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Farimani, A. B.

Publications and source records attributed to Farimani, A. B..

2 recordsLinked to original sources

AggreBots: configuring CiliaBots through guided, modular tissue aggregation

Ciliated biobots, or CiliaBots, are a class of engineered multicellular tissues that are capable of self-actuated motility propelled by the motile cilia located on their exterior surface. Correlations have been observed between CiliaBot motility patterns and their morphology and cilia distribution. However, precise control of these structural parameters to generate desired motility patterns predictably remains lacking. Here, we developed a novel Aggregated CiliaBot (AggreBot) platform capable of producing designer motility patterns through spatially controlled aggregation of epithelial spheroids made from human airway cells (referred to as CiliaBot Building Blocks or CBBs), yielding AggreBots with configurable geometry and distribution of active cilia. Guided multi-CBB aggregation led to the production of rod-, triangle-, and diamond-shaped AggreBots, which consistently effected greater motility than traditional single-spheroid CiliaBots. Furthermore, CBBs were found to maintain internal boundaries post-aggregation through the combined action of pathways controlling cellular fluidity and tissue polarity. This boundary fidelity, combined with the use of CBBs with immotile cilia due to mutations in the CCDC39 gene, allowed for the generation of hybrid AggreBots with precision control over the coverage and distribution of active cilia, further empowering control of motility patterns. Our results demonstrate the potential of AggreBots as self-propelling biological tissues through the establishment of morphological "levers" by which alterations to tissue motility can be theoretically planned and experimentally verified.

bioengineering↗

Kinetic Machine Learning Unravels Ligand-Directed Conformational Change of μ Opioid Receptor

The Opioid Receptor (OR) is a G-Protein Coupled Receptor (GPCR) that mediates pain and is a key target for clinically administered analgesics. The current generation of prescribed opiates - drugs that bind to OR - engender dangerous side effects such as respiratory depression and addiction in part by stabilizing off-target conformations of the receptor. To determine both the key conformations of OR to atomic resolution as well as the transitions between them, long timescale molecular dynamics (MD) simulations were conducted and analyzed. These simulations predict new and potentially druggable metastable states that have not been observed by crystallography. We applied cutting edge algorithms (e.g., tICA and Transfer Entropy) to guide our analysis and distill the key events and conformations from simulation, presenting a transferrable and systematic analysis scheme. Our approach provides a complete, predictive model of the dynamics, structure of states, and structure-ligand relationships of OR with broad applicability to GPCR biophysics and medicinal chemistry.

biophysics↗