Biology subjects

Jariwala, S.

Publications and source records attributed to Jariwala, S..

3 recordsLinked to original sources

bioRxiv2025

Advancing FAIR Data Management through AI-Assisted Curation of Morphological Data Matrices

Curation of biological and paleontological datasets is a labor-intensive process that requires standardization and validation to ensure data integrity. In particular, manual curation of datasets is prone to human errors such as typographical errors, inconsistent formatting, and incomplete metadata, which hinder reproducibility and compliance with Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Artificial Intelligence (AI) offers a transformative solution for enhancing research efficiency by automating data validation, improving accuracy, and streamlining curation workflows. This study presents a proof-of-concept implementation of an AI-assisted curation tool developed for MorphoBank, an open access repository established to enhance standardization and usability of morphological character datasets. Specifically, this work presents an AI tool designed to extract, structure, and standardize morphological character data from published literature into the NEXUS file format, a widely used format for phylogenetic analyses. This tool leverages machine learning techniques, including Large Language Models (LLMs), to automate the extraction of character names and states from text in various formats, reducing manual data entry errors and improving data completeness. The system enables efficient conversion of matrix-only files into complete, machine- and human-readable datasets that include key character metadata. By assisting with these tasks, the tool reduces the manual effort required for curation while improving consistency and standardization. This approach increases the FAIRness of morphological character data and provides a framework for extending AI-assisted curation to other types of biological data. These results illustrate the potential of AI-assisted workflows to support scalable data curation and reuse in paleontology, systematics, and evolutionary biology.

bioinformatics↗

bioRxiv2025

Designer DX-tile DNAns hydrogels

Pure deoxyribonucleic acid (DNA) hydrogels synthesized via the hybridization of multi-arm DNA tiles are uniquely programmable and functionalizable biomaterials suitable for applications ranging from biosensing to cell-free protein production and soft tissue engineering. However, the full potential of the design flexibility and functionalization offered by DNA molecules has not yet been leveraged for pure DNA hydrogels, thereby limiting their range of mechanical properties and reducing their versatility and broader use. In this study, we introduce multi-arm double-crossover (DX)-tile motifs, often used in DNA nanoparticle design, to enable greater control over the hydrogels mechanical properties and facilitate functionalization. Specifically, we demonstrate that modifying structural design parameters, such as the arm geometry, length, valency, and linker design, allows fine control of the elastic modulus and viscoelastic properties of the hydrogels. We also show that functionalization can be performed without compromising the hydrogels physical properties and exhibit enhanced mechanical strength and tunable properties, compared to simple duplex-based DNA hydrogels. Furthermore, these DNA hydrogels demonstrated printability and scalability, which pave the way towards the development of novel formulations and bioinks for the rational design of soft tissue engineering scaffolds and broaden the use of DNA hydrogels for other biomedical applications.

bioengineering↗

bioRxiv2020

Pathogenic Mutations in the Kinesin-3 Motor KIF1A Diminish Force Generation and Movement Through Allosteric Mechanisms

The kinesin-3 motor KIF1A functions in neurons where its fast and superprocessive motility is thought to be critical for long-distance transport. However, little is known about the force-generating properties of kinesin-3 motors. Using optical tweezers, we demonstrate that KIF1A and its C. elegans homolog UNC-104 undergo force-dependent detachments at ~3 pN and then rapidly reattach to the microtubule to resume motion, resulting in a sawtooth pattern of clustered force generation events that is unique among the kinesin superfamily. Whereas UNC-104 motors stall before detaching, KIF1A motors do not. To examine the mechanism of KIF1A force generation, we introduced mutations linked to human neurodevelopmental disorders, V8M and Y89D, based on their location in structural elements required for force generation in kinesin-1. Molecular dynamics simulations predict that the V8M and Y89D mutations impair docking of the N-terminal ({beta}9) or C-terminal ({beta}10) portions of the neck linker, respectively, to the KIF1A motor domain. Indeed, both mutations dramatically impair force generation of KIF1A but not the motors ability to rapidly reattach to the microtubule track. Homodimeric and heterodimeric mutant motors also display decreased velocities, run lengths, and landing rates and homodimeric Y89D motors exhibit a higher frequency of non-productive, diffusive events along the microtubule. In cells, cargo transport by the mutant motors is delayed. Our work demonstrates the importance of the neck linker in the force generation of kinesin-3 motors and advances our understanding of how mutations in the kinesin motor domain can manifest in disease.

biophysics↗