On-line monitoring of cell growth and cytotoxicity using electric cell-substrate impedance sensing (ECIS)

An on-line and continuous technique based on electric cell-substrate impedance sensing (ECIS) was developed for measuring the concentration and time response function of fibroblastic V79 cells exposed to mercury chloride and 1,3,5-trinitrobenzene (TNB). Attachment, spreading and proliferation of V79 fibroblastic cells cultured on a microarray of small gold electrodes precoated with fibronectin were detected as resistance changes. The response function was derived to reflect the resistance change as a result of cell attachment, spreading, mitosis and cytotoxicity effect. Exposure of V79 cells to mercury chloride or TNB led to alterations in cell behavior, and therefore, chemical cytotoxicity was easily screened by measuring the response function of the attached and spread cells in the presence of inhibitor. The half inhibition concentration, the required concentration to achieve 50% inhibition, was obtained from the response function to provide information about cytotoxicity during the course of the assay. A simple mathematical model was developed to describe the responses of ECIS that were related to the attachment, spreading, and proliferation of V79 fibroblastic cells. The novel results of this paper are mainly characterized by the systematic study of several parameters including the cell number, detection limit, sensor sensitivity, and cytotoxicity, and they may motivate further research and study of ECIS sensors.     

Monitoring viral-induced cell death using electric cell-substrate impedance sensing

Using an electrical measurement known as electric cell-substrate impedance sensing (ECIS), we have recorded the dynamics of viral infections in cell culture. With this technique, cells are cultured on small gold electrodes where the measured impedance mirrors changes in attachment and morphology of cultured cells. As the cells attach and spread on the electrode, the measured impedance increases until the electrode is completely covered. Viral infection inducing cytopathic effect results in dramatic impedance changes, which are mainly due to cell death. In the current study, two different fish cell lines have been used: chinook salmonid embryonic (CHSE-214) cells infected with infectious pancreatic necrosis virus (IPNV) and epithelioma papulosum cyprini (EPC) carp cells infected with infectious hematopoeitic necrosis virus (IHNV). The impedance changes caused by cell response to virus are easily measured and converted to resistance and capacitance. An approximate linear correlation between log of viral titer and time of cell death was determined.     

Electric cell-substrate impedance sensing (ECIS) as a noninvasive means to monitor the kinetics of cell spreading to artificial surfaces

This article describes the optimization of an experimental technique referred to as electric cell-substrate impedance sensing (ECIS) to monitor attachment and spreading of mammalian cells quantitatively and in real time. The method is based on measuring changes in AC impedance of small gold-film electrodes deposited on a culture dish and used as growth substrate. Based on experimental data and theoretical considerations we demonstrate that high-frequency capacitance measurements (f = 40 kHz) are most suited to follow the increasing surface coverage of the electrode due to cell spreading. The excellent time resolution of the method allowed an in-depth analysis of cell spreading kinetics under various experimental conditions. Using ECIS we studied the attachment and spreading of epithelial MDCK cells (strain II) on different protein coatings, and investigated the influence of divalent cations on spreading kinetics. We quantified the inhibitory effect of soluble peptides that mimic the recognition sequence of fibronectin and other extracellular matrix proteins (RGDS). We also applied the ECIS technique to monitor the detachment of confluent fibroblastic cell layers (WI38/VA-13) by means of these peptides.     

Impedance analysis of MDCK cells measured by electric cell-substrate impedance sensing.

Transepithelial impedance of Madin-Darby canine kidney cell layers is measured by a new instrumental method, referred to as electric cell-substrate impedance sensing. In this method, cells are cultured on small evaporated gold electrodes, and the impedance is measured in the frequency range 20-50,000 Hz by a small probing current. A model for impedance analysis of epithelial cells measured by this method is developed. The model considers three different pathways for the current flowing from the electrode through the cell layer: (1) in through the basal and out through the apical membrane, (2) in through the lateral and out through the apical membrane, and (3) between the cells through the paracellular space. By comparing model calculation with experimental impedance data, several morphological and cellular parameters can be determined: (1) the resistivity of the cell layer, (2) the average distance between the basal cell surface and substratum, and (3) the capacitance of apical, basal, and lateral cell membranes. This model is used to analyze impedance changes on removal of Ca2+ from confluent Mardin-Darby canine kidney cell layers. The method shows that reduction of Ca2+ concentration causes junction resistance between cells to drop and the distance between the basal cell surface and substratum to increase.     

Mathematical modeling of cancer cell invasion of tissue: biological insight from mathematical analysis and computational simulation

The ability of cancer cells to break out of tissue compartments and invade locally gives solid tumours a defining deadly characteristic. One of the first steps of invasion is the remodelling of the surrounding tissue or extracellular matrix (ECM) and a major part of this process is the over-expression of proteolytic enzymes, such as the urokinase-type plasminogen activator (uPA) and matrix metalloproteinases (MMPs), by the cancer cells to break down ECM proteins. Degradation of the matrix enables the cancer cells to migrate through the tissue and subsequently to spread to secondary sites in the body, a process known as metastasis. In this paper we undertake an analysis of a mathematical model of cancer cell invasion of tissue, or ECM, which focuses on the role of the urokinase plasminogen activation system. The model consists of a system of five reaction-diffusion-taxis partial differential equations describing the interactions between cancer cells, uPA, uPA inhibitors, plasmin and the host tissue. Cancer cells react chemotactically and haptotactically to the spatio-temporal effects of the uPA system. The results obtained from computational simulations carried out on the model equations produce dynamic heterogeneous spatio-temporal solutions and using linear stability analysis we show that this is caused by a taxis-driven instability of a spatially homogeneous steady-state. Finally we consider the biological implications of the model results, draw parallels with clinical samples and laboratory based models of cancer cell invasion using three-dimensional invasion assay, and go on to discuss future development of the model.     

A computational analysis framework for molecular cell dynamics: case-study of exocytosis

One difficulty in conducting biologically meaningful dynamic analysis at the systems biology level is that in vivo system regulation is complex. Meanwhile, many kinetic rates are unknown, making global system analysis intractable in practice. In this article, we demonstrate a computational pipeline to help solve this problem, using the exocytotic process as an example. Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles that contain a wide range of intracellular molecules. During this process a set of proteins called SNAREs acts as an engine in this vesicle-membrane fusion, by forming four-helical bundle complex between (membrane) target-specific and vesicle-specific SNAREs. As expected, the regulatory network for exocytosis is very complex. Based on the current understanding of the protein-protein interaction network related to exocytosis, we mathematically formulated the whole system, by the ordinary differential equations (ODE). We then applied a mathematical approach (called inverse problem) to estimating the kinetic parameters in the fundamental subsystem (without regulation) from limited in vitro experimental data, which fit well with the reports by the conventional assay. These estimates allowed us to conduct an efficient stability analysis under a specified parameter space for the exocytotic process with or without regulation. Finally, we discuss the potential of this approach to explain experimental observations and to make testable hypotheses for further experimentation.     

Glucose sensing in the pancreatic beta cell: a computational systems analysis

Background  

Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of K_ATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.     

Atomistic modeling and molecular dynamics analysis of human CaV1.2 channel using external electric field and ion pulling simulations

BackgroundHuman Ca_V1.2 (hCav1.2), a calcium selective voltage-gated channel, plays important roles in normal cardiac and neuronal functions. Calcium influx and gating mechanisms leading to the activation of hCa_V1.2 are critical for its functionalities. Lack of an experimentally resolved structure of hCa_V1.2 remains a significant impediment in molecular-level understanding of this channel. This work focuses on building atomistic hCa_V1.2 model and studying calcium influx using computational approaches.MethodsWe employed homology modeling and molecular dynamics (MD) to build the structure of hCa_V1.2. Subsequently, we employed steered molecular dynamics (SMD) to understand calcium ion permeation in hCa_V1.2.ResultsWe report a comprehensive three-dimensional model of a closed state hCa_V1.2 refined under physiological membrane-bound conditions using MD simulations. Our SMD simulations on the model revealed four important barriers for ion permeation: this includes three calcium binding sites formed by the EEEE- and TTTT- rings within the selectivity filter region and a large barrier rendered by the hydrophobic internal gate. Our results also revealed that the first hydration shell of calcium remained intact throughout the simulations, thus playing an important role in ion permeation in hCa_V1.2.ConclusionsOur results have provided some important mechanistic insights into the structure, dynamics and ion permeation in hCa_V1.2. The significant barriers for ion permeation formed by the four phenylalanine residues at the internal gate region suggest that this site is important for channel activation.     

A computational modeling and molecular dynamics study of the Michaelis complex of human protein Z-dependent protease inhibitor (ZPI) and factor Xa (FXa)

Protein Z-dependent protease inhibitor (ZPI) and antithrombin III (AT3) are members of the serpin superfamily of protease inhibitors that inhibit factor Xa (FXa) and other proteases in the coagulation pathway. While experimental structural information is available for the interaction of AT3 with FXa, at present there is no structural data regarding the interaction of ZPI with FXa, and the precise role of this interaction in the blood coagulation pathway is poorly understood. In an effort to gain a structural understanding of this system, we have built a solvent equilibrated three-dimensional structural model of the Michaelis complex of human ZPI/FXa using homology modeling, protein–protein docking and molecular dynamics simulation methods. Preliminary analysis of interactions at the complex interface from our simulations suggests that the interactions of the reactive center loop (RCL) and the exosite surface of ZPI with FXa are similar to those observed from X-ray crystal structure-based simulations of AT3/FXa. However, detailed comparison of our modeled structure of ZPI/FXa with that of AT3/FXa points to differences in interaction specificity at the reactive center and in the stability of the inhibitory complex, due to the presence of a tyrosine residue at the P1 position in ZPI, instead of the P1 arginine residue in AT3. The modeled structure also shows specific structural differences between AT3 and ZPI in the heparin-binding and flexible N-terminal tail regions. Our structural model of ZPI/FXa is also compatible with available experimental information regarding the importance for the inhibitory action of certain basic residues in FXa. Figure Solvent equilibrated models for protein z-dependent protease inhibitor and its initial reactive complex with coagulation factor Xa (show here) are developed. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. V.C. and C.J.L. contributed equally to this work. The solvent-equilibrated PDB structure of the ZPI/FXa will be made available upon request. Conflict of interest statement  The authors state that they have no conflict of interest.     

Forest cover dynamics analysis and prediction modeling using logistic regression model

Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (β = −26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2 km² of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC = 87%).     

Optimization and scale‐up of oligonucleotide synthesis in packed bed reactors using computational fluid dynamics modeling

A computational fluid dynamics (CFD) model for the analysis of oligonucleotide synthesis in packed bed reactors was developed and used to optimize the scale up of the process. The model includes reaction kinetics data obtained under well defined conditions comparable to the situation in the packed bed. The model was validated in terms of flow conditions and reaction kinetics by comparison with experimental data. Experimental validation and the following model parameter studies by simulation were performed on the basis of a column with 0.3 g oligonucleotide capacity. The scale‐up studies based on CFD modelling were calculated on a 440 g scale (oligonucleotide capacity). © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:1048–1056, 2014     

p21(Cip1) regulates cell-substrate adhesion and interphase microtubule dynamics in untransformed human mammary epithelial cells

Despite its frequent inactivation in human breast cancers, the role of p21(Cip1) (p21) in morphological plasticity of normal mammary epithelial cells is still poorly understood. To address this question, we have investigated the consequences of p21 silencing in two-dimensional (2D) morphogenesis of untransformed human mammary epithelial cells. Here we show that p21 inactivation causes a reduction of 2D cell spreading and suppresses focal adhesion. In order to investigate the cytoskeletal modifications associated with this altered morphology, we have analyzed the microtubule dynamics in interphase p21-depleted cells. Our results demonstrate that interphase microtubule dynamic instability is strongly increased by p21 silencing. This alteration correlates with severe microtubule hypoacetylation. Next, we show that these microtubule defects in p21-depleted cells can be reversed by the use of the small molecule tubacin, a specific inhibitor of the α-tubulin deacetylase HDAC6. Tubacin-induced microtubule dynamics decrease also correlates with a partial recovery of cell spreading and focal adhesion in those cells. Collectively, these data indicate that p21 regulates the morphological plasticity of normal mammary epithelial cells by modulating dynamics of key cytoskeletal components.     

A computational analysis of pyrazole-based inhibitors binding to Hsp90 using molecular dynamics simulation and the MM-GBSA method

Owing to the key role of heat-shock protein 90 (Hsp90) in the evolution, development and disease pathogenesis of cancer, it has been an important target for anti-cancer chemotherapy over the years. A five-nanosecond molecular dynamics simulation combined with the calculation of the binding free energy was carried out to investigate the binding mechanisms of three Hsp90 inhibitors 4BH, 2E1 and 2D9 to Hsp90. The binding free energy of each complex was computed using the molecular mechanics–generalised Born surface area method. Detailed binding free energies between each inhibitor and residues of Hsp90 were calculated using a per-residue basis decomposition method. The detailed inhibitor–residue interaction provides insights into binding mechanisms and in-depth understanding of the structure–affinity relationship. This study suggests that van der Waals energy is primarily responsible for driving the binding of the inhibitors to Hsp90, and the three inhibitors bind to Hsp90 in a similar binding mode. However, a substituent in 2D9 leads to higher binding free energy than the other two inhibitors. These data may assist in designing new potent drugs to combat cancer.     

Comparative modeling of PON2 and analysis of its substrate binding interactions using computational methods

Paraoxonase (PON) constitutes a family of calcium-dependent mammalian enzymes comprising of PON1, PON2, and PON3. PON family shares ~60% sequence homology. These enzymes exhibit multiple activities like paraoxonase, arylesterase, and lactonase in a substrate dependent manner. Decreased PON activity has been reported in diseases like cardiovascular disease, atherosclerosis, and diabetes. Even though, PON2 is the oldest member of the family, PON1 is the only member studied in silico. In this study, the structure of PON2 was modeled using MODELLER 9v7 and its interactions with relevant ligands and it's physiological substrate homocysteine thiolactone was performed using AutoDock 4.0. The results reveal that PON1 and PON2 share common ligand binding patterns for arylesterase and lactonase activity, whereas in case of paraoxon binding, the residues involved in the interactions were different. Interestingly, the substrate HCTL was found to have the lowest free energy of binding (ΔG) and highest affinity for PON2 than PON1.     

Backbone dynamics of the ribonuclease binase active site area using multinuclear ((15)N and (13)CO) NMR relaxation and computational molecular dynamics

The nano-pico second backbone dynamics of the ribonuclease binase, homologous to barnase, is investigated with (15)N, (13)C NMR relaxation at 11.74 and 18.78 T and with a 1.1 ns molecular dynamics simulation. The data are compared with the temperature factors reported for the X-ray structure of this enzyme. The molecular dynamics and X-ray data correspond well and predict motions in the loops 56-61 and 99-104 that contain residues that specifically recognize substrate and are catalytic (His101), respectively. In contrast, the (15)N relaxation data indicate that these loops are mostly ordered at the nano-pico second time scale. Nano-pico second motions in the recognition loop 56-61 are evident from (13)CO-(13)C cross relaxation data, but the mobility of the catalytic loop 99-104 is not detected by (13)CO cross relaxation either. From the results of this and previous work [Wang, L., Pang, Y., Holder, T., Brender, J. R., Kurochkin, A., and Zuiderweg, E. R. P. (2001) Proc. Natl. Acad. Sci. U.S.A., 98, 7684-7689], the following dynamical characterization of the active site area of binase emerges: a beta sheet, rigid at all probed time scales, supports the catalytic residue Glu 72. Both substrate-encapsulating loops are mobile on both fast and slow time scales, but the fast motions of the loop which contains the other catalytic residue, His 101, as predicted by B-factors and computational molecular dynamics is not detected by NMR relaxation. This work strongly argues for the use of several measures in the study of protein dynamics.     

Flow cytometric analysis of the cell cycle: Mathematical modeling and biological interpretation

Estimation of the repartition of asynchronous cells in the cell cycle can be explained by two hypotheses: the cells are supposed to be distributed into three groups: cells with a 2c DNA content (G0/1 phase), cells with a 4c DNA content (G2 + M phase) and cells with a DNA content ranging from 2c to 4c (S phase); there is a linear relationship between the amount of fluorescence emitted by the fluorescent probe which reveals the DNA and the DNA content. According to these hypotheses, the cell cycle can be represented by the following equation: [formula: see text] All the solutions for this equation are approximations. Non parametric methods (or graphical methods: rectangle, peak reflect) only use one or two phase(s) of the cell cycle, the remaining phase(s) being estimated by exclusion. In parametric methods (Dean & Jett, Baisch II, Fried), the DNAT(x) distribution is supposed to be known and is composed of two gaussians (representative of G0/1 and G2 + M) and a P(x,y) function representative of S phase. Despite the generality, these models are not applicable to all sample types, particularly heterogeneous cell populations with various DNA content. In addition, the cell cycle is dependent on several regulation points (transition from quiescence to proliferation, DNA synthesis initiation, mitosis induction) and biological perturbations can also lead to cytokinesis perturbations. Before the emergence of flow cytometry, the current view of cell cycle resided in the assessment of cell proliferation (increase in cell number) or the kinetic of molecules incorporation (DNA precursors).(ABSTRACT TRUNCATED AT 250 WORDS)     

Design and analysis of flow velocity distribution inside a raceway pond using computational fluid dynamics

Bioprocess and Biosystems Engineering - Open raceway ponds are widely adopted for cultivating microalgae on a large scale. Working depth of the raceway pond is the major component to be analysed...