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Computational modelling has made many useful contributions to the field of optical tweezers. One aspect in which it can be applied is the simulation of the dynamics of particles in optical tweezers. This can be useful for systems with many degrees of freedom, and for the simulation of experiments. While modelling of the optical force is a prerequisite for simulation of the motion of particles in optical traps, non-optical forces must also be included; the most important are usually Brownian motion and viscous drag. We discuss some applications and examples of such simulations. We review the theory and practical principles of simulation of optical tweezers, including the choice of method of calculation of optical force, numerical solution of the equations of motion of the particle, and finish with a discussion of a range of open problems.

Over the past decade, optical tweezers (OT) have been increasingly used in neuroscience for studies of molecules and neuronal dynamics, as well as for the study of model organisms as a whole. Compared to other areas of biology, it has taken much longer for OT to become an established tool in neuroscience. This is, in part, due to the complexity of the brain and the inherent difficulties in trapping individual molecules or manipulating cells located deep within biological tissue. Recent advances in OT, as well as parallel developments in imaging and adaptive optics, have significantly extended the capabilities of OT. In this review, we describe how OT became an established tool in neuroscience and we elaborate on possible future directions for the field. Rather than covering all applications of OT to neurons or related proteins and molecules, we focus our discussions on studies that provide crucial information to neuroscience, such as neuron dynamics, growth, and communication, as these studies have revealed meaningful information and provide direction for the field into the future.

We demonstrate how optical tweezers combined with a three-dimensional force detection system and high-speed camera are used to study the swimming force and behavior of trapped micro-organisms. By utilizing position sensitive detection, we measure the motility force of trapped particles, regardless of orientation. This has the advantage of not requiring complex beam shaping or microfluidic controls for aligning trapped particles in a particular orientation, leading to unambiguous measurements of the propulsive force at any time. Correlating the direct force measurements with position data from a high-speed camera enables us to determine changes in the particle’s behavior. We demonstrate our technique by measuring the swimming force and observing distinctions between swimming and tumbling modes of the Escherichia coli (E. coli) strain MC4100. Our method shows promise for application in future studies of trappable but otherwise arbitrary-shaped biological swimmers and other active matter.

It is common to introduce optical tweezers using either geometric optics for large particles or the Rayleigh approximation for very small particles. These approaches are successful at conveying the key ideas behind optical tweezers in their respective regimes. However, they are insufficient for modelling particles of intermediate size and large particles with small features. For this, a full field approach provides greater insight into the mechanisms involved in trapping. The advances in computational capability over the last decade has led to better modelling and understanding of optical tweezers.Problems that were previously difficult to model computationally can now be solved using a variety of methods on modern systems. These advances in computational power allow for full field solutions to be visualised, leading to increased understanding of the fields and behaviour in various scenarios. In this paper we describe the operation of optical tweezers using full field simulations calculated using the finite difference time domain method. We use these simulations to visually illustrate various situations relevant to optical tweezers, from the basic operation of optical tweezers, to engineered particles and evanescent fields.

Since their invention in the 1980s, optical tweezers have found a wide range of applications, from biophotonics and mechanobiology to microscopy and optomechanics. Simulations of the motion of microscopic particles held by optical tweezers are often required to explore complex phenomena and to interpret experimental data. For the sake of computational efficiency, these simulations usually model the optical tweezers as an harmonic potential. However, more physically-accurate optical-scattering models are required to accurately model more onerous systems; this is especially true for optical traps generated with complex fields. Although accurate, these models tend to be prohibitively slow for problems with more than one or two degrees of freedom (DoF), which has limited their broad adoption. Here, we demonstrate that machine learning permits one to combine the speed of the harmonic model with the accuracy of optical-scattering models. Specifically, we show that a neural network can be trained to rapidly and accurately predict the optical forces acting on a microscopic particle. We demonstrate the utility of this approach on two phenomena that are prohibitively slow to accurately simulate otherwise: the escape dynamics of swelling microparticles in an optical trap, and the rotation rates of particles in a superposition of beams with opposite orbital angular momenta. Thanks to its high speed and accuracy, this method can greatly enhance the range of phenomena that can be efficiently simulated and studied.

A R T I C L E I N F OKeywords: optical trapping dual beam trap annular beams spatial light modulator dynamic orientation motile particles A B S T R A C TOptical tweezers are a versatile tool that can be used to manipulate small particles including both motile and non-motile bacteria and cells. The orientation of a non-spherical particle within a beam depends on the shape of the particle and the shape of the light field. By using multiple beams, sculpted light fields or dynamically changing beams, it is possible to control the orientation of certain particles. In this paper we discuss the orientation of the rod-shaped bacteria Escherichia coli (E. coli) using dynamically shifting annular beam optical tweezers. We begin with examples of different beams used for the orientation of rod-shaped particles. We discuss the differences between orientation of motile and non-motile particles, and explore annular beams and the circumstances when they may be beneficial for manipulation of non-spherical particles or cells. Using simulations we map out the trajectory the E. coli takes. Estimating the trap stiffness along the trajectory gives us an insight into how stable an intermediate rotation is with respect to the desired orientation. Using this method, we predict and experimentally verify the change in the orientation of motile E. coli from vertical to near-horizontal with only one intermediate step.The method is not specific to exploring the orientation of particles and could be easily extended to quantify the stability of an arbitrary particle trajectory.

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