Systems, Synthetic, and Computational Bioengineering
Research groups in systems, synthetic, and computational bioengineering apply engineering principles to model and understand complex biological systems, including differentiation and development, pathogenesis and cancer, and learning and behavior. This involves designing and implementing methods for procuring quantitative and sometimes very large data sets, as well as developing theoretical models and computational tools for interpreting these data.
Deciphering the workings of a biological system allows us to identify potential biomarkers and drug targets, to develop protocols for personalized medicine, and more. In addition, we use the design principles of biological systems we discover to engineer and refine new synthetic biological systems for clinical, agricultural, environmental, and energy applications.
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AI and multi modal data for improving disease care, Cancer research, Cardiovascular disease research, Artificial intelligence including deep learning and machine learning for healthcare, natural language processing (NLP) and image processing for healthcare, recommender systems for healthcare
Systems and synthetic biology of the Brain-Immune-Gut super-system; Interactions among hosts and microbes; Deep learning approaches to interpreting biological data and designing biomedical solutions
h.levine@northeastern.edu
Physical modeling of cancer progression, metastasis and interaction with the immune system. Most recent interests include the role of metabolic plasticity in these processes and the co-evolution of the tumor and the adaptive immune system. Other areas include spatial organization of the actin cytoskeleton, the mechanics of collective cell motility, and the analysis of genetic circuits involved in cell fate decisions.
Synthetic biology, microbiology, biosensor development
Computational systems biology, an integration of mathematical modeling and bioinformatics for studying gene regulatory networks, single cell genomics, epithelial-mesenchymal transition, coarse-graining, reverse engineering, machine learning, stochasticity and heterogeneity in gene expression
m.minkara@northeastern.edu
Pulmonary surfactant (PS), Surfactant immunoproteins, Innate immune response, Computational methods, Computational modeling, Molecular Dynamics, Monte Carlo Simulations, Interfacial Phenomena, Biological interfaces, Therapeutics, Molecular Biophysics, Computational chemistry, Bioengineering, STEM Accessibility
n.slavov@northeastern.edu
Single-cell proteomics, immunology, cancer drug resistance, translational regulation, quantitative systems biology, mass spectrometry, RNA decoding, protein synthesis and degradation, macrophage polarization, neurodegenerative diseases
Feedback control theory, systems biology, cancer, and biomedicine
Systems biology of signaling networks in cancer (and) therapeutics, metastasis and embryonic development
Computational modeling of the cardiac myocyte to understand the molecular basis of arrhythmias; machine learning in critical care medicine to identify those patients who require urgent care