Simulation of hyperelastic materials in real-time using Deep Learning

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm. We show that deep learning, one of the latest machine learning methods based on artificial neural networks, can enhance computational mechanics through its ability to encode highly non-linear models in a compact form. Our method is applied to two benchmark examples: a cantilever beam and an L-shape subject to moving punctual loads. A comparison between our method and proper orthogonal decomposition (POD) is done through the paper. The results show that U-Mesh can perform very fast simulations on various geometries, mesh resolutions and number of input forces with very small errors

Diffuse reflectance spectroscopy as a tool to evaluate liver tissue

The shape of a diffuse reflectance spectra can provide knowledge about tissue composition. By analysing this spectra one can, with the right evaluation methods, find out information about the tissue. With known absorption properties of the present tissue chromophores it is possible, with a curve fit algorithm, to find their volume fractions in any measured tissue. In this work, the usability of diffuse reflectance spectroscopy (DRS) as a tool in tissue diagnostics was investigated. A model based on diffusion theory with a semi-infinite boundary condition and known tissue chromophore data was developed. It was used to evaluate DRS signals in tissue phantoms and on murinae, porcine and human livers. To ease the evaluation, an interface was developed to run the instruments (one light source and two spectrometers) from the computer and to compute the tissue composition from the provided diffusion model. The tissue composition was computed through a Levenberg-Marquardt least-squares algorithm in MATLAB. Validation of the diffusion model was performed on tissue phantoms together with Monte Carlo (MC) simulations and a Time-of-Flight Spectroscopy (TOFS) measurement. The results from the validation measurements were varying. Many different kinds of phantoms were created. Commonly used phantoms, containing water, blood and intralipid gave results systematically underestimating the absorption while phantoms with greater fractions of lipid, mixed with agar or Triton-X100, gave results with less good correlation between signals and concentrations. The poor phantom results were discovered to be due to a low haemoglobin concentration in the used blood whilst there was also limitations in the diffusion criteria for these phantoms. The results from liver measurements gave better correlation between signals and concentrations, and when investigating the criteria for diffusion these were also fulfilled in a greater wavelength region. The liver results also showed an evident difference between healthy and malignant tissue.I dagens sjukvård finns det många etablerade metoder för att diagnostisera sjukdomar. Vissa av dessa är avbildningstekniker som PET, CT eller MR medan andra, som biopsi, är en annan typ av undersökning som utförs. Dessa metoder har både för- och nackdelar. Gemensamt för dem är att de antingen är dyra, potentiellt kan ha bieffekter eller tar lång tid. Sjukvården har många alternativ, men vilket är egentligen det bästa? Optiska metoder är inte lika frekvent utnyttjade som diagnostiska verktyg. De är kraftfulla tekniker som varken gör skada, tar tid eller kostar mycket pengar. Genom att i stället använda sig av optiska metoder för diagnostik och terapi kan man både reducera kostnad, tid och i vissa fall förbättra den diagnostiska informationen. Självklart finns det inte endast positiva saker med de optiska metoderna, en stor nackdel är att ljuset inte når långt ner i vävnaden. Fotonerna når endast en bråkdel av en millimeter till någon centimeter ner i vävnaden, beroende på vilken färg ljuset har. En optisk metod för att avgöra om viss vävnad är frisk eller skadad bygger på diffus reflektans. Genom att mäta den diffusa reflektansen på specifik vävnad kan man sedan räkna ut vilka komponenter som finns, samt till vilken grad de är närvarande. Detta tillsammans med kunskap om komposition om frisk samt skadad vävnad kan bidra till att ställa en diagnos. Diffus reflektans har i det här projektet används till att karakterisera vävnad i levern. Applikationerna finns både genom att tekniken kan använadas som ett guidande hjälpmedel under kirurgi samt vid undersökning av kemoterapiskador. I den här rapporten beskrivs den underliggande teorin för hur ljus och vävnad växelverkar med varandra. Med detta som bakgrund har en modell för diffus reflektans utvecklats. Utifrån modellen valideras och evalueras instrument, i form av en optisk prob, en ljuskälla och två spektrometrar. Valideringen har också inkluderat metoder för att karakterisera levervävnad genom en fantomstudie samt genom mätningar på olika levrar. Modellen har också testats genom datorsimuleringar. Valideringsmätningar av blodfantomerna gav varierande resultat vilket troligen berodde på låga hemoglobinvärden i blodet som användes samt begränsningar i diffusion teori. Levermätningarna gav bra resultat med en tydlig skillnad mellan hälsosam och sjuk vävnad

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

International audienceIn computer-aided interventions, biomechanical models reconstructed from the pre-operative data are used via augmented reality to facilitate the intra-operative navigation. The predictive power of such models highly depends on the knowledge of boundary conditions. However , in the context of patient-specific modeling, neither the pre-operative nor the intra-operative modalities provide a reliable information about the location and mechanical properties of the organ attachments. We present a novel image-driven method for fast identification of boundary conditions which are modelled as stochastic parameters. The method employs the reduced-order unscented Kalman filter to transform in real-time the probability distributions of the parameters, given observations extracted from intra-operative images. The method is evaluated using synthetic, phantom and real data acquired in vivo on a porcine liver. A quantitative assessment is presented and it is shown that the method significantly increases the predictive power of the biomechanical model

Data-driven simulation for augmented surgery

International audienceTo build an augmented view of an organ during surgery, it is essential to have a biomechanical model with appropriate material parameters and boundary conditions , able to match patient specific properties. Adaptation to the patient's anatomy is obtained by exploiting the image-rich context specific to our application domain. While information about the organ shape, for instance, can be obtained preoper-atively, other patient-specific parameters can only be determined intraoperatively. To this end, we are developing data-driven simulations, which exploit information extracted from a stream of medical images. Such simulations need to run in real-time. To this end we have developed dedicated numerical methods, which allow for real-time computation of finite element simulations. The general principle consists in combining finite element approaches with Bayesian methods or deep learning techniques, that allow to keep control over the underlying computational model while allowing for inputs from the real world. Based on a priori knowledge of the mechanical behavior of the considered organ, we select a constitutive law to model its deformations. The predictive power of such constitutive law highly depends on the knowledge of the material parameters and A. Mendizaba

Identification et caractérisation des conditions aux limites pour des simulations biomécaniques patient-spécifiques

The purpose of the work is to find a way to estimate the boundary conditions of the liver. They play an essential role in forming the predictive capacity of the biomechanical model, but are presented mainly by ligaments, vessels, and surrounding organs, the properties of which are "patient specific" and cannot be measured reliably. We propose to present the boundary conditions as nonlinear springs and estimate their parameters. Firstly, we create a generalized initial approximation using the constitutive law available in the literature and a statistical atlas, obtained from a set of models with segmented ligaments. Then, we correct the approximation based on the nonlinear Kalman filtering approach, which assimilates data obtained from a modality during surgical intervention. To assess the approach, we performed experiments for both synthetic and real data. The results show a certain improvement in simulation accuracy for the cases with estimated boundaries.L'objectif de ce travail est trouvé un moyen d'estimer les conditions aux limites du foie. Elles jouent un rôle essentiel dans la capacité de prédiction du modèle biomécanique, mais sont principalement présentées par les ligaments, les vaisseaux et les organes environnants, dont les propriétés sont "spécifiques au patient" et ne peuvent être mesurées fidèlement. Nous proposons de présenter ces conditions comme des ressorts non linéaires et d'estimer ses paramètres. D’abord, nous créons une approximation initiale en utilisant la loi constitutive disponible dans la littérature et un atlas statistique obtenu à partir des modèles avec des ligaments segmentés. Après, nous la corrigeons basée sur le filtrage de Kalman non linéaire, qui assimile les données acquises d'une modalité pendant la chirurgie. Pour évaluation, nous avons réalisé des expériences avec des données synthétiques et réelles. Les résultats montrent une amélioration de la précision pour les cas avec des limites estimées

Acoustic Communication for Medical Nanorobots

Communication among microscopic robots (nanorobots) can coordinate their activities for biomedical tasks. The feasibility of in vivo ultrasonic communication is evaluated for micron-size robots broadcasting into various types of tissues. Frequencies between 10MHz and 300MHz give the best tradeoff between efficient acoustic generation and attenuation for communication over distances of about 100 microns. Based on these results, we find power available from ambient oxygen and glucose in the bloodstream can readily support communication rates of about 10,000 bits/second between micron-sized robots. We discuss techniques, such as directional acoustic beams, that can increase this rate. The acoustic pressure fields enabling this communication are unlikely to damage nearby tissue, and short bursts at considerably higher power could be of therapeutic use.Comment: added discussion of communication channel capacity in section

Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles

We investigate the usefulness of quantitative ultrasound (QUS) and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the nerves were extracted to prepare histology sections. 85 fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin, and ultrasound data to calculate backscatter coefficient (-24.89 dB $\pm$ 8.31), attenuation coefficient (0.92 db/cm-MHz $\pm$ 0.04), Nakagami parameter (1.01 $\pm$ 0.18) and entropy (6.92 $\pm$ 0.83), as well as B-mode texture features obtained via the gray level co-occurrence matrix algorithm. Significant Spearman's rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R=-0.68), entropy (R=-0.51), and for several texture features. Our study demonstrates that QUS may potentially provide information on structural components of nerve fascicles

Experimental and computational biomedicine : Russian Conference with International Participation in memory of Professor Vladimir S. Markhasin : abstract book

Toward 100 Anniversary of I. P. Pavlov's Physiological Society.The volume contains the presentations that were made during Russian conference with international participation "Experimental and Computational Biomedicine" dedicated to corresponding member of RAS V.S. Markhasin (Ekaterinburg, April 10‒12, 2016). The main purpose of the conference is the discussion of the current state of experimental and theoretical research in biomedicine. For a wide range of scientists, as well as for lecturers, students of the biological and medical high schools.Сборник содержит тезисы докладов, представленных на российской конференции с международным участием «Экспериментальная и компьютерная биомедицина», посвященной памяти члена‐корреспондента РАН В. С. Мархасина (г. Екатеринбург, 10‒12 апреля 2016 г.). Основной целью конференции является обсуждение современного состояния экспериментальных и теоретических исследований в области биомедицины. Сборник предназначен для ученых, преподавателей, студентов и аспирантов биологического и медицинского профиля.МАРХАСИН ВЛАДИМИР СЕМЕНОВИЧ (1941-2015)/ MARKHASIN VLADIMIR SEMENOVICH (1941-2015). [3] PROGRAMM COMMITTEE. [5] ORGANIZING COMMITTEE. [6] KEYNOTE SPEAKERS. [7] CONTENTS. [9] PLENARY LECTURES. [10] Fedotov S. Non-Markovian random walks and anomalous transport in biology. [10] Hoekstra A. Multiscale modelling in vascular disease. [10] Kohl P. Systems biology of the heart: why bother? [10] Meyerhans A. On the regulation of virus infection fates. [11] Panfilov A.V., Dierckx H., Kazbanov I., Vandersickel N. Systems approach to studying mechanisms of ventricular fibrillationusing anatomically accurate modeling. [11] Revishvili A.S. Atrial fibrillation. Noninvasive diagnostic and treatment:from fundamental studies to clinical practice. [12] Rice J. Life sciences research at IBM. [12] Roshchevskaya I.M., Smirnova S., Roshchevsky M.P. Regularities of the depolarization of an atria:an experimental comparative-physiological study. [12] Rusinov V.L., Chupahin O.N., Charushin V.N Scientific basis for development of antiviral drugs. [13] Solovyova O.E. Tribute Lecture. Mechano-electric heterogeneity of the myocardiumas a paradigm of its function. [13] Veksler V. Myocardial energy starvation in chronic heart failure:perspectives for metabolic therapy. [13] Wladimiroff J.W. Fetal cardiac assessment using new methodsof ultrasound examination. [14] Yushkov B.G., Chereshnev V.A. The important questions of regeneration theory. [14] EXPERIMENTAL AND COMPUTATIONAL MODELS IN CARDIOVASCULARPHYSIOLOGY AND CARDIOLOGY. [15] EXPERIMENTAL AND COMPUTATIONAL MODELS IN CARDIOVASCULARPHYSIOLOGY AND CARDIOLOGY. [15] Arteyeva N. T-wave area along with Tpeak-Tend interval is the most accurateindex of the dispersion of repolarization. [15] Borodin N., Iaparov B.Y., Moskvin A. Mathematical modeling of the calmodulin effect on the RyR2 gating. [15] Dokuchaev A., Katsnelson L.B., Sulman T.B., Shikhaleva E.V., Vikulova N.A. Contribution of cooperativity to the mechano-calcium feedbacksin myocardium. Experimental discrepancy and mathematicalapproach to overcome it. [16] Elman K.A., Filatova D.Y., Bashkatova Y.V., Beloschenko D.V. The stochastic and chaotic estimation of parametersof cardiorespiratory system of students of Ugra. [16] Erkudov V.O., Pugovkin A.P., Verlov N.A., Sergeev I.V., Ievkov S.A., Mashood S., Bagrina J.V. Characteristics of the accuracy of calculation of values of systemic blood pressure using transfer functions in experimental blood loss and its compensation. [16] Ermolaev P., Khramykh T.Mechanisms of cardiodepression after 80% liver resection in rats. [17] Filatova O.E., Rusak S.N., Maystrenko E.V., Dobrynina I.Y. Aging dynamics of cardio-vascular parameters аboriginal systemand alien population of the Russian North. [17] Frolova S., Agladze K.I., Tsvelaya V., Gaiko O. Photocontrol of voltage-gated ion channel activity by azobenzenetrimethylammonium bromide in neonatal rat cardiomyocytes. [18] Gorbunov V.S., Agladze K.I., Erofeev I.S. The application of C-TAB for excitation propagation photocontrolin cardiac tissue. [18] Iribe G. Localization of TRPC3 channels estimated by in-silicoand cellular functional experiments. [19] Kachalov V.N., Tsvelaya V., Agladze K.I. Conditions of the spiral wave unpinning from the heterogeneitywith different boundary conditions in a model of cardiac tissue. [19] Kalita I., Nizamieva A.A., Tsvelaya V., Kudryashova N., Agladze K.I. The influence of anisotropy on excitation wave propagationin neonatal rat cardiomyocytes monolayer. [19] Kamalova Y. The designing of vectorcardiograph prototype. [20] Kapelko V., Shirinsky V.P., Lakomkin V., Lukoshkova E., Gramovich V.,Vyborov O., Abramov A., Undrovinas N., Ermishkin V. Models of chronic heart failure with acute and gradual onset. [20] Khassanov I., Lomidze N.N., Revishvili A.S. Remote Patient Monitoring and Integration of Medical Data. [20] Kislukhin V. Markov chain for an indicator passing throughoutcardio-vascular system (CVS). [21] Konovalov P.V., Pravdin S., Solovyova O.E., Panfilov A.V. Influence of myocardial heterogeneity on scroll wave dynamicsin an axisymmetrical anatomical model of the left ventricle of thehuman heart. [21] Koshelev A., Pravdin S., Ushenin K.S., Bazhutina A.E. An improved analytical model of the cardiac left ventricle. [22] Lookin O., Protsenko Y.L. Sex-related effects of stretch on isometric twitch and Ca2+ transientin healthy and failing right ventricular myocardiumof adult and impuberal rats. [22] Moskvin A. Electron-conformational model of the ligand-activated ion channels. [22] Nezlobinsky T., Pravdin S., Katsnelson L.B. In silico comparison of the electrical propagation wave alongmyocardium fibers in the left ventricle wall vs. isolation. [23] Nigmatullina R.R., Zemskova S.N., Bilalova D.F., Mustafin A.A., Kuzmina O.I., Chibireva M.D., Nedorezova R.S. Valid method for estimation of pulmonary hypertention degreein children. [23] Parfenov A. Mathematical modeling of the cardiovascular systemunder the influence of environmental factors. [24] Pimenov V.G., Hendy A. Adaptivity of the alternating direction method for fractional reactiondiffusion equation with delay effects in electrocardiology. [24] Podgurskaya A.D., Krasheninnikova A., Tsvelaya V., Kudryashova N., Agladze K.I. Influence of alcohols on excitation wave propagationin neonatal rat ventricular cardiomyocyte monolayer. [24] Pravdin S. A mathematical model of the cardiac left ventricle anatomy and morphology. [24] Seemann G. Cause and effects of cardiac heterogeneity:insights from experimental and computational models. [25] Seryapina A.A., Shevelev O.B. Basic metabolomic patterns in early hypertensive rats: MRI study. [25] Shestakov A.P., Vasserman I.N., Shardakov I.N. Modeling of cardiac arrhythmia generation caused bypathological distribution of myocardial conductivity. [26] Shutko A.V., Gorbunov V.S., Nizamieva A.A., Guriya K.G., Agladze K.I. Contractile micro-constructs from cardiac tissue culturefor the research of autowave propagation in excitable systems. [26] Simakov S., Gamilov T., Kopylov Ph. Computational study of the haemodynamic significanceof the stenosis during multivessel coronary disease. [27] Syomin F., Zberiya M.V. A numerical simulation of changes in the performance of the leftventricle of the heart under various hemodynamic conditions. [27] Tsaturyan A. A simple model of cardiac muscle:mechanics, actin-myosin interaction and Ca-activation. [27] Tsvelaya V., Krasheninnikova A., Kudryashova N., Agladze K.I. Calcium-current dominated upstroke in severe hyperkalemia. [28] Ushenin K.S., Pravdin S., Chumarnaya T.V., Alueva Y.S., Solovyova O.E. Dynamics of scroll wave filaments in personalized modelsof the left ventricle of the human heart. [28] Vasserman I.N., Shardakov I.N., Shestakov A.P. Deriving of macroscopic intracellular conductivity of deformedmyocardium based on its microstructure. [28] Vassilevski Y.V., Pryamonosov R., Gamilov T. Personalized 3D models and applications. [29] Zun P.S., Hoekstra A., Anikina T.S. First results of fully coupled 3D models of in-stent restenosis. [29] BIOMECHANICS. EXPERIMENTAL AND MATHEMATICAL MODELSSBIOMECHANICS. EXPERIMENTAL AND MATHEMATICAL MODELS. EXPERIMENTAL AND MATHEMATICAL MODELS. [30] Balakin A., Kuznetsov D., Protsenko Y.L. The ‘length-tension’ loop in isolated myocardial preparations of theright ventricle of normal and hypertrophied hearts of male rats. [30] Belousova M.D., Kruchinina A.P., Chertopolokhov V.A. Automatic control model of the three-tier arm type manipulatorin the aimed-movement task. [30] Berestin D.K., Bazhenova A.E., Chernikov N.A., Vokhmina Y.V. Mathematical modeling of dynamics of development of Parkinson'sdisease on the tremor parameters. [31] Dubinin A.L., Nyashin Y.I., Osipenko M.A. Development of the biomechanical approach to tooth movementunder the orthodontic treatment. [31] Galochkina T., Volpert V. Reaction-diffusion waves in mathematical model of bloodcoagulation. [31] Golov A.V., Simakov S., Timme E.A. Mathematical modeling of alveolar ventilationand gas exchange during treadmill stress tests. [32] Gurev V., Rice J. Strain prediction in 3D finite element models of cardiac mechanics. [32] Kamaltdinov M.R. Simulation of digestion processes in antroduodenum:food particles dissolution in consideration of functional disorders. [33] Khamzin S., Kursanov A., Solovyova O.E. Load-dependence of the electromechanical function of myocardiumin a 1D tissue model. [33] Khokhlova A., Iribe G., Solovyova O.E Transmural gradient in mechanical properties of isolatedsubendocardial and subepicardial cardiomyocytes. [33] Kruchinin P.A. Optimal control problem and indexesof stabilometric "test with the visual step input". [34] Kruchinina A.P., Yakushev A.G. A study of the edge segments of saccadic eye trajectory. [34] Kursanov A., Khamzin S., Solovyova O.E. Load-dependence of intramyocardial slow force responsein heterogeneous myocardium. [35] Lisin R.V., Balakin A., Protsenko Y.L. Experimental study of the intramyocardial slow force response. [35] Melnikova N.B., Hoekstra A. The mechanics of a discrete multi-cellular model of arterial in‐stent restenosis. [35] Murashova D.S., Murashov S.A., Bogdan O.P., Muravieva O.V., Yugova S.O. Modelling of soft tissue deformation for static elastometry. [36] Nikitin V.N., Tverier V.M., Krotkikh A.A. Occlusion correction based on biomechanical modelling. [36] Nyashin Y.I., Lokhov V.A. Development of the “Virtual physiological human” concept. [37] Shulyatev A.F., Akulich Y.V., Akulich A.Y., Denisov A.S. 3D FEA simulation of the proximal human femur. [37] Smoluk A.T., Smoluk L.T., Balakin A., Protsenko Y.L., Lisin R.V. Modelling viscoelastic hysteresis of passive myocardial sample. [37] Svirepov P.I. Mathematical modeling of the left atria mechanical actionwith mitral regurgitation. [38] Svitenkov A., Rekin O., Hoekstra A. Accuracy of 1D blood flow simulations in relation to level of detailof the arterial tree model. [38] Tsinker M. Mathematical modelling of airflow in human respiratory tract. [39] Wilde M.V. Influence of artificial initial and boundary conditionsin biomechanical models of blood vessels. [39] ELECTROPHYSIOLOGY. EXPERIMENTAL AND COMPUTATIONAL MODELS. CLINICAL STUDIES. [40] Agladze K.I., Agladze N.N. Arrhythmia modelling in tissue culture. [40] Golovko V., Gonotkov M.A. Pharmacological analysis of transmembrane action potential'smorphology of myoepitelial cells in the spontaneously beating heartof ascidia Styela rustica. [40] Gonotkov M.A., Golovko V. The crucial role of the rapidly activating component of outwarddelayed rectifier K-current (IKr) in pig sinoauricular node (SAN). [40] Danilov A.A. Numerical methods for electrocardiography modelling. [41] Kolomeyets N.L., Roshchevskaya I.M. The electrical resistivity of a segment of the tail, lungs, liver,intercostal muscles of grass snakes during cooling. [41] Kharkovskaia E., Zhidkova N., Mukhina I.V., Osipov G.V. Role of TRPC1 channels in the propagation of electrical excitationin the isolated rat heart. [42] Lubimceva T.A., Lebedeva V.K., Trukshina M.A., Lyasnikova E.A., Lebedev D.S. Ventricular lead position and mechanical dyssynchronyin response to cardiac resynchronization therapy. [42] Poskina T.Y., Shakirova L.S., Klyus L.G., Eskov V.V. Stochastics and chaotic analysis of electromyogramand electroencefalogramm. [42] Prosheva V.I. New insights into the pacemaker and conduction systemcells organization in the adult avian heart. [43] Suslonova O., Smirnova S., Roshchevskaya I.M. Cardioelectric field in rats with experimental pulmonaryhypertension during ventricular depolarization. [43] Syunyaev R.A., Karpaev A.A., Aliev R.R. Simulation of the fibroblasts effect on synchronizationand rhythmogenesis in the sinoatrial node. [44] Zorin N.M., Ryvkin A.М., Moskvin A. Cooperation of membrane and calcium oscillatorsin sinoatrial node cells. [44] EXPERIMENTAL AND COMPUTATIONAL MODELS IN IMMUNOLOGY. [45] Bocharov G. Systems approach to modelling the "virus-host organism" interactionin infectious diseases. [45] Brilliant S.A. Impact of immobilization stress on change of protein fractionshemoglobin of bone marrow in rats. [45] Bykova M. The features of biochemical properties of extracellular matrix of bonemarrow in rats in conditions which stimulate granulocytopoiesis. [45] Chigvintsev V.M. A mathematical model of the functioning and mutual regulation ofthe immune and neuroendocrine systems in response to viralexposure under the impact of environmental factors, taking intoaccount the evolution of synthetic function impairment. [46] Khramtsova Y. The role of mast cells in the regulation of repair testicles. [46] Novikov M.Y., Kim A.V. Simulation of immune processes using Bio-Medical Software Package. [47] Polevshchikov A.V., Bondar A.V., Gumovskaya J.P. Modelling of t cell extravasation into a lymph node:from morphological basics towards clonal selection theory. [47] Tuzankina I.A., Sarkisyan N., Bolkov M., Tihomirov L.B., Bass E.A. Oral and maxillofacial manifestationsof primary immunodeficiency syndroms. [47] Zaitsev S.V., Polevshchikov A.V. Evaluation of probabilities of antigen recognition by T-lymphocytesin the lymph node: a mathematical model. [48] MOLECULAR BASIS OF BIOLOGICAL MOTILITY. 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