Our lab aims to develop a data-driven integrative in-silico framework to capture the inter-tissue crosstalk and time dynamics molecular interactions between different omics data associated with progressive cardiometabolic-associated diseases (CMD). Our goal is to create an effective decision-making tool for systematically defining personalized and efficient treatment options with translational value.
Systemic diseases, including CMD, are often progressive and multifactorial, requiring different biomarkers at each stage for early detection. Treating them effectively often involves polypharmacy (drug combinations) or polypharmacology (multi-targeted drugs). On top of that, patient heterogeneity further complicates treatment. Current data-driven approaches tend to overlook the time dynamics of disease progression and fail to capture its full complexity, limiting their clinical relevance. Incorporating those allows for the discovery of time-specific biomarkers and synergistic drug target combinations to treat different factors within the disease.
Our lab focuses on CMD, progressive polygenic and multifactorial disorders characterized by, among others, obesity, insulin resistance, dyslipidemia, and hypertension. CMD can also be associated with the aging process. Additionally, CMD affects multiple tissues, including the liver, adipose tissue, skeletal muscle, heart, and gut microbiome. When untreated, CMD often progresses to severe diseases such as type 2 diabetes, cardiovascular disease, and chronic kidney failure. Its complexity and variability among patients make it difficult to detect and treat early. We aim to develop and apply systems and network biology approaches to improve the understanding of CMD and its systemic impact, leading to better early detection and personalized treatment strategies.
Group members
Amanda Olmin – Postdoctoral Researcher
Liam Davidsson – Associate Researcher
Hiring postdocs, PhD and master thesis students. Please reach out.