Our group focuses on developing experimental-computational approaches for the analysis of large data sets from global measurement technologies and their integration. Below we highlight some of the problems we have worked on and some of our ongoing work.
Systems Chemical Biology
It is well known that nutrition is the cornerstone of an individual’s environment , as such understanding how diet influences metabolic regulation and how dietary interventions can improve health is a key scientific goal. Furthermore, diet has a major influence on the overall quality of life beyond the prevention of diseases and its role is fundamental for individual performance and enjoyment. Even though the personalized approach to diet is the logical next step – much like the transition from pharmacology to personalized medicine – this task is extraordinarily complicated. Most foods are composed of hundreds of bioactive compounds, often interacting with each other. Furthermore, the targets are of varied concentrations and different targets have different affinities and specificities. Unfortunately, nutritional trials are not designed for mechanism-based preclinical studies, and little is known about their molecular targets. Our group integrates text mining, chemoinformatics and network biology for performing global analyses of diet that elucidates the synergistic interactions of small molecules that yield specific phenotypes and hopefully contribute towards personalized nutrition.
The development of next-generation sequencing that enables obtaining thousands to millions of reads per run at affordable for the scientific community costs, has revolutionized the field of medical microbiology. By assessing much deeper layers of microbial communities researchers were able to explore in detail both “who’s there?” and “what are they doing?” and develop models that describe the interplay of hosts, commensal microbes and diseases. Since tools and statistical methodologies are becoming faster and more specialized for complex microbial communities we expect that soon metagenomics will allow a full characterization of the community that will subsequently shift the focus from descriptive to mechanistic modeling of the host-microbiome interactome. The primary goals of my group are to: (i) create spatially and temporally resolved maps of the microbial world of the human, built and natural environment, (ii) develop a roadmap for discovering how microbes travel between different parts in the body and between various environments we come into contact every day, and (iii) harness this knowledge to explain the rise of diseases in urbanized parts of the world.
Yeast Synthetic Biology
The application of genome-scale technologies, both experimental and in silico, to industrial biotechnology has allowed improving the conversion of biomass-derived feedstocks to chemicals, materials and fuels through microbial fermentation. In particular, due to rapidly decreasing costs and its suitability for identifying the genetic determinants of a phenotypic trait of interest, whole genome sequencing is expected to be one of the major driving forces in industrial biotechnology in the coming years. Our group applies high-throughput sequencing technologies for finding the underlying molecular mechanisms for (a) improved carbon source utilization, (b) increased product formation, and (c) stress tolerance. We use Saccharomyces cerevisiae as a cell factory of added- and high added- value molecules and we integrate –omic data for mapping industrially relevant genotype-to-phenotype links including exploiting natural diversity in natural isolates or crosses between isolates, classical mutagenesis and evolutionary engineering.
Disease Systems Biology
Advances in medical research revealed that a disease phenotype is the result of pathobiological processes that interact in complex networks. The group works on computational data integration approaches where clinical and phenotypic data generated at the Li Ka Shing Faculty of Medicine are combined with molecular level data, predominantly exome and transcriptome information. One important aim is to identify the genetic contributors to human variation in drug efficacy, which is an important aspect in personalized medicine. By longitudinal exome analyses of the timor heterogeneity we can significantly reduce the number of hits for functional validation. Another focus is to apply RNA sequencing of human tissues and network biology to study the complexity of the biochemical networks at multiple scales for understanding the development of metabolic diseases, and specifically diseases linked to energy storage.