/10

Our study provides the most extensive characterization of receptor binding in filoviruses to date, integrating experimental data with machine learning (#ML) to uncover molecular rules driving receptor binding. 5/10

The Niemann-Pick C1 (#NPC1) protein is a critical receptor for filoviruses, binding the #filovirus #glycoprotein (GP) to enable viral entry. Variations at the GP-NPC1 interface dictate viral #susceptibility and host range. 4/10

#Bats are the primary host suspects for #filoviruses, but the exact species involved remain poorly characterized. This knowledge gap hinders our ability to predict and prevent #spillover events. 3/10

#Filoviruses cause sporadic yet increasingly frequent outbreaks of fatal hemorrhagic fever in #humans. Monitoring host animals can help predict animal-to-human transmission and reduce the risk of future #outbreaks. 2/10