Beyond the programme of the LM the co-location with the EPS Forum, allowed the participants to learn about industrial opportunities and to attend lectures from world-class researchers, including 3 Nobel Laureates. Scientific outreach, cultural exchange, and peaceful international collaboration are more important than ever. Bringing young scientists together and equipping them with tools and skills is a great way of fostering these aspects.
Between the 23rd and the 27th of May 2022 Liam participated in the CECAM workshop on Computational methods and tools for complex suspensions to present some of his work. In his talk titled “Modelling biological matter as active nematic fluids” he highlighted how numerical simulations of active fluids can be used to study the self-organization of three-dimensional tissues in a variety of biological systems, where a continuous influx of energy on a single-cell level drives striking collective behaviour at the tissue scale.
Then, the ESRs briefly described their research in a popular science style, so addressed to a broader public. The first hour concluded with a presentation about rules to keep in mind while communicating both in oral and written form.
Afterwards, a few examples among the written texts were selected and discussed with all the participants. The aim was to exchange feedback and suggestions on how to make the communication more effective. The feedback was the inspiration for everyone to review their communication example, and the final versions are being uploaded on the official twitter account @ActiveMatterITN.
He presented the poster “A microfluidic platform for the study of bacterial biofilms” showing his advances in the development of a droplet-based microfluidic platform for in situ observation of bacterial behavior and biofilms.
The NanoBioTech Conference brings together international researchers in the fields of Micro- and Nanotechnology and its applications in Biology and Medicine.
Apart from the featured talks and presentations on related topics and techniques of interest, Jesus benefited from the direct contact with international researchers, that promoted an exchange of ideas and opens the door for possible future collaborations.
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
Submitted to: ELS-XIX (2021)
Date: 14 July
Time: 12:50 CEST
Short Abstract:
We show how machine learning can improve the speed and accuracy of the optical force calculations in the geometrical optics approximation
Extended Abstract:
Since the pioneering work by Ashkin in the 1970’s, optical forces have played a fundamental role in fields like biology, nanotechnology, or atomic physics. In all these fields, numerical simulations are of great help for validating theories, for the planning of experiments, and in the interpretation of the results. However, the calculation of the forces is computationally expensive and prohibitively slow for numerical simulations when the forces need to be calculated many times in a sequential way.
Recently, machine learning has been demonstrated to be a promising approach to improve the speed of these calculations and therefore, to expand the applicability of numerical simulations for experimental design and analysis. In this work we show that machine learning can be used to improve not only the speed but also the accuracy of the force calculation in the geometrical optics regime, valid when the particles are significantly bigger than the wavelength of the incident light. This is first demonstrated for the case of a spherical particle with 3 degrees of freedom and later expanded to 9 degrees of freedom by including all the relevant parameters involved in the optical forces calculation. Machine learning is proved as a compact, accurate, and fast approach for optical forces calculation and presents a tool that can be used to study systems that, due to computation limitations, were out of the scope of the traditional ray optics approach.
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
Submitted to: OSA-OMA-2021, AF2D.2 Contribution
Date: 16 April
Time: 17:00 CEST
Short Abstract:
We show how machine learning can improve the speed and accuracy of the optical force calculations in the geometrical optics approximation
Extended Abstract:
Light can exert forces by exchanging momentum with particles. Since the pioneering work by Ashkin in the 1970’s, optical forces have played a fundamental role in fields like biology, nanotechnology, or atomic physics. Optical tweezers, which are instruments that, by tightly focusing a laser beam, are capable of confining particles in three dimensions, have become a common tool for manipulation of micro- and nano- particles, as well as a force and torque transducer with sensing capabilities at the femtonewton level. Optical tweezers have also been successfully employed to explore novel phenomena, including protein folding and molecular motors, or the optical forces and Brownian motion of 1D and 2D materials.
Numerical simulations play a fundamental role in the planning of experiments and in the interpretation of the results. In some basic cases for optical tweezers, the optical trap can be approximated by a harmonic potential. However, there are many situations where this approximation is insufficient, for example in the case of a particle escaping an optical trap, or for particles that are moving on an optical landscape but are not trapped. In these cases, a more complex treatment of the light-matter interaction is required for a more accurate calculation of the forces. This calculation is computationally expensive and prohibitively slow for numerical simulations when the forces need to be calculated many times in a sequential way. Recently, machine learning has been demonstrated to be a promising approach to improve the speed of these calculations and therefore, to expand the applicability of numerical simulations for experimental design and analysis.
In this work, we explore the geometrical optics regime, valid when the particles are significantly bigger than the wavelength of the incident light. This is typically the case in experiments with micrometer-size particles. The optical field is described by a collection of N light rays and the momentum exchange between the rays and the particle is calculated employing the tools of geometrical optics. The limitation of considering a discrete N number of light rays introduces artifacts in the force calculation. We show that machine learning can be used to improve not only the speed but also the accuracy of the force calculation. This is first demonstrated by training a neural network for the case of a spherical particle with 3 degrees of freedom accounting for the position of the particle. We show how the neural network improves the prediction of the force with respect to the initial training data that has been generated through the geometrical optics approach.
Starting from these results for 3 degrees of freedom, the work has been expanded to 9 degrees of freedom by including all the relevant parameters for the calculation of the optical forces considering also different refractive indexes, shapes, sizes, positions, and orientations of the particle besides different numerical apertures of the objective that focuses the light.
This work proves machine learning as a compact, accurate, and fast approach for optical forces calculation and presents a tool that can be used to study systems that, due to computation limitations, were out of the scope of the traditional ray optics approach.
A lot is understood about the ways in which single cells move, but there are still many questions about the motion and organisation of cell aggregates where cells coupled through intercellular junctions show a range of collective behaviours.
This work, which has been recently published Phys. Rev. X 11, 021001 (2021), shows the potential of active nematic continuum models to describe collective cell motion in a three dimensional environment.
Popular Summary:
Active matter describes systems—living and synthetic—where a continuous influx of energy at the level of individual components leads to striking collective behavior among the individual components, such as self-organizing bacteria colonies, bird flocks, or polymers in the cytoskeleton of cells. Understanding their behavior has attracted interest for studies of biological systems—from the spread of cancer to the development of organisms—as well the development of mesoscopic engines. Here, we numerically investigate 3D droplets composed of active matter and the ways in which their shapes change in response to the continuous input of energy.
One striking observation is the continuous formation of fingerlike protrusions, reminiscent of the collective motion of invading cancer cells. By changing the mechanical properties of the drop or the activity level, we find several different dynamical responses: For example, the droplet surface can wrinkle in a way that resembles a walnut or the active forces can drive a dimple in the droplet to grow, leading to a cup shape. Such invagination is reminiscent of patterns seen during morphogenesis.
Understanding the behavior of model systems, here a continuum model of active material, is an important step toward the goal of understanding the role of physical theories in the life sciences.
The first meeting between all PIs and ESRs meeting in our network took place on 10 September 2020. During this meeting, Ayten Gülce Bayram, ESR from Bilkent University, presented herself and her research project through a short video presentation. If you are curious about how her research studies are going as a first-year doctoral student in ActiveMatter ITN, please have a look at her presentation!
The ActiveMatter ESRs + PIs Online Meeting (if you want to know more read the main news here) took place on the 10th of last month. For the first time, the members of the ActiveMatter were in the “same place” also if just online. All the Early Stage Researchers were asked to introduce themselves in a short presentation video.
Laura Natali, ESR at the University of Gothenburg, also presented herself during the meeting.
The video lasts only five minutes and introduces Laura, her current project and the area of research she will study during the PhD. You can find the presentation below, and it is also on the ActiveMatter youtube channel .
