Research
The human microbiome: Towards a mechanicistc understanding of microbial communities
As humans we are not alone, inside our body we host trillions of microbes that compose our microbiome. The microbiome has a significant impact on human health. For instance, the bacteria contribute to the digestion of food, they train the immune system and prevent pathogen colonisation.
Over the last two decades, the scientific community has been able to successfully identify associations between specific bacterial strains and host health. However, the precise mechanisms that maintain health in humans are still poorly understood. In other words, we know which bacteria are associated with a healthy individual but we do not know why they decrease the chances of us getting sick.
Our group works on understanding how bacteria grow and survive, how they interact with each other and with the environment. In particular, by studying how bacteria interact over time and space at the single-cell level, our goal is to determine the principles and mechanisms linking specific bacterial communities and human health. Our results aim to increase prevention and treatment of diseases such as diabetes mellitus, inflammatory bowel disease, atherosclerosis, alcoholic liver disease, and cancer.
Over the last two decades, the scientific community has been able to successfully identify associations between specific bacterial strains and host health. However, the precise mechanisms that maintain health in humans are still poorly understood. In other words, we know which bacteria are associated with a healthy individual but we do not know why they decrease the chances of us getting sick.
Our group works on understanding how bacteria grow and survive, how they interact with each other and with the environment. In particular, by studying how bacteria interact over time and space at the single-cell level, our goal is to determine the principles and mechanisms linking specific bacterial communities and human health. Our results aim to increase prevention and treatment of diseases such as diabetes mellitus, inflammatory bowel disease, atherosclerosis, alcoholic liver disease, and cancer.
Complex systems and the role of individual cell behaviors
From a fundamental point of view, we study how a system composed by different bacterial cells works together. We are interested in studying how bacteria self-organise in space and time, how they interact and with which other cells they interact. Can we predict the community dynamics? Can we identify new emergent functions and properties that could not be understood by studying the individual cells alone?
A fascinating aspect of bacterial cells is that they are intrinsically stochastic and even genetically identical individuals exposed to the same environmental conditions behave differently. We are interested in understanding the conditions under which this phenotypic heterogeneity is promoted in communities of interacting types. Understanding in which conditions minorities of cells (not easily detectable) drive the community dynamics allows us to identify early instances of pathogenic invasion.
A fascinating aspect of bacterial cells is that they are intrinsically stochastic and even genetically identical individuals exposed to the same environmental conditions behave differently. We are interested in understanding the conditions under which this phenotypic heterogeneity is promoted in communities of interacting types. Understanding in which conditions minorities of cells (not easily detectable) drive the community dynamics allows us to identify early instances of pathogenic invasion.
Our approach
Our strength lies in the interdisciplinary approach to science. In particular we are experts in microfluidic techniques that allow for imaging of individual bacterial cells that grow and divide in spatially structured communities over long period of times. We use and develop software for image analysis (segmentation and tracking of individual cells) based on machine learning algorithms. We complement experiments with mathematical models and stochastic simulations. We are experts in information theory and Bayesian inference.
Other research interests:
Linking cell biology and ecology. Bacteria often live within large communities composed of thousands of different cells of either identical or different genotypes. Ecology historically has focused on describing and understanding the dynamics of these large genotypes within communities. On the other hand, cell biology has mainly focused on single cell behaviour. However, little is known about whether and how single cell behavior and cell-cell interactions shape the community dynamics. Using a bottom-up approach, I designed a synthetic microbial consortium composed of two Escherichia coli auxotrophic strains. These strains do not grow on their own when amino acids are not provided to them. However, they can grow together by sharing amino acids. I use high resolution microscopy and microfluidics devices to measure growth properties of single cells and communities, simultaneously. Furthermore, I plan to expose cells to fluctuating environments and quantify the contribution of single cells to the overall growth. I combine these datasets with theoretical modelling in order to provide unique insights and general principles about how single cell properties scale up in shaping community behaviours (mainly with Martin Ackermann, Alma Dal Co and Alyson Hockenberry).
Cell division control. Bacteria, such as Escherichia coli, grow exponentially, double in size and divide symmetrically into two cells. This division cycle is affected by biological noise. How cells deal with noisy fluctuations is extremely relevant for cell physiology. For instance, in homogeneous nutrient conditions Escherichia coli cells maintain a stable distribution of cell volume despite noisy growth rates and inter-division times. This is indicative of a coupling between cell size and growth, i.e. a control mechanism. Size control seems to be a shared feature among bacteria, yeast and archaea. I work on theoretical models which recapitulate recent single cell observations and use them to explain the coupling with few relevant parameters (mainly with Marco Cosentino Lagomarsino, Jacopo Grilli, and Matteo Osella).
Interactions across kingdoms. Bacteria not only interact with other bacteria but also with bacteriophages and fungi. Little is known about how these interactions shape the dynamics of a microbial community when it is exposed to antibiotic treatments. Our research aims to build model communities to understand how the interactions across kingdoms may affect antibiotic treatments.
Cell division control. Bacteria, such as Escherichia coli, grow exponentially, double in size and divide symmetrically into two cells. This division cycle is affected by biological noise. How cells deal with noisy fluctuations is extremely relevant for cell physiology. For instance, in homogeneous nutrient conditions Escherichia coli cells maintain a stable distribution of cell volume despite noisy growth rates and inter-division times. This is indicative of a coupling between cell size and growth, i.e. a control mechanism. Size control seems to be a shared feature among bacteria, yeast and archaea. I work on theoretical models which recapitulate recent single cell observations and use them to explain the coupling with few relevant parameters (mainly with Marco Cosentino Lagomarsino, Jacopo Grilli, and Matteo Osella).
Interactions across kingdoms. Bacteria not only interact with other bacteria but also with bacteriophages and fungi. Little is known about how these interactions shape the dynamics of a microbial community when it is exposed to antibiotic treatments. Our research aims to build model communities to understand how the interactions across kingdoms may affect antibiotic treatments.
