Search for: All records

The emergence of engineered living materials (ELMs) has led to the development of functional composites by combining living microorganisms with nonliving components, particularly hydrogels. Hydrogels, which mimic the extracellular matrix, support microbial growth by providing essential nutrients and promoting cell adhesion, making them ideal for ELM production. However, hydrogel-based materials often face challenges in three-dimensional printing due to poor structural integrity and limited printability, frequently requiring additional processes, precise control, and/or material modifications to enhance their printing performance. This study focuses on developing a microorganism-laden gelatin microgel and gelatin solution-based composite bioink for self-supported printing of ELMs, enhanced via microbial-induced calcium carbonate precipitation. Gelatin microgels are utilized as rheology modifiers, enabling the yield-stress fluid behavior of the bioink for improved printability and postprinting shape retention, while transglutaminase enzymatically cross-links printed structures completely, resulting in good printability. Furthermore, Sporosarcina pasteurii in the bioink enables calcium carbonate deposition during postprinting culturing, forming robust, biomineralized structures. Fabricated samples are found to have significant successful mineral deposition with over 50 wt% calcium carbonate content, and they exhibit compressive strengths of up to 1.4 MPa. This approach offers a cost-effective, energy-efficient method for creating high-strength, biocompatible biocomposites with potential applications such as bone tissue engineering, coral restoration, and sustainable building development. 

Representation learning is a powerful tool that enables learning over large multitudes of agents or domains by enforcing that all agents operate on a shared set of learned features. However, many robotics or controls applications that would benefit from collaboration operate in settings with changing environments and goals, whereas most guarantees for representation learning are stated for static settings. Toward rigorously establishing the benefit of representation learning in dynamic settings, we analyze the regret of multi-task representation learning for linear-quadratic control. This setting introduces unique challenges. Firstly, we must account for and balance the misspecification introduced by an approximate representation. Secondly, we cannot rely on the parameter update schemes of single-task online LQR, for which least-squares often suffices, and must devise a novel scheme to ensure sufficient improvement. We demonstrate that for settings where exploration is benign, the regret of any agent after T timesteps scales with the square root of T/H, where H is the number of agents. In settings with difficult exploration, the regret scales as the square root of the input dimension times the parameter dimension multiplied by T, plus a term which scales with T to the three quarters divided by H to the one fifth. In both cases, by comparing to the minimax single-task regret, we see a benefit of a large number of agents. Notably, in the difficult exploration case, by sharing a representation across tasks, the effective task-specific parameter count can often be small. Lastly, we validate the trends we predict. 

Inducibly degradable polymers present new opportunities to integrate tough hydrogels into a wide range of biomaterials. Rapid and inducible degradation enables fast transition in material properties without sacrificing material integrity prior to removal. In pursuit of bioorthogonal chemical modalities that will enable inducible polymer degradation in biologically relevant environments, enamine N-oxide crosslinkers are developed for double network acrylamide-based polymer/alginate hydrogels. Bioorthogonal dissociation initiated by the application of aqueous diboron solution through several delivery mechanisms effectively lead to polymer degradation. Their degradation by aqueous B2(OH)4 solution results in a fracture energy half-life of <10 min. The biocompatibility of the degradable hydrogels and B2(OH)4 reagent is assessed, and the removability of strongly adhered tough hydrogels on mice skin is evaluated. Thermoresponsive PNiPAAm/Alg hydrogels are fabricated and application of the hydrogels as a chemically inducible degradable intraoral wound dressing is demonstrated. It is demonstrated through in vivo maximum tolerated dose studies that diboron solution administered to mice by oral gavage is well tolerated. Successful integration of enamine N-oxides within the tough double network hydrogels as chemically degradable motifs demonstrates the applicability of enamine N-oxides in the realm of polymer chemistry and highlights the importance of chemically induced bioorthogonal dissociation reactions for materials science. 

Neuromorphic hardware promises to revolutionize information technology with brain-inspired parallel processing, in-memory computing, and energy-efficient implementation of artificial intelligence and machine learning. In particular, two-dimensional (2D) memtransistors enable gate-tunable non-volatile memory, bio-realistic synaptic phenomena, and atomically thin scaling. However, previously reported 2D memtransistors have not achieved low operating voltages without compromising gate-tunability. Here, we overcome this limitation by demonstrating MoS2 memtransistors with short channel lengths < 400 nm, low operating voltages < 1 V, and high field-effect switching ratios > 10,000 while concurrently achieving strong memristive responses. This functionality is realized by fabricating back-gated memtransistors using highly polycrystalline monolayer MoS2 channels on high-κ Al2O3 dielectric layers. Finite-element simulations confirm enhanced electrostatic modulation near the channel contacts, which reduces operating voltages without compromising memristive or field-effect switching. Overall, this work demonstrates a pathway for reducing the size and power consumption of 2D memtransistors as is required for ultrahigh-density integration. 

Ligand-induced conformational changes are critical to the function of many membrane proteins and arise from numerous intramolecular interactions. In the photocycle of the model membrane protein bacteriorhodopsin (bR), absorption of a photon by retinal triggers a conformational cascade that results in pumping a proton across the cell membrane. While decades of spectroscopy and structural studies have probed this photocycle in intricate detail, changes in intramolecular energetics that underlie protein motions have remained elusive to experimental quantification. Here, we measured these energetics on the millisecond time scale using atomic-force-microscopy-based single-molecule force spectroscopy. Precisely, timed light pulses triggered the bR photocycle while we measured the equilibrium unfolding and refolding of the terminal 8-amino-acid region of bR’s G-helix. These dynamics changed when the EF-helix pair moved ~9 Å away from this end of the G helix during the “open” portion of bR’s photocycle. In ~60% of the data, we observed abrupt light-induced destabilization of 3.4 ± 0.3 kcal/mol, lasting 38 ± 3 ms. The kinetics and pH-dependence of this destabilization were consistent with prior measurements of bR’s open phase. The frequency of light-induced destabilization increased with the duration of illumination and was dramatically reduced in the triple mutant (D96G/F171C/F219L) thought to trap bR in its open phase. In the other ~40% of the data, photoexcitation unexpectedly stabilized a longer-lived putative misfolded state. Through this work, we establish a general single-molecule force spectroscopy approach for measuring ligand-induced energetics and lifetimes in membrane proteins. 

Clusters of hydrophobic residues are known to promote structured protein stability and drive protein aggregation. Recent work has shown that identifying contiguous hydrophobic residue clusters (termed “blobs”) has proven useful in both intrinsically disordered protein (IDP) simulation and human genome studies. However, a graphical interface was unavailable. Here, we present the blobulator: an interactive and intuitive web interface to detect intrinsic modularity in any protein sequence based on hydrophobicity. We demonstrate three use cases of the blobulator and show how identifying blobs with biologically relevant parameters provides useful information about a globular protein, two orthologous membrane proteins, and an IDP. Other potential applications are discussed, including: predicting protein segments with critical roles in tertiary interactions, providing a definition of local order and disorder with clear edges, and aiding in predicting protein features from sequence. The blobulator GUI can be found atwww.blobulator.branniganlab.org, and the source code with pip installable command line tool can be found on GitHub at www.GitHub.com/BranniganLab/blobulator.