Ali A. Abbasi

Force Controlled Manipulation of Biological Cells Using a Monolithic MEMS Based Nano-Micro Gripper

Nano-micro grippers are able to pick-transport-place the micro or nanometer–sized materials, such as manipulation of biological cells or DNA molecules in a liquid medium. This paper proposes a novel monolithic nano-micro gripper structure with two axis piezoresistive force sensor which its resolution is under nanoNewton. The results of the study have been obtained by the simulation of the proposed gripper structure in Matlab software. Motion of the gripper arm is produced by a voice coil actuator. The behavior of the cell has been derived using the assumptions in the literatures. Moreover, two simple PID controllers, one for control of the gripper motion and another for control of the force during manipulation of a biologic cell, have been implemented. Although the proposed gripper has not been fabricated, since the geometrical dimensions of the proposed gripper is the same as previously developed electrothermally actuated micro-nano gripper, the results of force control have been also compared with it. The simulated results with the very simple PID force controller which has a more rapid response than previously developed electrothermally actuated micro-nano gripper show that the designed gripper has the potential to be considered and fabricated for manipulation of biological cells in the future.Copyright

Deformation prediction by a feed forward artificial neural network during mouse embryo micromanipulation

Abstract In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Flückiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.

Deformation Characterization of Mouse Oocyte Cell Using Inverse Finite Element and Levenberg–Marquardt Optimization Algorithm in Needle Injection Experiment

In order to better understand the mechanical properties of biological cells, characterization and investigation of their material behavior is necessary. In this paper hyperelastic Neo-Hookean material is used to characterize the mechanical properties of mouse oocyte cell. It has been assumed that the cell behavior is continues, isotropic, nonlinear and homogenous material. Then, by matching the experimental data with finite element (FE) simulation result and using the Levenberg–Marquardt optimization algorithm, the nonlinear hyperelastic model parameters have been extracted. Experimental data of mouse oocyte captured from literatures. Advantage of the developed model is that it can be used to calculate accurate reaction force on surgical instrument or it can be used to compute deformation or force in virtual reality based medical simulations.Copyright

Application of Hyperelastic Models in Mechanical Properties Prediction of Mouse Oocyte and Embryo Cells at Large Deformations

Biological cell studies have many applications in biology, cell manipulation and diagnosis of diseases such as cancer and malaria. In this study, inverse finite element method (IFEM) combined with Levenberg-Marquardt optimization algorithm has been used to extract and characterize material properties of mouse oocyte and embryo cells at large deformations. Then, the simulation results have been validated using data from experimental works. In this study, it is assumed cell material is hyperelastic, isotropic, homogenous and axisymmetric. For inverse analysis, FEM model of cell injection experiment which implemented in Abaqus software has been coupled with Levenberg-Marquardt optimization algorithm written in Matlab; based on this coupling the optimum hyperelastic coefficients which give the best match between experimental and simulated forces are extracted. Results show that among different hyperelastic material models, Ogden material is well suitable for characterization of mouse oocyte cell and Mooney-Rivlin or polynomial are suitable for characterization of mouse embryo cell. Moreover the evaluated Poisson ratio of the cell is obtained to be equal to 0.5, which indicates the structural material of mouse oocyte and embryo, are compressible.Copyright

Utilization of Least Square Support Vector Machine (LSSVM) for Electrical Resistivity Prediction of the Zn-Mn-S Nanocrystalline Semiconductor Films

In this investigation, application of the least square support vector machine (LSSVM) for modeling of the electrical resistivity of the magnetic Zn-Mn-S nanocrystalline semiconductor films has been described. The model has been trained based on the experimental data obtained from a published work by Sreekantha Reddy et al. The model inputs are temperature and variations in the concentrations of Zn, Mn. The results indicate that LSSVM is able to be used for accurate prediction of the electrical resistivity of the Zn-Mn-S nanocrystalline semiconductor films.Copyright

Prediction of Reaction Force on External Indenter in Cell Injection Experiment Using Support Vector Machine Technique

Evaluation of the reaction force on a tool which is used for exertion of force on biomaterials such as biological cells or soft tissues has applications in virtual reality based medical simulators or haptic tools. In this study, two least square based support vector machine (SVM) models have been constructed to predict the indentation or reaction force on mouse oocyte and embryo cells in cell injection experiment. Inputs of these two models are geometrical parameters of indented cell, namely dimple radius (a), dimple depth (w) and radius of the semicircular curve (R). Experimental data for calibration and prediction of the models have been captured from literatures. The performance of the models has been evaluated using root mean square error (RMSE), correlation coefficient (r), relative error of prediction (REP), Nash-sutcliffe coefficient of efficiency (Ef) and accuracy factor (Af). Comparison of the prediction results of the SVM models with experimental datapoints shows that the proposed SVM models have the potential to be used for force prediction applications.Copyright

Whole Cell Mechanical Property Characterization Based on Mechanical Properties of Its Cytoplasm and Bio Membrane

Analysis and investigation of the relation between different parts of biological cells such as biomembrane, cytoplasm and nucleus can help to better understand their behaviors and material properties. In this paper, first, the whole elastic properties of mouse oocyte and embryo cells have been computed by inverse finite element and Levenberg–Marquardt optimization algorithm and second, using the derived mechanical properties and the mechanical properties of its bio membrane from the literature, the mechanical properties of its cytoplasm has been characterized. It has been assumed that the cell behavior is as continues, isotropic, nonlinear and homogenous material for modeling. Matching the experimental forces with the forces from the finite element (FE) simulation by the Levenberg–Marquardt optimization algorithm, gives the nonlinear hyperelastic model parameters for the whole cell. Experimental data of mouse oocyte and embryo cells captured from the literatures.Copyright

Adaptive Neural Fuzzy Inference (ANFI) Modeling Technique for Production of Marine Biosurfactant

In this study; a Sugeno type ANFI model which describes the relationship between the bio surfactant concentration as a model output and the critical medium components as its inputs has been constructed. The critical medium components are glucose, urea, SrCl2 and MgSo4. The experimental data for training and testing capability of the model obtained by a statistical experimental design which have been captured from literatures. Six generalized bell shaped membership function have been selected for each of input variables and based on the training data ANFI model has been trained using the hybrid learning algorithm. The yielded biosurfactant concentration values from the model prediction shows close agreement with the experimental data.Copyright

Deformation Prediction of Mouse Embryos in Cell Injection Experiment by a Feedforward Artificial Neural Network

In this study, neural network models have been used to predict the mechanical behaviors of mouse embryos. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. In order to reach these purposes two neural network models have been implemented. Experimental data earlier deduced-by [Fluckiger, M. (2004). Cell Membrane Mechanical Modeling for Microrobotic Cell Manipulation. Diploma Thesis, ETHZ Swiss Federal Institute of Technology, Zurich, WS03/04] –were collected to obtain training and test data for the neural network. The results of these investigations show that the correlation values of the test and training data sets are between0.9988 and 1.0000, which are in good agreement with the experimental observations.Copyright

Modeling of Cell Deformation Under External Force Using Neural Network

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper the goal is the prediction of cell membrane deformation under a certain force, and to visually estimate the force of indentation on the membrane from membrane geometries. The neural network input and output parameters are associated to a three dimensional model without the assumption of the adherent affects. The neural network is modeled by applying error back propagation algorithm. In order to validate the strength of the developed neural network model, the results are compared with the experimental data on mouse oocyte and mouse embryos that are captured from literature. The results of the modeling match nicely the experimental findings.© 2010 ASME