Each EmIRGE-Bio participant will be matched with two faculty co-mentors to pursue a research project in the broad field of emergent intelligence. Example projects include:
Sensing temperature via phase separation
Faculty: Bah, Upstate Mol Bio; Movileanu, PHY; Hanes, Upstate Biochem & Mol Bio; Wolfe, Ichor Therapeutics; Castañeda, BIO/CHEM.
Combining techniques from biochemistry and biophysics, the NRT trainees will establish a reconstituted in vitro system using purified components from this fungal transcription system, and systematically substitute individual CTDs, Ess1, and other transcription components from the extremophile fungi to monitor effects of phase separation on transcription activity. Skills: protein production, purification strategies for intrinsically disordered proteins, phase separation and binding assays, bioinformatics analysis from public databases.
How do cells in the brain sense and alter their environment to regulate neuronal plasticity?
Faculty: MacDonald, BIO; Patteson, PHY; Matthews, Upstate Neuro.; Schwarz, PHY.
Trainees will characterize biochemical ECM composition across typical and atypical neurodevelopment, and will investigate how altered composition changes the physical properties of the ECM, contributing to disrupted neuronal plasticity. Trainees will perform computational modeling of mechanics of cell/matrix interactions, building upon recently developed state-of-the-art simulations. Skills: mammalian cell culture, biochemical and structural analysis of fiber networks, rheological (material properties) measurements of tissue, computational modeling of cell collectives.
Controlling structure and dynamics of a cytoskeletal network using biomolecular condensates
Faculty: Henty-Ridilla, Upstate Mol Bio; Castañeda, BIO/CHE.
Using condensate-forming proteins that are known interactors with cytoskeleton such as UBQLN267 and RNA-binding proteins (TDP-43), the NRT trainee will use purified proteins combined with real-time super-resolution microscopy to examine the kinetics of filament growth in the absence and presence of condensates. The group will examine and model how condensates with different viscoelastic properties affect cytoskeletal network growth rate and filament size. Interactions between UBQLN2 and filament proteins will be examined with biophysical approaches including NMR spectroscopy. Skills: protein purification, NMR spectroscopy, super resolution microscopy, modeling.
How does the morphology of tree frog toe pads – and biomimetic analogues – actuate fluid transport and wet adhesion?
Faculty: Garner, BIO; Paulsen, PHY; Pandey, MAE; Santangelo, PHY; Zhang, MAE.
NRT trainees will (1) use morphological approaches to examine the diversity in tree frog toe pad morphology; (2) manufacture larger-scale biomimetic physical models and use high speed photography and precision force measurements to assess how this diversity influences fluid transport and adhesive performance, and (3) employ computational and theoretical approaches to model fluid transport and wet adhesion. Skills: functional morphology, biomechanics, experimental, computational and theoretical solid and fluid mechanics.
Are wrinkles a form of physical intelligence that alleviates stress in bacterial biofilms?
Faculty: Patteson, PHY; Zhang, MAE; Paulsen, PHY; Welch, BIO.
NRT trainees will first characterize wrinkles in biofilms, using high throughput microbiology techniques to generate a suite of bacterial biofilms with different genotypes and phenotypes and use state-of-the-art image analysis techniques to trace wrinkles. They will extend techniques developed by the Patteson lab to quantify the rheology (material response) of biofilms and correlate it with structural features of wrinkles. They will compare structural features of wrinkles in biofilms to those in synthetic wrinkled thin films pioneered by the Paulsen lab. They will develop a finite-element computational model of the biofilm-substrate interface and use models to quantitatively test the hypothesis that wrinkles alleviate stress. They will then study whether different material properties of biofilms make them more efficient at alleviating stress via wrinkles, e.g. whether wrinkles might be an evolved response. Skills: thin-sheet wrinkling mechanics, biophysics of microbial communities, computational modeling of mechanics of active materials.
Using hysterons, computation, and learning to build intelligent mechanical systems
Faculty: Schwarz, PHY; Pandey, MAE; Paulsen, PHY; Santangelo, PHY.
The trainee will couple together collections of such physical “hysterons” into a network of interconnected rotors — each of them a mechanical logical element that interacts with its neighbors. The trainee will design the rotor connections and positions and the anchor positions to control the emergent behavior of the entire network. This design process will be guided by new algorithms for multi-mechanism learning developed by Schwarz. One objective will be to produce a “pattern matcher”: a network that can recognize a particular state for a set of rotors on one boundary of the network, by flipping a single “indicator” rotor at the other side of the sample only when a particular pattern is entered in. Skills: mechanical metamaterials design, mathematical modeling, and computation.