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Biomedical active matter: Emergence and breakdown of collective functionalities

Arnold Mathijssen, Hamed Almohammadi, Lauren Altman, Talia Calazans, M. J. Ferencz, Michelle Fung, Ian J. Lee, Maciej Lisicki, Ivy Liu, Maggie Liu, Tianyi Liu, Ernest Park, Ran Tao, Albane Thery, Zeyuan Wang, Margot Young

Abstract

Living systems are made of active materials with microscopic components that work together to perform macroscopic biological tasks. The breakdown of these collective functionalities leads to diseases, which, conversely, could be treated by exploiting self-organization in healthcare technologies. Here, we review recent advances in this rapidly growing field of biomedical active matter. The main themes are (1) collective self-assembly and spatiotemporal coordination; (2) collective motion, transport, and navigation; (3) collective sensing, signaling, and communication; and (4) collective adaptation, evolution, and learning. We discuss these emerging processes in a wide range of systems, including protein folding, biomolecular condensates, cytoskeleton dynamics, intracellular flows, bacterial biofilms, quorum sensing, cilia synchronization, wound healing, biolocomotion, neurons, endocrine signalling, and cardiovascular flow networks. For each, we highlight medical conditions associated with reduced collective functionality and how they may be treated using microrobotic swarms, bioinspired metamaterials, diagnostics, lab-on-chip devices, organoids, and other active and adaptive matter innovations.

Biomedical active matter: Emergence and breakdown of collective functionalities

Abstract

Living systems are made of active materials with microscopic components that work together to perform macroscopic biological tasks. The breakdown of these collective functionalities leads to diseases, which, conversely, could be treated by exploiting self-organization in healthcare technologies. Here, we review recent advances in this rapidly growing field of biomedical active matter. The main themes are (1) collective self-assembly and spatiotemporal coordination; (2) collective motion, transport, and navigation; (3) collective sensing, signaling, and communication; and (4) collective adaptation, evolution, and learning. We discuss these emerging processes in a wide range of systems, including protein folding, biomolecular condensates, cytoskeleton dynamics, intracellular flows, bacterial biofilms, quorum sensing, cilia synchronization, wound healing, biolocomotion, neurons, endocrine signalling, and cardiovascular flow networks. For each, we highlight medical conditions associated with reduced collective functionality and how they may be treated using microrobotic swarms, bioinspired metamaterials, diagnostics, lab-on-chip devices, organoids, and other active and adaptive matter innovations.
Paper Structure (27 sections, 5 figures)

This paper contains 27 sections, 5 figures.

Table of Contents

  1. FROM MOLECULES TO CELLS
  2. Protein folding and macromolecular engineering
  3. Partitioning into organelles and biomolecular condensates
  4. Chromatin and gene regulation
  5. Cargo transport by molecular motors
  6. Force generation in motile organelles
  7. Collective dynamics of the cytoskeleton
  8. Intracellular transport, cytoplasmic streaming, and enhanced diffusion
  9. Sensing and signal transduction in cells
  10. FROM CELLS TO TISSUES
  11. Bacterial biofilm formation and dispersal
  12. Adaptive navigation by cells in complex environments
  13. Collective cell migration and active tissue mechanics
  14. Microrobotic swarms for drug delivery and minimally invasive surgeries
  15. Hydrodynamic entrainment and mixing due to collective motion
  16. ...and 12 more sections

Figures (5)

  • Figure 1: Self-assembly, organization, and spatiotemporal coordination. Artwork by Maggie Liu. (A) Structure of the SARS-CoV-2 spike glycoprotein. From NIH 3D (CC-BY). (B) Cut-away model of the ebola virus. A helix of protein (yellow) encloses the virus' genetic material (pink). From NIH 3D (CC-BY-NC). (C) Liquid-liquid phase separation forming biomolecular condensates, with perinucleolar caps bound to nucleolar bodies. From Shin2017LiquidDisease. (D) Structure of the mitotic spindle, with polar cytoskeletal filaments actuated by molecular motors, driving chromosome separation. From Furthauer2022HowMaterials. (E) Defects in the nematic order of epithelial tissues. From Doostmohammadi2018ActiveNematics. (F) Tissue morphogenesis in the development of zebrafish embryos. From Bruckner2024LearningReview.
  • Figure 2: Collective motion, force generation, and transport. Artwork by Maggie Liu. (A) Cargo transport driven by molecular motors. Left: Lysosome trajectory (red) along microtubules (green) in a monkey kidney cell. Right: Advection of acidified organelles in the flowing cytoplasm of a migrating HL60 cell. From Mogre2020GettingWorld. (B) Motile cilia. Top: Multiciliated cells in the mouse trachea. From G. Ramirez-San Juan. Bottom: Two-dimensional model of the axoneme. From Sartori2016DynamicFlagella. (C) Flows driven by cilia in the human airway. From J. Nawroth. (D) Cell crawling. Left: Underlying dendritic F-actin networks in the cytoskeleton. From Banerjee2020TheMaterial. Right: Flow in migrating neutrophil-like HL60 cell associated with deformation of cell boundary (pink arrows). From Mogre2020GettingWorld. (E) Collective cell migration, with contact inhibition of locomotion between inner cells (yellow arrows) and polarization of the leaders (green arrows). From Hakim2017CollectivePerspective. (F) Traction forces in LifeAct-GFP MDCK cells closing a wound. From Alert2020PhysicalMigration. (G) Schematic of force-generating sarcomeres in muscle myofibrils. Adapted from Caruel2018PhysicsContraction. (H) Cardiovascular flows from a patient with coronary aneurysms caused by Kawasaki disease. Left: Volume-rendered CT image data. Right: Corresponding flow simulation showing wall shear stress. From Marsden2014OptimizationModeling. (I) Emergence of collective oscillations in large human crowds. Inset: tracking individual people. From Gu2025EmergenceCrowds.
  • Figure 3: Collective sensing, signaling, and communication. Artwork by Maggie Liu. (A) Chemosensory system in E. coli. Signals received by MCP receptors are transduced to the flagellar motors. (B) Rheotaxis enables bacterial upstream swimming. Courtesy of Ran Tao. (C) Bacterial quorum sensing. Right: Signalling under flow. From K. Kim, F. Ingremeau, B. Bassler and H. Stone. (D) V. cholerae bacterial biofilm architecture. Left: 3D structure imaged with confocal microscopy. Right: Horizontal slice, with nucleoid (blue) and membrane (red) labels. Courtesy of K. Drescher. (E) Communication through hydrodynamic trigger waves. From Mathijssen2019CollectiveWaves. (F) Action potential propagating along tissue interface, revealed by a voltage-sensitive dye. From Scheibner2024SpikingSystems. (G) Action potentials. Right: Electrical circuit representing a neuron membrane. From Hodgkin1952ANerve. (H) Mouse hippocampus with neurodegenerative disease Niemann-Pick type C1. From I. Williams, NICHD (CC). (I) Hormone signalling pathways in the endocrine system. From OpenStax (CC).
  • Figure 4: Collective learning, adaptation, and evolution. Artwork by Maggie Liu. (A) Overview of mechanisms that regulate gene expression. (B) Chromatin gene regulation, by packaging DNA into tight coils around histone proteins. Image from Fierz2019BiophysicsDynamics. (C) Single-cell learning by habituation to repetitive stimuli. Insets: Biochemical networks that implement habituation. From Eckert2024BiochemicallyLearning. (D) Smart network adaptation in the giant slime mold Physarum polycephalum. From LeVerge-Serandour2024PhysarumAdaptation. (E) Immune system adaptation: the affinity maturation cycle where memory B cells proliferate and develop receptor mutations, which is evaluated using helper T cells. Adapted from Chakraborty2017RationalMedicine. (F) Neurons forming connections in the brain. From Eyal Karzbrun and Orly Reiner. (G) Artificial neural networks, trained by minimizing a cost function using algorithms such as backpropagation. (H) Physics-driven learning machine: Left: Electical circuit with 16 adaptive resistors. Right: This network is trained to achieve desired output voltages for a given input. Adapted from Dillavou2022DemonstrationLearning.
  • Figure 5: Biomedical applications of collective functionalities. Artwork by Hamed Almohammadi and Maggie Liu. Examples include (i) Diagnostics, with (A) active particles enhance biosensing and (B) single-cell microrheology to detect pathological conditions; (ii) Therapeutic delivery, including (C) synthetic peptide condensates for intracellular cargo delivery and (D) artificial microtubules, adapted from Gu2022ArtificialMicrocargoes; (iii) Tissue modeling, comprising (E) multi-organ-on-a-chip systems, and (F) brain organoids, adapted from Karzbrun2018HumanFolding; (iv) Prophylaxis, involving (G) collective disease spread prevention and (H) biofilm removal with magnetic nanoparticles; (v) Metamaterials and metafluids, including (I) 3D-printed piezoelectric artificial bones, adapted from Li2021BulkManufacturing, and (J) fluids with programmable optical and mechanical properties, adapted from Djellouli2024ShellMetafluids; and (vi) Biomimetics, such as (K) artificial cilia for microfluidic flow manipulation and (L) gecko-inspired adhesive patches.