Periodic Reporting for period 4 - ActiveBioFluids (Origins of Collective Motion in Active Biofluids)
Período documentado: 2021-10-01 hasta 2022-09-30
Living organisms on all length scales, from bacteria to large animals, are often found to form clusters, groups and colonies. The emergence of coherent behaviour arising between many such closely interacting organisms is ubiquitous in the natural world. One area of growing interest is the coherent motion and spontaneous order arising within and between motile single-celled organisms on the small micron-scale. This collective motion is crucial to diverse cell processes including fluid transport, mechanical signal transduction, embryonic development and biofilm formation. Collective dynamics is also involved in the colonization of surfaces by micro-organism, which are at the origin of the spreading of bacterial infections or the biofouling of surface. Understanding collective motion is also of interest to a range of applications in biomimetic micro-systems engineering.
We want to elucidate how such higher-order organization emerge in simple motile organisms and identify the physical mechanisms and the building blocks at the origin of this collective motion. To do this we design new experimental tools that allow us to both (1) mechanically interact with microorganisms in real time within microfluidic environments and (2) accurately track and record the motile response of the micro-organism. With our experimental set up we can apply fully characterized and controlled perturbations and precisely track the response of the organism to this external perturbation.
By using tools from control theory, we want to model the motile response of micro-organisms, which will allow us to fully control their motility. We apply this experimental approach to three different biological systems: the synchronization of the flagella of green algae Chlamydomonas Rheinhardtii, the metachronal wave in the cilia of protist Paramecium and the collective motion of swimming microorganisms in dense suspensions. The results of this project provide new understanding of the mechanical forces required for the collective motion of arguably some of the simplest motile living organisms. First, we have identified the phase dynamics of biological flagella, opening the door to the external control of cell motility. Second, we have assembled biological cilia in controlled lattice configurations on surfaces to study the emergence of synchronization between large numbers of ciliated cells. Finally, we have performed 3D tracking of microswimmers in flow chambers to study the interactions between swimmers and solid surfaces, the effect of viscoelasticity on swimmer locomotion and the pair-wise interactions between swimmers.
We want to elucidate how such higher-order organization emerge in simple motile organisms and identify the physical mechanisms and the building blocks at the origin of this collective motion. To do this we design new experimental tools that allow us to both (1) mechanically interact with microorganisms in real time within microfluidic environments and (2) accurately track and record the motile response of the micro-organism. With our experimental set up we can apply fully characterized and controlled perturbations and precisely track the response of the organism to this external perturbation.
By using tools from control theory, we want to model the motile response of micro-organisms, which will allow us to fully control their motility. We apply this experimental approach to three different biological systems: the synchronization of the flagella of green algae Chlamydomonas Rheinhardtii, the metachronal wave in the cilia of protist Paramecium and the collective motion of swimming microorganisms in dense suspensions. The results of this project provide new understanding of the mechanical forces required for the collective motion of arguably some of the simplest motile living organisms. First, we have identified the phase dynamics of biological flagella, opening the door to the external control of cell motility. Second, we have assembled biological cilia in controlled lattice configurations on surfaces to study the emergence of synchronization between large numbers of ciliated cells. Finally, we have performed 3D tracking of microswimmers in flow chambers to study the interactions between swimmers and solid surfaces, the effect of viscoelasticity on swimmer locomotion and the pair-wise interactions between swimmers.
We have designed different experimental set ups for the three biological systems we are targeting: the synchronization of the flagella of green algae Chlamydomonas Rheinhardtii, the metachronal wave in carpets of cilia and the collective motion of swimming microorganisms in dense suspensions.
1./ For single-cell experiments we have developed a velocimetry technique using optical tweezers, to track the motility of single cells by accurately measuring the flow field they generate with hightemporal accuracy. We are now able to measure flow velocities on the order of a few micrometers per second and fluctuating over short timescales on the order of ten milliseconds. This tool allows us to detect subtle rapid changes in the motion of the cells and precisely track their response to external mechanical forcing. In addition, we have developed a machine learning-based tracking algorithm to detect shape changes of motile organisms in real time, from high speed imaging. The tracking method uses convolutional and recurrent neural networks trained on a large set of cell images to detect their shapes. Rapid development in machine learning and the availability of new tools to implement neural networks has tremendously improved our ability to track the detail of cell deformations at the origin of motility. The tracking algorithm opens the door to real time tracking and control of cell motility. We apply mechanical forces and shear forces of hydrodynamic origins on different cells and using engineering tools from control theory and system identification, we identified the parameters characterizing the dynamics of a large sample of cells and in particular the phase sensitivity to hydrodynamic forces.
2./ To study the synchronization in carpets of cilia, we have developed a microfluidic approach to assemble ciliated cells on lattice geometries on a planar surface. We have designed a variety of cell arrangements, which allows us to vary the number of ciliated cells that we can assemble on the surface, the spatial organization of the lattice, the distance between the ciliated cells varying between 10 microns and 100 microns, as well as the rheology of the surrounding fluid.
3./ For experiments on the collective motion of swimming microorganisms in dense suspensions, we have developed a multi-view microscopy technique that allows us to track many freely swimming cells in a millimeter sized flow chamber. The microscopy technique supports the imaging of the swimming cells from four different angles. We have developed an accurate calibration technique and tracking technique within the frame work of projective geometry and using tools from computer vision. Currently, our method enables us to track the motion of up to 2000 separate cells in semidilute suspensions. We have gathered experimental data on close to 30000 cells interacting with solid boundaries and have characterized near and far wall interactions, which influence the concentration distribution of the cells away from the boundary. The experimental setup has been used to gather large datasets on the dynamics and trajectories of microorganisms, which provide insight into the mechanical interactions between swimmer and solid surface as well as between swimming microorganisms.
1./ For single-cell experiments we have developed a velocimetry technique using optical tweezers, to track the motility of single cells by accurately measuring the flow field they generate with hightemporal accuracy. We are now able to measure flow velocities on the order of a few micrometers per second and fluctuating over short timescales on the order of ten milliseconds. This tool allows us to detect subtle rapid changes in the motion of the cells and precisely track their response to external mechanical forcing. In addition, we have developed a machine learning-based tracking algorithm to detect shape changes of motile organisms in real time, from high speed imaging. The tracking method uses convolutional and recurrent neural networks trained on a large set of cell images to detect their shapes. Rapid development in machine learning and the availability of new tools to implement neural networks has tremendously improved our ability to track the detail of cell deformations at the origin of motility. The tracking algorithm opens the door to real time tracking and control of cell motility. We apply mechanical forces and shear forces of hydrodynamic origins on different cells and using engineering tools from control theory and system identification, we identified the parameters characterizing the dynamics of a large sample of cells and in particular the phase sensitivity to hydrodynamic forces.
2./ To study the synchronization in carpets of cilia, we have developed a microfluidic approach to assemble ciliated cells on lattice geometries on a planar surface. We have designed a variety of cell arrangements, which allows us to vary the number of ciliated cells that we can assemble on the surface, the spatial organization of the lattice, the distance between the ciliated cells varying between 10 microns and 100 microns, as well as the rheology of the surrounding fluid.
3./ For experiments on the collective motion of swimming microorganisms in dense suspensions, we have developed a multi-view microscopy technique that allows us to track many freely swimming cells in a millimeter sized flow chamber. The microscopy technique supports the imaging of the swimming cells from four different angles. We have developed an accurate calibration technique and tracking technique within the frame work of projective geometry and using tools from computer vision. Currently, our method enables us to track the motion of up to 2000 separate cells in semidilute suspensions. We have gathered experimental data on close to 30000 cells interacting with solid boundaries and have characterized near and far wall interactions, which influence the concentration distribution of the cells away from the boundary. The experimental setup has been used to gather large datasets on the dynamics and trajectories of microorganisms, which provide insight into the mechanical interactions between swimmer and solid surface as well as between swimming microorganisms.
Rapid development in machine learning and the availability of new tools to implement neural networks has tremendously improved our ability to track the detail of cell deformations at the origin of motility. The tracking algorithm opens the door to real time tracking and control of cell motility.
The project we will gather large data sets on the motile response of cells to mechanical forces. We will apply mechanical forces and shear forces of hydrodynamic origins on different cells and using engineering tools from control theory and system identification, we will identify the parameters characterizing the dynamics of a large sample of cells. We will investigate, how closely biological cells can be represented with model dynamics commonly used to represent engineering systems and we will design feedback loops to mechanically control motile cells. Experiments will be performed on different micro-organisms to find general principle governing mechanical interactions and control of biolocomotion.
Our tracking experiments of individual micro-organisms in a suspension has up to now focused on interactions between two micro-swimmers in a semi-dilute suspension. We will further develop our experimental setup and tracking algorithm to support tracking of denser suspension in order to study collective motion.
The project we will gather large data sets on the motile response of cells to mechanical forces. We will apply mechanical forces and shear forces of hydrodynamic origins on different cells and using engineering tools from control theory and system identification, we will identify the parameters characterizing the dynamics of a large sample of cells. We will investigate, how closely biological cells can be represented with model dynamics commonly used to represent engineering systems and we will design feedback loops to mechanically control motile cells. Experiments will be performed on different micro-organisms to find general principle governing mechanical interactions and control of biolocomotion.
Our tracking experiments of individual micro-organisms in a suspension has up to now focused on interactions between two micro-swimmers in a semi-dilute suspension. We will further develop our experimental setup and tracking algorithm to support tracking of denser suspension in order to study collective motion.