More About The Joao Xavier Lab Minus iconIcon indicating subtraction, or that the element can be closed. Plus IconIcon indicating addition, or that the element can be opened. Arrow (down) icon.An arrow icon, usually indicating that the containing element can be opened and closed.

Joao Xavier: Overview

Xavier Lab @ cBio — Visit the Xavier lab’s Web site at [More »]Cells and organisms cope with the task of maintaining their phenotypes in the face of numerous challenges. Much attention has recently been paid to questions of how cells control molecular processes to ensure robustness. However, many biological functions are multicellular and depend on interactions, both physical and chemical, between cells. How do multicellular behaviors emerge from interactions among individual cells? What makes multicellular systems robust to the many challenges that they face?

We use a combination of computational models and quantitative experiments to answer these questions. Our goal is to identify the underlying physical, biological, and evolutionary principles that are common among, and confer robustness to, multicellular systems.

We investigate how cell-cell interactions govern complex behavior in cell populations. We combine experimental and computational approaches and investigate three systems of biomedical relevance: 

  • The opportunistic pathogen Pseudomonas aeruginosa
  • The gut microbiome
  • The tumor microenvironment

Social interaction and evolution in pathogenic bacteria
Pathogenic bacteria can be very social. Bacterial cells interact by signaling each other chemically, exchanging metabolites, building biofilms and moving in swarms. However, the interactions can be vulnerable to cheating, which happens when individuals exploit the efforts of the collective without contributing. How do cooperative traits evolve in bacteria despite cheating? Can we exploit cheating to develop better therapies against bacterial pathogens? 

We use swarming in Pseudomonas aeruginosa (an opportunistic human pathogen) as a model social behavior. Drugs that target individual level traits of bacteria, such as antibiotics and other antimicrobials, create a strong selective pressure for resistance. Our growing understanding of social interaction opens the way to therapeutic strategies that do not select for resistance.

The gut microbiota
Our gut hosts bacteria in such high numbers that they outnumber our own cells by 10:1. The gut microbiota i helps digest nutrients and fight off invasion by pathogens. Yet, despite this central role for human health, the microbiota ecosystem remains poorly understood. Many of the bacterial species are unculturable which makes even a simple census difficult.

This is changing in recent years with the application of metagenomics and we can envision future diagnostics and therapeutics that restitute healthy balances in the microbiome. Before clinical translation of microbiome biology is possible we must seek to understand the ecological processes governing its composition dynamics and function thoroughly. 

We develop mathematical modeling and analytical methods inspired by mathematical ecology.

Cell-cell interactions in cancer
Tumors cells interact with cells in their microenvironment and those interactions are essential to cancer progression and invasiveness. Often, signaling and physical processes interact in complex ways. Mathematical formalism can make this complexity more tractable.

We adapt mathematical models that we developed for other systems such as bacterial biofilms and we investigate cancer interactions in spatially structured environments. This can lead to novel predictive tools for tumor progression and may eventual assist in the rational design of therapies.