scholarly journals Modelling nonlinear dendritic processing of facilitation in a dragonfly target-tracking neuron

2021 ◽  
Author(s):  
Bo M. B. Bekkouche ◽  
Patrick A. Shoemaker ◽  
Joseph M. Fabian ◽  
Elisa Rigosi ◽  
Steven D. Wiederman ◽  
...  
Keyword(s):  
Nmda Receptors ◽  
Target Tracking ◽  
Computational Models ◽  
Target Location ◽  
Computational Modelling ◽  
Neuronal Model ◽  
Dendritic Morphology ◽  
Morphological Data ◽  
Motion Processing

AbstractDragonflies are highly skilled and successful aerial predators that are even capable of selectively attending to one target within a swarm. Detection and tracking prey is likely to be driven by small target motion detector (STMD) neurons identified from several insect groups. Prior work has shown that dragonfly STMD responses are facilitated by targets moving on a continuous path, enhancing the response gain at the present and predicted future location of targets. In this study, we combined detailed morphological data with computational modelling to test whether a combination of dendritic morphology combined with the nonlinear properties of NMDA receptors could explain these observations. We developed a hybrid neuronal model of neurons within the dragonfly optic lobe, which integrates numerical and morphological components. The model was able to generate potent facilitation for targets moving on continuous trajectories, including a localized spotlight of maximal sensitivity close to the last seen target location, as also measured duringin vivorecordings. The model did not, however, include a mechanism capable of producing a traveling or spreading wave of facilitation. Our data support a strong role for the high dendritic density seen in the dragonfly neuron in enhancing non-linear facilitation. An alternative model based on morphology of an unrelated type of motion processing neuron from a dipteran fly required more than 3 times higher synaptic gain in order to elicit similar levels of facilitation, despite having only 20% fewer synapses. Our data supports a potential role for NMDA receptors in target tracking and also demonstrates the feasibility of combining biologically plausible dendritic computations with more abstract computational models for basic processing as used in earlier studies.

scholarly journals Modeling Nonlinear Dendritic Processing of Facilitation in a Dragonfly Target-Tracking Neuron

2021 ◽  
Vol 15 ◽  
Author(s):  
Bo M. B. Bekkouche ◽  
Patrick A. Shoemaker ◽  
Joseph M. Fabian ◽  
Elisa Rigosi ◽  
Steven D. Wiederman ◽  
...  
Keyword(s):  
Nmda Receptors ◽  
Target Tracking ◽  
Computational Models ◽  
Target Location ◽  
Dendritic Morphology ◽  
Morphological Data ◽  
Motion Processing ◽  
Nonlinear Properties ◽  
Data Support

Dragonflies are highly skilled and successful aerial predators that are even capable of selectively attending to one target within a swarm. Detection and tracking of prey is likely to be driven by small target motion detector (STMD) neurons identified from several insect groups. Prior work has shown that dragonfly STMD responses are facilitated by targets moving on a continuous path, enhancing the response gain at the present and predicted future location of targets. In this study, we combined detailed morphological data with computational modeling to test whether a combination of dendritic morphology and nonlinear properties of NMDA receptors could explain these observations. We developed a hybrid computational model of neurons within the dragonfly optic lobe, which integrates numerical and morphological components. The model was able to generate potent facilitation for targets moving on continuous trajectories, including a localized spotlight of maximal sensitivity close to the last seen target location, as also measured during in vivo recordings. The model did not, however, include a mechanism capable of producing a traveling or spreading wave of facilitation. Our data support a strong role for the high dendritic density seen in the dragonfly neuron in enhancing non-linear facilitation. An alternative model based on the morphology of an unrelated type of motion processing neuron from a dipteran fly required more than three times higher synaptic gain in order to elicit similar levels of facilitation, despite having only 20% fewer synapses. Our data support a potential role for NMDA receptors in target tracking and also demonstrate the feasibility of combining biologically plausible dendritic computations with more abstract computational models for basic processing as used in earlier studies.

Nanobiosensing with target amplification, and nanoswimmers can be controlled by magnetic fields and facilitate gene therapy and nanomedicine applications

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Magnetic Fields ◽  
Computational Models ◽  
Target Location ◽  
Vascular System ◽  
Target Identification ◽  
Tracking System ◽  
Rotating Magnetic Field ◽  
Three Dimensions ◽  
Learning Approaches

Nanobiosensing with target amplification is one such example. In this scenario, "activator" nanoparticles stimulate the target location, such as a tumor, resulting in spatial amplification of a tumor-triggered phenomenon-of-interest (POI). The typical targeting approach, which relies on the human vascular system to transport nanoparticles, is inefficient and is considered a brute-force search from a computing standpoint. By evaluating the observable properties of these nanoswimmers, which are controlled by magnetic fields created by electromagnetic coils, an external tracking system is utilized to explore the tissue environment. The stochastic movement of numerous loosely connected, disc-shaped components in the system results in deterministic locomotion. When each component is programmed to oscillate omnidirectionally along its radius, expanding and contracting in response to varying environmental signals, the system can collectively locomote towards the source of the environmental signal. The main goal is to enable interoperability while developing multiple simulation components for computational nanobiosensing with different and non-interoperable interfaces. The accuracy of the computational models and algorithms should be tested utilizing multi-physics in silico platforms that simulate the targeting of externally manipulable or self-regulatable nanorobots. To minimize the rates of erroneous and missed detection, "natural" deep learning approaches might be used to train mathematical models for in vivo target identification. The particle stretching approach for creating worm-like structures capable of low-Reynolds-number propulsion when actuated by a rotating magnetic field is one possibility. To replicate the function of an MRI, a sensor array made up of several magnetoresistive sensors might be utilized to precisely place nanorobots. Such systems would also need to be developed in three dimensions, with more complicated locomotive behavior of components and aggregates inside blood flows. In conclusion, computational nanobiosensing is to improve in vivo POI targeting and understanding of POI-induced gradients.

scholarly journals Computational modelling of muscle fibre operating ranges in the hindlimb of a small ground bird (Eudromia elegans), with implications for modelling locomotion in extinct species

2021 ◽  
Vol 17 (4) ◽  
pp. e1008843
Author(s):  
Peter J. Bishop ◽  
Krijn B. Michel ◽  
Antoine Falisse ◽  
Andrew R. Cuff ◽  
Vivian R. Allen ◽  
...  
Keyword(s):  
Muscle Function ◽  
Computational Models ◽  
Fibre Length ◽  
Computational Modelling ◽  
Length Change ◽  
Muscle Fibres ◽  
Extinct Species ◽  
Length Changes ◽  
Experimental Findings

The arrangement and physiology of muscle fibres can strongly influence musculoskeletal function and whole-organismal performance. However, experimental investigation of muscle function during in vivo activity is typically limited to relatively few muscles in a given system. Computational models and simulations of the musculoskeletal system can partly overcome these limitations, by exploring the dynamics of muscles, tendons and other tissues in a robust and quantitative fashion. Here, a high-fidelity, 26-degree-of-freedom musculoskeletal model was developed of the hindlimb of a small ground bird, the elegant-crested tinamou (Eudromia elegans, ~550 g), including all the major muscles of the limb (36 actuators per leg). The model was integrated with biplanar fluoroscopy (XROMM) and forceplate data for walking and running, where dynamic optimization was used to estimate muscle excitations and fibre length changes throughout both gaits. Following this, a series of static simulations over the total range of physiological limb postures were performed, to circumscribe the bounds of possible variation in fibre length. During gait, fibre lengths for all muscles remained between 0.5 to 1.21 times optimal fibre length, but operated mostly on the ascending limb and plateau of the active force-length curve, a result that parallels previous experimental findings for birds, humans and other species. However, the ranges of fibre length varied considerably among individual muscles, especially when considered across the total possible range of joint excursion. Net length change of muscle–tendon units was mostly less than optimal fibre length, sometimes markedly so, suggesting that approaches that use muscle–tendon length change to estimate optimal fibre length in extinct species are likely underestimating this important parameter for many muscles. The results of this study clarify and broaden understanding of muscle function in extant animals, and can help refine approaches used to study extinct species.

scholarly journals Evaluation of a modified Cheatham-Platinum stent for the treatment of aortic coarctation by finite element modelling

2018 ◽  
Vol 7 ◽  
pp. 204800401877395 ◽  
Author(s):  
Barbara EU Burkhardt ◽  
Nicholas Byrne ◽  
Marí Nieves Velasco Forte ◽  
Francesco Iannaccone ◽  
Matthieu De Beule ◽  
...  
Keyword(s):  
Finite Element ◽  
Aortic Coarctation ◽  
Computational Models ◽  
Three Dimensional ◽  
Computational Modelling ◽  
Patient Specific ◽  
Stent Design ◽  
Initial Length ◽  
Element Analysis

Objectives Stent implantation for the treatment of aortic coarctation has become a standard approach for the management of older children and adults. Criteria for optimal stent design and construction remain undefined. This study used computational modelling to compare the performance of two generations of the Cheatham-Platinum stent (NuMED, Hopkinton, NY, USA) deployed in aortic coarctation using finite element analysis. Design Three-dimensional models of both stents, reverse engineered from microCT scans, were implanted in the aortic model of one representative patient. They were virtually expanded in the vessel with a 16 mm balloon and a pressure of 2 atm. Results The conventional stent foreshortened to 96.5% of its initial length, whereas the new stent to 99.2% of its initial length. Diameters in 15 slices across the conventional stent were 11.6–15 mm (median 14.2 mm) and slightly higher across the new stent: 10.7–15.3 mm (median 14.5 mm) (p= 0.021). Apposition to the vessel wall was similar: conventional stent 31.1% and new stent 28.6% of total stent area. Conclusions The new design Cheatham-Platinum stent showed similar deployment results compared to the conventional design. The new stent design showed slightly higher expansion, using the same delivery balloon. Patient-specific computational models can be used for virtual implantation of new aortic stents and promise to inform subsequent in vivo trials.

scholarly journals The Digital Mouse: why computational modelling of mouse models of disease can improve translation

2020 ◽  
Author(s):  
Natal van Riel ◽  
Ralph Müller ◽  
Enrico Dall’Ara
Keyword(s):  
Animal Models ◽  
Chronic Diseases ◽  
In Silico ◽  
Computational Models ◽  
Frailty Index ◽  
Computational Modelling ◽  
List Type ◽  
Age Related ◽  
Experimental Findings

AbstractComputational models can be used to study the mechanistic phenomena of disease. Current mechanistic computer simulation models mainly focus on (patho)physiology in humans. However, often data and experimental findings from preclinical studies are used as input to develop such models. Biological processes underlying age-related chronic diseases are studied in animal models. The translation of these observations to clinical applications is not trivial. As part of a group of international scientists working in the COST Action network MouseAGE, we argue that in order to boost the translation of pre-clinical research we need to develop accurate in silico counterparts of the in vivo animal models. The Digital Mouse is proposed as framework to support the development of evidence-based medicine, for example to develop geroprotectors, which are drugs that target fundamental mechanisms of ageing.HighlightsComputational modelling of human (patho)physiology is advancing rapidly, often using and extrapolating experimental findings from preclinical disease models.The lack of in silico models to support in vivo modelling in mice is a missing link in current approaches to study complex, chronic diseases.The development of mechanistic computational models to simulate disease in mice can boost the discovery of novel therapeutic interventions.The ‘Digital Mouse’ is proposed as a framework to implement this ambition. The development of a Digital Mouse Frailty Index (DM:FI) to study aging and age-related diseases is provided as an example.

Computational Modelling in Shoulder Biometrics

2011 ◽  
pp. 348-384
Author(s):  
David C. Ackland ◽  
Cheryl J. Goodwin ◽  
Marcus G. Pandy
Keyword(s):  
Computational Models ◽  
Computational Modelling ◽  
In Vitro Techniques ◽  
Emerging Trends ◽  
Modelling Techniques ◽  
Shoulder Complex ◽  
Complex Movement

The objectives of this chapter are as follows. First, a background in anatomy and biomechanics of the shoulder complex is presented to provide a brief review of the essential functions of the shoulder. Second, important features of practical shoulder models are discussed with reference to capabilities of current computational modelling techniques. Third, techniques in computational modelling of the shoulder complex are compared and contrasted for their effectiveness in representing shoulder biomechanics in situ, with some sample calculations included. Fourth, in vivo and in vitro techniques for verifying computational models will be briefly reviewed. Finally, a summary of emerging trends will indicate the clinical impact that computational modelling can be expected to have in progressing our understanding of shoulder complex movement and its fundamental biomechanics.

scholarly journals Synaptic mechanisms underlying the network state-dependent recruitment of VIP-expressing interneuron-specific interneurons in the CA1 hippocampus

2018 ◽  
Author(s):  
Xiao Luo ◽  
Alexandre Guet-McCreight ◽  
Vincent Villette ◽  
Ruggiero Francavilla ◽  
Beatrice Marino ◽  
...  
Keyword(s):  
Computational Models ◽  
Computational Modelling ◽  
Theta Oscillations ◽  
Network Activity ◽  
Network Oscillations ◽  
State Dependent ◽  
Speed Modulation ◽  
Theoretical Predictions ◽  
Synaptic Mechanisms

SUMMARYIn the hippocampus, a highly specialized population of vasoactive intestinal peptide (VIP)-expressing interneuron-specific (IS) inhibitory cells provides local circuit disinhibition via preferential innervation of different types of GABAergic interneurons. While disinhibition can be critical in modulating network activity and different forms of hippocampal learning, the synaptic and integrative properties of IS cells and their recruitment during network oscillations remain unknown. Using a combination of patch-clamp recordings, photostimulation, computational modelling as well as recordings of network oscillations simultaneously with two-photon Ca2+-imaging in awake mice in vivo, we identified synaptic mechanisms that can control the firing of IS cells, and explored their impact on the cell recruitment during theta oscillations and sharp-wave-associated ripples. We found that IS cells fire spikes in response to both the Schaffer collateral and the temporoammonic pathway activation. Moreover, integrating their intrinsic and synaptic properties into computational models predicted recruitment of these cells during the rising to peak phases of theta oscillations and during ripples depending on inhibitory contributions. In vivo Ca2+-imaging in awake mice confirmed in part the theoretical predictions, revealing a significant speed modulation of IS cells and their preferential albeit delayed recruitment during theta-run epochs, with firing at the rising phase to peak of the theta cycle. However, it also uncovered that IS cells are not activated during ripples. Thus, given the preferential theta-modulated firing of IS cells in awake hippocampus, we postulate that these cells may be important for information gating during spatial navigation and memory encoding.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

Current trends in mathematical modeling of tumor–microenvironment interactions: a survey of tools and applications

2010 ◽  
Vol 235 (4) ◽  
pp. 411-423 ◽  
Author(s):  
Katarzyna A Rejniak ◽  
Lisa J McCawley
Keyword(s):  
Mathematical Modeling ◽  
Protein Interactions ◽  
Computational Models ◽  
Chemical Species ◽  
Tumor Stroma ◽  
Cellular Functions ◽  
Complex Interactions ◽  
Expanding Population

In its simplest description, a tumor is comprised of an expanding population of transformed cells supported by a surrounding microenvironment termed the tumor stroma. The tumor microcroenvironment has a very complex composition, including multiple types of stromal cells, a dense network of various extracellular matrix (ECM) fibers interpenetrated by the interstitial fluid and gradients of several chemical species that either are dissolved in the fluid or are bound to the ECM structure. In order to study experimentally such complex interactions between multiple players, cancer is dissected and considered at different scales of complexity, such as protein interactions, biochemical pathways, cellular functions or whole organism studies. However, the integration of information acquired from these studies into a common description is as difficult as the disease itself. Computational models of cancer can provide cancer researchers with invaluable tools that are capable of integrating the complexity into organizing principles as well as suggesting testable hypotheses. We will focus in this Minireview on mathematical models in which the whole cell is a main modeling unit. We will present a current stage of such cell-focused mathematical modeling incorporating different stromal components and their interactions with growing tumors, and discuss what modeling approaches can be undertaken to complement the in vivo and in vitro experimentation.

Hybrid Rigid-Deformable Model for Prediction of Neighboring Intervertebral Disk Loads During Flexion Movement After Lumbar Interbody Fusion at L3–4 Level

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Tien Tuan Dao
Keyword(s):  
Lumbar Spine ◽  
Computational Models ◽  
Interbody Fusion ◽  
Clinical Decision ◽  
Intervertebral Disk ◽  
Design Guideline ◽  
Spinal Loads ◽  
Stress Changes ◽  
Stress Behaviors

Knowledge of spinal loads in neighboring disks after interbody fusion plays an important role in the clinical decision of this treatment as well as in the elucidation of its effect. However, controversial findings are still noted in the literature. Moreover, there are no existing models for efficient prediction of intervertebral disk stresses within annulus fibrosus (AF) and nucleus pulposus (NP) regions. In this present study, a new hybrid rigid-deformable modeling workflow was established to quantify the mechanical stress behaviors within AF and NP regions of the L1–2, L2–3, and L4–5 disks after interbody fusion at L3–4 level. The changes in spinal loads were compared with results of the intact model without interbody fusion. The fusion outcomes revealed maximal stress changes (10%) in AF region of L1–2 disk and in NP region of L2–3 disk. The minimal stress change (1%) is noted at the NP region of the L1–2 disk. The validation of simulation outcomes of fused and intact lumbar spine models against those of other computational models and in vivo measurements showed good agreements. Thus, this present study may be used as a novel design guideline for a specific implant and surgical scenario of the lumbar spine disorders.

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