Touchable Computing: Computing-Inspired Bio-Detection

Nanoswimmers statistical mechanics: Unlocking whole-blood viscosity sensing for tumor microenvironment

BACKGROUND AND OBJECTIVE

The ability to sense the biological microenvironment surrounding early-stage tumor tissues is critical for tumorigenesis tracing and tumor detection and treatment. An efficient tumor microenvironment (TME) sensing strategy remains a significant challenge. We propose a novel "seeing is sensing" approach that has the potential to discern the whole-blood viscosity (WBV) information of the TME by using a swarm of nanoswimmers (NS).

METHODS

In this study, we employ statistical mechanics methods to derive the relationship between the aggregation of NS in the microscale and their macroscopic concentration distribution. We utilize the finite difference method to develop a discrete numerical model of the NS diffusion in the blood. We further develop a novel model for TME sensing that can dynamically detect the WBV by analyzing the kinematic motion of the NS swarm, which enables real-time detection of WBV by observing the Full Width at Half Maximum (FWHM) of the NS swarm motion.

RESULTS

The viscosity value obtained with our sensing method is benchmarked against the gold standard results obtained from the Brookfield viscometer. The measurements obtained from both methods exhibit an excellent correlation, with a coefficient of determination (R2) of 0.9617. Furthermore, the constructed Bland-Altman plot reveals that the majority of observed data points lie within the limits of agreement of the 95% clinical confidence interval (lower limit of agreement = -0.0660, upper limit of agreement = 0.1130), thus validating the feasibility of our sensing strategy.

CONCLUSIONS

We present a new sensing strategy that utilizes the diffusion dynamics of the NS swarm within the vascular network to infer variations in WBV. Comparative analysis with gold standard data substantiates the accuracy and applicability of this method in assessing WBV parameters in the vicinity of tumor tissues. Our work demonstrates relevant prospects for visualizing, comprehending, and evaluating the pathological progression of blood-related disorders in real-time.

Tension-Relaxation In Vivo Computing Principle for Tumor Sensitization and Targeting

By modeling the tumor sensitization and targeting (TST) as a natural computational process, we have proposed the framework of nanorobots-assisted in vivo computation. The externally manipulable nanorobots are steered to detect the tumor in the high-risk tissue, which is analogous to the process of searching for the optimal solution by the computing agents in the search space. To overcome the constraint of a nanorobotic platform that can only generate a uniform magnetic field to actuate the nanorobots, we have proposed the weak priority evolution strategy (WP-ES) in our previous works. However, these works do not consider the proportions of the nanorobot control and tracking operations, which are part and parcel of in vivo computation as the control operation aims at searching for the tumor effectively while the tracking mode is used for gathering information about the biological gradient function (BGF). Careful planning about the durations spent in these operations is needed for optimal performance of the TST strategy. To account for this issue, in the current article, we propose a novel computational principle, called the tension-relaxation (T-R) principle, to balance the displacements of nanorobots during each control and tracking cycle. Furthermore, we build three tumor vascular models with different sizes to represent three different targeting regions as the morphology of tumor vasculature determined by the tumor growth process is an important factor affecting TST. We carry out the computational experiments for tumors with three different sizes for several representative landscapes by introducing the T-R principle into the WP-ES-based swarm intelligence algorithms and considering the realistic internal constraints. The experimental outcomes demonstrate the effectiveness of the proposed TST strategy.

In vivo computation with sensor fusion and search acceleration for smart tumor homing

BACKGROUND AND OBJECTIVE

Motivated by the advancements on bioresorbable nanoswimmers, this paper considers the advantages of direct targeting over systemic targeting for smart tumor homing under the general framework of computational nanobiosensing. Nanoswimmers assembled by magnetic nanoparticles can be used as contrast agents to estimate the locations of tumors inside the human body.

METHODS

Closely observing the response of nanoswimmers (which act as in vivo biosensors) to the tumor-triggered biological gradients and then guiding them through external manipulation, can result in a higher accumulation at the diseased location. Sensor informatics along with data fusion can play a crucial role in such a knowledge-aided targeting process. Specifically, built upon our previous work on direct targeting inspired by the gradient descent optimization, this work is focused on resolving the real-life constraints of in vivo natural computation such as uniformity of the magnetic field and finite life span of the nanoswimmers. To overcome these challenges, we propose a multi-estimate-fusion strategy to obtain a common steering direction for the swarm of nanoswimmers.

RESULTS

We show through computational experiments (1) that the mean of individual gradient estimations provides the best choice for symmetrical conditions (tumor location in line with the direction of blood flow) while leader-based swarm steering gives the best results for non-symmetrical search space, and (2) that the iterative memory-driven gradient descent optimization detects the target faster compared to the classical memory-less gradient descent and knowledge-less systemic targeting.

CONCLUSION

Our proposed strategies demonstrate that a clear demarcation between malignant tumors and healthy tissues can be visualized before nanoswimmers are consumed in human vasculature. We believe that our work will help in overcoming the challenges posed by natural in vivo computation for tumor diagnosis at its early stage.

In Vivo Computing Strategies for Tumor Sensitization and Targeting

Several evolution strategies for in vivo computation are proposed with the aim of realizing tumor sensitization and targeting (TST) by externally manipulable nanoswimmers. In such targeting systems, nanoswimmers assembled by magnetic nanoparticles are externally manipulated to search for the tumor in the high-risk tissue by a rotating magnetic field produced by a coil system. This process can be interpreted as in vivo computation, where the tumor in the high-risk tissue corresponds to the global maximum or minimum of the in vivo optimization problem, the nanoswimmers are seen as the computational agents, the tumor-triggered biological gradient field (BGF) is used for fitness evaluation of the agents, and the high-risk tissue is the search space. Considering that the state-of-the-art magnetic nanoswimmer control method can only actuate all the nanoswimmers heading in the same direction simultaneously, we introduce the orthokinetic movement strategies into the agent location updating in the existing swarm intelligence algorithms. Especially, the gravitational search algorithm (GSA) is revisited and the corresponding in vivo optimization algorithm called orthokinetic GSA (OGSA) is proposed to carry out the TST. Furthermore, to determine the direction of the orthokinetic agent movement in every iteration of the operation, we propose several strategies according to the fitness ranking of the nanoswimmers in the BGF. To verify the superiority of the OGSA and choose the optimal evolution strategy, some numerical experiments are presented and compared with that of the brute-force search, which represents the traditional method for TST. It is found that the TST performance can be improved by the weak priority evolution strategy (WP-ES) in most of the scenarios.

NGA-Inspired Nanorobots-Assisted Detection of Multifocal Cancer

We propose a new framework of computing-inspired multifocal cancer detection procedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected are regarded as solutions of the objective function, the tissue region around the cancer areas represents the parameter space, and the nanorobots loaded with contrast medium molecules for cancer detection correspond to the optimization agents. The process that the nanorobots detect tumors by swimming in the high-risk tissue region can be regarded as the process that the agents search for the solutions of an objective function in the parameter space with some constraints. For multimodal optimization (MMO) aiming to locate multiple optimal solutions in a single simulation run, the niche technology has been widely used. Specifically, the niche genetic algorithm (NGA) has been shown to be particularly effective in solving MMO. It can be used to identify the global optima of multiple hump functions in a running, effectively keep the diversity of the population, and prematurely avoid the genetic algorithm. Learning from the optimization procedure of NGA, we propose the NGA-inspired MCDP in order to locate the tumor targets efficiently while taking into account realistic in vivo propagation and controlling of nanorobots, which is different from the use scenario of the standard NGA. To improve the performance of the MCDP, we also modify the crossover operator of the original NGA from crossing within a population to crossing between two populations. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the NGA-inspired MCDP when the biological objective function is associated with the blood flow velocity profile caused by tumor-induced angiogenesis.

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

We have proposed a new tumor sensitization and targeting (TST) framework, named in vivo computation, in our previous investigations. The problem of TST for an early and microscopic tumor is interpreted from the computational perspective with nanorobots being the "natural" computing agents, the high-risk tissue being the search space, the tumor targeted being the global optimal solution, and the tumor-triggered biological gradient field (BGF) providing the aided knowledge for fitness evaluation of nanorobots. This natural computation process can be seen as on-the-fly path planning for nanorobot swarms with an unknown target position, which is different from the traditional path planning methods. Our previous works are focusing on the TST for a solitary lesion, where we proposed the weak priority evolution strategy (WP-ES) to adapt to the actuating mode of the homogeneous magnetic field used in the state-of-the-art nanorobotic platforms, and some in vitro validations were performed. In this paper, we focus on the problem of TST for multifocal tumors, which can be seen as a multimodal optimization problem for the "natural" computation. To overcome this issue, we propose a sequential targeting strategy (Se-TS) to complete TST for the multiple lesions with the assistance of nanorobot swarms, which are maneuvered by the external actuating and tracking devices according to the WP-ES. The Se-TS is used to modify the BGF landscape after a tumor is detected by a nanorobot swarm with the gathered BGF information around the detected tumor. Next, another nanorobot swarm will be employed to find the second tumor according to the modified BGF landscape without being misguided to the previous one. In this way, all the tumor lesions will be detected one by one. In other words, the paths of nanorobots to find the targets can be generated successively with the sequential modification of the BGF landscape. To demonstrate the effectiveness of the proposed Se-TS, we perform comprehensive simulation studies by enhancing the WP-ES based swarm intelligence algorithms using this strategy considering the realistic in-body constraints. The performance is compared against that of the "brute-force" search, which corresponds to the traditional systemic tumor targeting, and also against that of the standard swarm intelligence algorithms from the algorithmic perspective. Furthermore, some in vitro experiments are performed by using Janus microparticles as magnetic nanorobots, a two-dimensional microchannel network as the human vasculature, and a magnetic nanorobotic control system as the external actuating and tracking system. Results from the in silico simulations and in vitro experiments verify the effectiveness of the proposed Se-TS for two representative BGF landscapes.

A Feasibility Study of In Vivo Control and Tracking of Microrobot Using Taxicab Geometry for Direct Drug Targeting

In vivo direct drug targeting aims at delivering drug molecules loaded on microrobots to the diseased site using the shortest possible physiological routes, which potentially improves targeting efficiency and reduces systemic toxicity. It is thus essential to consider realistic in-body limitations for direct drug targeting applications. Here, we present a novel controller for microrobot maneuver by considering four key in vivo constraints: non-Euclidean structure of capillaries, irreversibility of blood flow, invisibility of microvasculature, and inaccuracy of microrobot tracking. We use the taxicab geometry of capillaries as the a priori knowledge for steering and tracking a microrobot in lattice-like vessels. Furthermore, we introduce a minimax repulsive boundary function to prevent the microrobot from getting too close to the boundaries imposed by the direction of blood flow. We also propose a novel Kalman filtering algorithm to reduce tracking error, while avoiding possible obstacles such as vessel walls without knowing their actual locations. The proposed control method consists of four modules, namely a model predictive control module for tumor targeting, a Kalman filtering module for microrobot tracking, a blind obstacle detection module, and a vessel structure estimation module. The interplay of these four modules offers successful maneuver and tracking of the microrobot while avoiding obstacles in a blind manner by utilizing the taxicab geometry of blood vessels. We present a comprehensive in silico simulation study to verify our designed controller.

Blind Obstacle Avoidance Using Taxicab Geometry for NanorobotAssisted Direct Drug Targeting

In this paper, we present a novel controller for steering nanorobots in lattice-like vessel systems while avoiding potential obstacles such as the vessel walls without any prior knowledge of the obstacles' positions. The proposed control method consists of two sub-modules, namely a blind obstacle avoidance (BOA) and a model predictive control (MPC). In the case that a nanorobot might encounter an obstacle on its path, the BOA module is activated, which gives rise to a desirable heading angle to change the direction of the nanorobot's movement to bypass the obstacle. On the other hand, the MPC module offers a series of actuating field's directions that control the nanorobots' movement in the blood vessel with a grid structure representing potential paths of vascular growth, and introduces a repulsive boundary function to stop nanorobots from getting too close to the boundaries. This new formulation offers successful control and steering of nanorobots while avoiding obstacles in a blind manner by taking into account realistic in vivo physical constraints. Simulation results demonstrate the effectiveness of the proposed feedback control design.

Perspective: Computational Nanobiosensing

With the development of nanotechnology, externally manipulable or self-regulatable smart nanosystems can be utilized as effective tools for computational nanobiosensing, where natural computing strategies are exploited for knowledge-aided nanobiosensing.

Latticed Channel Model of Touchable Communication Over Capillary Microcirculation Network

Recent progress on bioresorbable and bio-compatible miniature systems provides prospects for developing novel nanorobots operating inside the human body. These nanoscale systems are expected to dissolve in vivo and cause no side effect after completing their tasks. Motivated by these advancements, we have developed the analytical framework of touchable molecular communication (TouchCom) to describe the process of direct drug targeting (DDT) using externally controllable nanorobots. Built upon our previous work, we develop a novel latticed channel model of TouchCom for an interconnected capillary network near a targeted tumor area. Specifically, we propose a two-dimensional grid to synthesize the microcirculation environment, which is used to describe the propagation process of nanorobots. Furthermore, by applying the concept of multiple-input multiple-output (MIMO) systems in wireless communications to the therapeutic window in cancer treatment, we propose a MIMO DDT strategy in the latticed channel to enhance the targeting efficiency while minimizing the adverse effect of drug toxicity. Based on the proposed model, we study the influence of blood flow direction on the efficiency of DDT, and introduce a compensation strategy with the help of an external guiding field to mitigate the misalignment between the direction of blood flow and the tumor location.

Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization

We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biological gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumor-targeted "activator" nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the "brute-force" search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biological gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

Biosensing by Learning: Cancer Detection as Iterative optimization

We propose a novel cancer detection procedure (CDP) based on an iterative optimization method. The global minimum of a tumor-induced biological cost function indicates the tumor location, the domain of the cost function is the tissue region at high risk of malignancy, and the time-variant guess input is a swarm of externally controllable and trackable nanorobots for tumor sensing. We consider the spatial distrib-ution of fibrin as the cost function; the fibrin is formed during the coagulation cascade activated by tumor-targeted signalling modules (nanoparticles) and recruits clot-targeted receiving modules (nanorobots) towards the site of disease. Subsequently, the CDP can be interpreted from the iterative optimization perspective: the guess input (i.e., a swarm of nanorobots) is continuously updated according to the gradient of the cost function in order to find the optimum (i.e., cancer) by moving through the domain (i.e., tissue under screening). Along this line of thought, we consider the gradient descent (GD) iterative method, and propose the GD-inspired CDP, which takes into account the realistic in vivo propagation scenario of nanorobots. Finally, we present numerical examples to demonstrate the features of the GD-inspired CDP.