Engineering biophysical signals promises non-pharmacologic clinical interventions to improve cell anabolism and direct tissue regeneration in conditions that devastate bone such as cancer metastasis, chemotherapy, sedentary-lifestyle, microgravity, injury, and bedrest. There is however an unmet need for establishing temporal and causative relationships between how physical information affects cellular signaling and structure as well as understanding mechanism(s) of how cells maintain the mechanical memory of physical activity or inactivity to alter cellular form, function, and fate in both health and disease.
Current Lab interests
Nuclear mechanobiology
Proteins that connect nucleus to the extracellular signals such as Lamin A/C and Linker of Nucleoskeleton and Cytoskeleton (LINC) complexes are emerging as critical regulators of growth, differentiation and mechanosignaling. Therefore, our studies focus on better understanding nuclear mechanosignaling and it’s connection with aging and physical activity.
Data-driven, cell-specific prediction of nuclear forces and mechanics
Central to the cell mechanosignaling, the nucleus relies on cytoskeletal mechanical input through nuclear envelope adaptor proteins to sense external stimuli and respond by regulating intra-nuclear chromatin organization which determines cell function and fate. The increasing interest in closing the apparent knowledge gap of how nucleus is regulated by mechanical force is hampered by the lack of readily available methods that can estimate nuclear envelope forces without specialized experimental setups. MAL actively develops models to study nuclear mechanics in living cells and tissues using real time analysis and machine learning.
Engineered bone analogs for studying mechanical regulation
Both astronauts and aging individuals fail to maintain bone mass despite being physically active. To better understand the underlying mechanisms of this dysfunction we use novel bi-phasic bone marrow analogs and computational models to study effects of bone geometry on mechanosignaling of bone cells.
Organoids models for biotechnology research
A new research direction for MAL is the use of cortical organoids for controlling muscle activity. Biological neural networks, like our brains, execute tasks with high efficiency and speed while consuming substantially less power. The US could use such models to expand energy independence, reduce environmental impacts of large-scale AI use, and establish a secure, low-cost alternatives. In parallel, our emerging tools in cortical organoids are also enabling new directions to study neuronal development under microgravity.