Our research is driven by fundamental problems in biology and (bio)medicine, with a focus on building quantitative, predictive models. We incorporate multi-scale modelling and simulations, artificial intelligence (AI), bioengineering, and both micro- and molecular biology tools to investigate (1) host-microbiota interactions in complex human diseases, (2) microbial multi-drug resistance, and (3) gene expression and chromatin organization. In addition, we study (4) the role and effect of mechanical forces ('mechanicanobiology') and cell compartmentalization in pathogenesis .

In our work, we combine cutting edge modeling and simulation techniques with experimental design and analysis of experimental data. This data includes DNA/RNA/ATAC/ChIP-seq, metabolomics, proteomics, super-resolution microscopy and more. Many of the questions we address involve the integration of these data into fully calibrated models that have predictive power, revealing testable experiments in fundamental biology and translational research, primarily aimed at identifying effective biomarkers and viable precision therapeutics.

The Marquez-Lago lab collaborates with many research groups at UAB and around the world.

Brief intro to research lines

1. Systems Biology of human microbiota

We apply a systems biology approach to microbiota/microbiome research by integrating high-throughput approaches with network-based mathematical models and computational tools describing interactions between microorganisms and human hosts. Multi-scale modeling and analysis provides a meaningful avenue to conduct disease-specific predictive analysis, rational microbiome design and precision interventions. Our mechanistic and phenomenological models strive to predict novel dynamics, map phenotypes by integrating multi ‘omics’ and patient/disease-specific data, and to identify viable precision therapeutics.

2. Microbial multi-drug resistance (MDR)

According to estimates, every 10 minutes someone in the world dies from antibiotic resistant infections, and this frequency is projected to increase to one death every 3 seconds by 2050. Antibiotics in livestock and unnecessary prescription of antimicrobials contribute to growing resistance, compounded by empirical use of newest drugs first, quickly rendering them ineffective too. To better understand MDR acquisition and to identify possible intervention points, we are combining experimental and computational methods that allow us to study microbial evolution and mechanisms conferring resistance in space and time.

3. Mechanobiology in pathogenesis (incl. spatio-temporal modelling)

Despite intrinsic and extrinsic sources of stochasticity, spatial segregation and organization of molecules within cells enables activation and repression of transcriptional programs, precise signal transduction, and cell fate decisions. Two relevant mechanisms shown to be involved in cellular precision are mechanical forces and compartmentalization of cell membranes. We combine spatio-temporal modelling and simulation with cell biology and microscopy to incorporate (and correctly account for) dynamic compartmentalization and mechanobiology effects into predictive models of cellular functions relevant to development, health and disease.

4. Gene expression and chromatin organization

Genome function is intrinsically linked to chromatin architecture. However, the spatio-temporal organization of DNA inside cells is far from being uniform or randomly distributed: the genome is a complex biochemical system, folded and packaged into a (multi-layered, compact) dynamic 3D object that constantly remodels itself in response to e.g. mechanical forces and remodeling factors. Understanding the principles governing genome regulation directly translates to biomedical practice, but necessitates computational, physical and mathematical modeling approaches. To this end, we develop analysis tools and multi-scale models studying gene expression and chromatin organization in both space and time, incorporating microscopy, HTS and biochemistry data.

Methods: Simulations, AI and multi-omics in biomedicine

The identification of genes and proteins underlying human diseases became a top priority in the last decade. Nevertheless, genomics approaches alone are often insufficient to define RNA and proteins, or their dynamic roles in determining complex traits and disease. Consequently, integrative predictive tools such as dynamic models and simulations become essential. In support of our research goals, we develop analysis tools and predictive models facilitating integration and interpretation of ‘omics’ data, environmental and clinical data, aiding in silico predictive analysis for biomedical discovery. In addition, we collaborate with many labs in the development of novel analysis methods and bioinformatics toolkits, including AI methods such as machine learning and deep learning.