Long-term imaging and spatio-temporal control of living cells using targeted light based on closed-loop feedback

The ability to optically interact with cells on both an individual and collective level has applications from wound healing to cancer treatment. Building systems that can facilitate both localised light illumination and visualisation of cells can, however, be challenging and costly. This work takes the Dynamic Optical MicroEnvironment (DOME), an existing platform for the closed-loop optical control of microscale agents, and adapts the design to support live-cell imaging. Through modifications made to the imaging and projection systems within the DOME, a significantly higher resolution, alternative imaging channels and the ability to customise light wavelengths are achieved (Bio-DOME). This is accompanied by an interactive calibration procedure that is robust to changes in the hardware configuration and provides fluorescence imaging (Fluoro-DOME). These alterations to the fundamental design allow for long-term use of the DOME in an environment of higher temperature and humidity. Thus, long-term imaging of living cells in a wound, with closed-loop control of real-time frontier illumination via projected light patterns, is facilitated.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12213-024-00165-0.

Optimization of periodic treatment strategies for bacterial biofilms using an agent-based in silico approach

Biofilms are responsible for most chronic infections and are highly resistant to antibiotic treatments. Previous studies have demonstrated that periodic dosing of antibiotics can help sensitize persistent subpopulations and reduce the overall dosage required for treatment. Because the dynamics and mechanisms of biofilm growth and the formation of persister cells are diverse and are affected by environmental conditions, it remains a challenge to design optimal periodic dosing regimens. Here, we develop a computational agent-based model to streamline this process and determine key parameters for effective treatment. We used our model to test a broad range of persistence switching dynamics and found that if periodic antibiotic dosing was tuned to biofilm dynamics, the dose required for effective treatment could be reduced by nearly 77%. The biofilm architecture and its response to antibiotics were found to depend on the dynamics of persister cells. Despite some differences in the response of biofilm governed by different persister switching rates, we found that a general optimized periodic treatment was still effective in significantly reducing the required antibiotic dose. As persistence becomes better quantified and understood, our model has the potential to act as a foundation for more effective strategies to target bacterial infections.

Global parameter optimisation and sensitivity analysis of antivenom pharmacokinetics and pharmacodynamics

In recent years it has become possible to design snakebite antivenoms with diverse pharmacokinetic properties. Owing to the pharmacokinetic variability of venoms, the choice of antivenom scaffold may influence a treatment's neutralisation coverage. Computation offers a useful medium through which to assess the pharmacokinetics and pharmacodynamics of envenomation-treatment systems, as antivenoms with identical neutralising capacities can be simulated. In this study, we simulate envenomation and treatment with a variety of antivenoms, to define the properties of effective antivenoms. Systemic envenomation and treatment were described using a two-compartment pharmacokinetic model. Treatment of Naja sumatrana and Cryptelytrops purpureomaculatus envenomation was simulated with a set of 200,000 theoretical antivenoms across 10 treatment time delays. These two venoms are well-characterised and have differing pharmacokinetic properties. The theoretical antivenom set varied across molecular weight, dose, kon, koff, and valency. The best and worst treatments were identified using an area under the curve metric, and a global sensitivity analysis was performed to quantify the influence of the input parameters on treatment outcome. The simulations show that scaffolds of diverse molecular formats can be effective. Molecular weight and valency have a negligible direct impact on treatment outcome, however low molecular weight scaffolds offer more flexibility across the other design parameters, particularly when treatment is delayed. The simulations show kon to primarily mediate treatment efficacy, with rates above 105 M-1s-1 required for the most effective treatments. koff has the greatest impact on the performance of less effective scaffolds. While the same scaffold preferences for improved treatment are seen for both model snakes, the parameter bounds for C. purpureomaculatus envenomation are more constrained. This paper establishes a computational framework for the optimisation of antivenom design.

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

Advanced medical micro-robotics for early diagnosis and therapeutic interventions

Recent technological advances in micro-robotics have demonstrated their immense potential for biomedical applications. Emerging micro-robots have versatile sensing systems, flexible locomotion and dexterous manipulation capabilities that can significantly contribute to the healthcare system. Despite the appreciated and tangible benefits of medical micro-robotics, many challenges still remain. Here, we review the major challenges, current trends and significant achievements for developing versatile and intelligent micro-robotics with a focus on applications in early diagnosis and therapeutic interventions. We also consider some recent emerging micro-robotic technologies that employ synthetic biology to support a new generation of living micro-robots. We expect to inspire future development of micro-robots toward clinical translation by identifying the roadblocks that need to be overcome.

Lateral line morphology, sensory perception and collective behaviour in African cichlid fish

The lateral line system of fishes provides cues for collective behaviour, such as shoaling, but it remains unclear how anatomical lateral line variation leads to behavioural differences among species. Here we studied associations between lateral line morphology and collective behaviour using two morphologically divergent species and their second-generation hybrids. We identify collective behaviours associated with variation in canal and superficial lateral line morphology, with closer proximities to neighbouring fish associated with larger canal pore sizes and fewer superficial neuromasts. A mechanistic understanding of the observed associations was provided by hydrodynamic modelling of an artificial lateral line sensor, which showed that simulated canal-based neuromasts were less susceptible to saturation during unidirectional movement than simulated superficial neuromasts, while increasing the canal pore size of the simulated lateral line sensor elevated sensitivity to vortices shed by neighbouring fish. Our results propose a mechanism behind lateral line flow sensing during collective behaviour in fishes.

A simple macro-scale artificial lateral line sensor for the detection of shed vortices

Underwater robot sensing is challenging due to the complex and noisy nature of the environment. The lateral line system in fish allows them to robustly sense their surroundings, even in turbid and turbulent environments, allowing them to perform tasks such as shoaling or foraging. Taking inspiration from the lateral line system in fish to design robot sensors could help to power underwater robots in inspection, exploration, or environmental monitoring tasks. Previous studies have designed systems that mimic both the design and the configuration of the lateral line and neuromasts, but at high cost or using complex procedures. Here, we present a simple, low cost, bio-inspired sensor, that can detect passing vortices shed from surrounding obstacles or upstream fish or robots. We demonstrate the importance of the design elements used, and show a minimum 20% reduction in residual error over sensors lacking these elements. Results were validated in reality using a prototype of the artificial lateral line sensor. These results mark an important step in providing alternate methods of control in underwater vehicles that are simultaneously inexpensive and simple to manufacture.

Liquid biopsies for early diagnosis of brain tumours: in silico mathematical biomarker modelling

Brain tumours are the biggest cancer killer in those under 40 and reduce life expectancy more than any other cancer. Blood-based liquid biopsies may aid early diagnosis, prediction and prognosis for brain tumours. It remains unclear whether known blood-based biomarkers, such as glial fibrillary acidic protein (GFAP), have the required sensitivity and selectivity. We have developed a novel in silico model which can be used to assess and compare blood-based liquid biopsies. We focused on GFAP, a putative biomarker for astrocytic tumours and glioblastoma multi-formes (GBMs). In silico modelling was paired with experimental measurement of cell GFAP concentrations and used to predict the tumour volumes and identify key parameters which limit detection. The average GBM volumes of 449 patients at Leeds Teaching Hospitals NHS Trust were also measured and used as a benchmark. Our model predicts that the currently proposed GFAP threshold of 0.12 ng ml-1 may not be suitable for early detection of GBMs, but that lower thresholds may be used. We found that the levels of GFAP in the blood are related to tumour characteristics, such as vasculature damage and rate of necrosis, which are biological markers of tumour aggressiveness. We also demonstrate how these models could be used to provide clinical insight.

Information transport in communication limited swarms

Users and operators of swarms will, in the future, need to monitor the operations of swarms in a distributed way, without explicitly tracking every agent, and without the need for significant infrastructure or set up. Here we present a method for swarm self-monitoring that enables the aggregate display of information about swarm location by making use of physical transport of information and local communication. This method uses movement already exhibited by many swarms to collect self-reflective information in a fully distributed manner. We find that added swarm mobility can compensate for limited communication and that our self-monitoring swarm system scales well, with performance increasing with the size of the swarm in some cases. When developing systems such as this for real-world applications, individual agent memory will need to be taken into consideration, finding an effective means to spread swarm knowledge among robots while keeping information accessible to users.

Developing a computational pharmacokinetic model of systemic snakebite envenomation and antivenom treatment

Snakebite envenomation is responsible for over 100,000 deaths and 400,000 cases of disability annually, most of which are preventable through access to safe and effective antivenoms. Snake venom toxins span a wide molecular weight range, influencing their absorption, distribution, and elimination within the body. In recent years, a range of scaffolds have been applied to antivenom development. These scaffolds similarly span a wide molecular weight range and subsequently display diverse pharmacokinetic behaviours. Computational simulations represent a powerful tool to explore the interplay between these varied antivenom scaffolds and venoms, to assess whether a pharmacokinetically optimal antivenom exists. The purpose of this study was to establish a computational model of systemic snakebite envenomation and treatment, for the quantitative assessment and comparison of conventional and next-generation antivenoms. A two-compartment mathematical model of envenomation and treatment was defined and the system was parameterised using existing data from rabbits. Elimination and biodistribution parameters were regressed against molecular weight to predict the dynamics of IgG, F(ab')₂, Fab, scFv, and nanobody antivenoms, spanning a size range of 15-150 kDa. As a case study, intramuscular envenomation by Naja sumatrana (equatorial spitting cobra) and its treatment using Fab, F(ab')₂, and IgG antivenoms was simulated. Variable venom dose tests were applied to visualise effective antivenom dose levels. Comparisons to existing antivenoms and experimental rescue studies highlight the large dose reductions that could result from recombinant antivenom use. This study represents the first comparative in silico model of snakebite envenomation and treatment.

Measuring Nanoparticle Penetration Through Bio-Mimetic Gels

Background

In cancer nanomedicine, drugs are transported by nanocarriers through a biological system to produce a therapeutic effect. The efficacy of the treatment is affected by the ability of the nanocarriers to overcome biological transport barriers to reach their target. In this work, we focus on the process of nanocarrier penetration through tumour tissue after extravasation. Visualising the dynamics of nanocarriers in tissue is difficult in vivo, and in vitro assays often do not capture the spatial and physical constraints relevant to model tissue penetration.

Methods

We propose a new simple, low-cost method to observe the transport dynamics of nanoparticles through a tissue-mimetic microfluidic chip. After loading a chip with triplicate conditions of gel type and loading with microparticles, microscopic analysis allows for tracking of fluorescent nanoparticles as they move through hydrogels (Matrigel and Collagen I) with and without cell-sized microparticles. A bespoke image-processing codebase written in MATLAB allows for statistical analysis of this tracking, and time-dependent dynamics can be determined.

Results

To demonstrate the method, we show size-dependence of transport mechanics can be observed, with diffusion of fluorescein dye throughout the channel in 8 h, while 20 nm carboxylate FluoSphere diffusion was hindered through both Collagen I and Matrigel™. Statistical measurements of the results are generated through the software package and show the significance of both size and presence of microparticles on penetration depth.

Conclusion

This provides an easy-to-understand output for the end user to measure nanoparticle tissue penetration, enabling the first steps towards future automated experimentation of transport dynamics for rational nanocarrier design.

Morphogenesis in robot swarms

Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering.

Toward Engineering Biosystems With Emergent Collective Functions

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.

Mutual Shaping in Swarm Robotics: User Studies in Fire and Rescue, Storage Organization, and Bridge Inspection

Many real-world applications have been suggested in the swarm robotics literature. However, there is a general lack of understanding of what needs to be done for robot swarms to be useful and trusted by users in reality. This paper aims to investigate user perception of robot swarms in the workplace, and inform design principles for the deployment of future swarms in real-world applications. Three qualitative studies with a total of 37 participants were done across three sectors: fire and rescue, storage organization, and bridge inspection. Each study examined the users' perceptions using focus groups and interviews. In this paper, we describe our findings regarding: the current processes and tools used in these professions and their main challenges; attitudes toward robot swarms assisting them; and the requirements that would encourage them to use robot swarms. We found that there was a generally positive reaction to robot swarms for information gathering and automation of simple processes. Furthermore, a human in the loop is preferred when it comes to decision making. Recommendations to increase trust and acceptance are related to transparency, accountability, safety, reliability, ease of maintenance, and ease of use. Finally, we found that mutual shaping, a methodology to create a bidirectional relationship between users and technology developers to incorporate societal choices in all stages of research and development, is a valid approach to increase knowledge and acceptance of swarm robotics. This paper contributes to the creation of such a culture of mutual shaping between researchers and users, toward increasing the chances of a successful deployment of robot swarms in the physical realm.

Testing the limits of pheromone stigmergy in high-density robot swarms

Area coverage and collective exploration are key challenges for swarm robotics. Previous research in this field has drawn inspiration from ant colonies, with real, or more commonly virtual, pheromones deposited into a shared environment to coordinate behaviour through stigmergy. Repellent pheromones can facilitate rapid dispersal of robotic agents, yet this has been demonstrated only for relatively small swarm sizes (N < 30). Here, we report findings from swarms of real robots (Kilobots) an order of magnitude larger (N > 300) and from realistic simulation experiments up to N = 400. We identify limitations to stigmergy in a spatially constrained, high-density environment-a free but bounded two-dimensional workspace-using repellent binary pheromone. At larger N and higher densities, a simple stigmergic avoidance algorithm becomes first no better, then inferior to, the area coverage of non-interacting random walkers. Thus, the assumption of robustness and scalability for such approaches may need to be re-examined when they are working at a high density caused by ever-increasing swarm sizes. Instead, subcellular biology, and diffusive processes, may prove a better source of inspiration at large N in high agent density environments.

Synthetic and living micropropellers for convection-enhanced nanoparticle transport

Nanoparticles (NPs) have emerged as an advantageous drug delivery platform for the treatment of various ailments including cancer and cardiovascular and inflammatory diseases. However, their efficacy in shuttling materials to diseased tissue is hampered by a number of physiological barriers. One hurdle is transport out of the blood vessels, compounded by difficulties in subsequent penetration into the target tissue. Here, we report the use of two distinct micropropellers powered by rotating magnetic fields to increase diffusion-limited NP transport by enhancing local fluid convection. In the first approach, we used a single synthetic magnetic microrobot called an artificial bacterial flagellum (ABF), and in the second approach, we used swarms of magnetotactic bacteria (MTB) to create a directable "living ferrofluid" by exploiting ferrohydrodynamics. Both approaches enhance NP transport in a microfluidic model of blood extravasation and tissue penetration that consists of microchannels bordered by a collagen matrix.

A Two Teraflop Swarm

We introduce the Xpuck swarm, a research platform with an aggregate raw processing power in excess of two teraflops. The swarm uses 16 e-puck robots augmented with custom hardware that uses the substantial CPU and GPU processing power available from modern mobile system-on-chip devices. The augmented robots, called Xpucks, have at least an order of magnitude greater performance than previous swarm robotics platforms. The platform enables new experiments that require high individual robot computation and multiple robots. Uses include online evolution or learning of swarm controllers, simulation for answering what-if questions about possible actions, distributed super-computing for mobile platforms, and real-world applications of swarm robotics that requires image processing, or SLAM. The teraflop swarm could also be used to explore swarming in nature by providing platforms with similar computational power as simple insects. We demonstrate the computational capability of the swarm by implementing a fast physics-based robot simulator and using this within a distributed island model evolutionary system, all hosted on the Xpucks.

Mechanisms of cooperation in cancer nanomedicine: towards systems nanotechnology

Nanoparticles are designed to deliver therapeutics and diagnostics selectively to tumors. Their size, shape, charge, material, coating, and cargo determine their individual functionalities. A systems approach could help predict the behavior of trillions of nanoparticles interacting in complex tumor environments. Engineering these nanosystems may lead to biomimetic strategies where interactions between nanoparticles and their environment give rise to cooperative behaviors typically seen in natural self-organized systems. Examples include nanoparticles that communicate the location of a tumor to amplify tumor homing or self-assemble and disassemble to optimize nanoparticle transport. The challenge is to discover which nanoparticle designs lead to a desired system behavior. To this end, novel nanomaterials, deep understanding of biology, and computational tools are emerging as the next frontier.

A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors

Targeted nanoparticles are increasingly being engineered for the treatment of cancer. By design, they can passively accumulate in tumors, selectively bind to targets in their environment, and deliver localized treatments. However, the penetration of targeted nanoparticles deep into tissue can be hindered by their slow diffusion and a high binding affinity. As a result, they often localize to areas around the vessels from which they extravasate, never reaching the deep-seeded tumor cells, thereby limiting their efficacy. To increase tissue penetration and cellular accumulation, we propose generalizable guidelines for nanoparticle design and validate them using two different computer models that capture the potency, motion, binding kinetics, and cellular internalization of targeted nanoparticles in a section of tumor tissue. One strategy that emerged from the models was delaying nanoparticle binding until after the nanoparticles have had time to diffuse deep into the tissue. Results show that nanoparticles that are designed according to these guidelines do not require fine-tuning of their kinetics or size and can be administered in lower doses than classical targeted nanoparticles for a desired tissue penetration in a large variety of tumor scenarios. In the future, similar models could serve as a testbed to explore engineered tissue-distributions that arise when large numbers of nanoparticles interact in a tumor environment.

Identification and characterization of receptor-specific peptides for siRNA delivery

Tumor-targeted delivery of siRNA remains a major barrier in fully realizing the therapeutic potential of RNA interference. While cell-penetrating peptides (CPP) are promising siRNA carrier candidates, they are universal internalizers that lack cell-type specificity. Herein, we design and screen a library of tandem tumor-targeting and cell-penetrating peptides that condense siRNA into stable nanocomplexes for cell type-specific siRNA delivery. Through physiochemical and biological characterization, we identify a subset of the nanocomplex library of that are taken up by cells via endocytosis, trigger endosomal escape and unpacking of the carrier, and ultimately deliver siRNA to the cytosol in a receptor-specific fashion. To better understand the structure-activity relationships that govern receptor-specific siRNA delivery, we employ computational regression analysis and identify a set of key convergent structural properties, namely the valence of the targeting ligand and the charge of the peptide, that help transform ubiquitously internalizing cell-penetrating peptides into cell type-specific siRNA delivery systems.