Rashid, M. R. | bioRxiv Science

bioRxiv2025

Force Scaling in Active Swarm of Microtubules via Magnetic Manipulation

Scalability refers to the ability of a system to enhance performance with increasing size. This ability is a defining feature of natural swarms such as ant and bee colonies. Extending this principle to artificial active matter has motivated the creation of molecular swarms composed of myosin-driven actin filaments and kinesin-driven microtubules (MTs). While these swarms exhibit cooperative transport behaviors that surpass the abilities of individual filaments, their collective force generation has remained unquantified. Here, we introduce a straightforward electromagnetic tweezer approach to directly measure the forces generated by MT swarms propelled by surface-bound kinesin motors. We find that force output changes with swarm size, demonstrating quantitative scalability and linking collective organization to mechanical performance. These results establish MT swarms as a model system for scalable, force-producing active matter and provide a foundation for designing biomolecular devices that exploit collective dynamics for microscale actuation and transport. Significance StatementThe cooperative activity of multiple motile agents is observed in nature, known as swarming. In artificial systems, swarming has been demonstrated using active agents like kinesin and MTs, successfully enabling efficient cargo transportation. However, the force generated by the swarm remains unexplored, which is one of the keys to understanding the principles underlying swarm systems. This study introduces a simple method employing electromagnetic tweezers and magnetic beads to measure the force of the MT swarm accurately. The measured forces within the microtubule swarms of different sizes unveil the scaling effect, the additive nature of force generation as the kinesin unit increases. The force study sheds light on the fundamental properties, work performance, efficiency, and execution of the MT swarm.

biophysics↗

bioRxiv2020

Variation in error-based and reward-based human motor learning is related and associated with entorhinal volume

Error-based and reward-based processes are critical for motor learning, and are thought to be mediated via distinct neural pathways. However, recent behavioral work in humans suggests that both learning processes are supported by cognitive strategies and that these contribute to individual differences in motor learning ability. While it has been speculated that medial temporal lobe regions may support this strategic component to learning, direct evidence is lacking. Here we first show that faster and more complete learning during error-based visuomotor adaptation is associated with better learning during reward-based shaping of reaching movements. This result suggests that strategic processes, linked to faster and better learning, drive individual differences in both error-based and reward-based motor learning. We then show that right entorhinal cortex volume was larger in good learning individuals--classified across both motor learning tasks--compared to their poorer learning counterparts. This suggests that strategic processes underlying both error- and reward-based learning are linked to neuroanatomical differences in entorhinal cortex. Significance StatementWhile it is widely appreciated that humans vary greatly in their motor learning abilities, little is known about the processes and neuroanatomical bases that underlie these differences. Here, using a data-driven approach, we show that individual variability in error-based and reward-based motor learning is tightly linked, and related to the use of cognitive strategies. We further show that structural differences in entorhinal cortex predict this intersubject variability in motor learning, with larger entorhinal volumes being associated with better overall error-based and reward-based learning. Together, these findings provide support for the notion that the ability to recruit strategic processes underlies intersubject variability in both error-based and reward-based learning, which itself may be linked to structural differences in medial temporal regions.

neuroscience↗