A computational model for populations of dividing cells.

A mathematical model of the cell movements due to cell division is presented. In the model we assume that every cell is a computational object with a given volume, and that the cell pushes the neighbouring cells in order to acquire the space for this volume. The Force that each cell exerts over the other cells is derived from a harmonic arbitrary Potential. The main parameter of the model is the average distance among the cells, that checks if the system is in spatial equilibrium or not. We show that just changing the physical constraints we can model two different systems, a two-dimensional culture on a plate and a three-dimensional early embryo. In both cases the patterns of the cell populations we obtain are similar to the real ones.     

A computational analysis of some diaryl ureas in relation to their observed crystalline hydrogen bonding patterns

We have carried out anab initio self-consistent-field molecular orbital analysis of the structures, atomic charges and electrostatic potentials of 1,3-bisphenylurea, 1,3-bis(m-nitrophenyl)urea and 1,3-bis(p-nitrophenyl)urea. Our results provide insight into the contrasting hydrogen bond patterns and preferences of these molecules in crystalline environments. The tendency for 1,3-bisphenylurea to form homomeric rather than heteromeric (i.e. host-guest) crystals is attributed to a relatively strong and extended homomeric attractive electrostatic interaction between two like molecules. Our calculated internuclear distances and atomic charges indicate that these molecules have some degree of H...O intramolecular hydrogen bonding between the carbonyl oxygen and the nearest aromatic hydrogens when the aromatic and the urea portions of the molecules are coplanar. This interaction is strongest for the meta nitro derivative, consistent with the latter remaining very nearly planar in its cocrystal structures. Our surface electrostatic potentials for the three diaryl ureas are used to interpret their differing crystallization properties and tendencies to form cocrystals with guest molecules.     

Ventricular motion during the ejection phase: a computational analysis.

In the present paper, the study of the ventricular motion during systole was addressed by means of a computational model of ventricular ejection. In particular, the implications of ventricular motion on blood acceleration and velocity measurements at the valvular plane (VP) were evaluated. An algorithm was developed to assess the force exchange between the ventricle and the surrounding tissue, i.e., the inflow and outflow vessels of the heart. The algorithm, based on the momentum equation for a transitory flowing system, was used in a fluid-structure model of the ventricle that includes the contractile behavior of the fibers and the viscous and inertial forces of the intraventricular fluid. The model calculates the ventricular center of mass motion, the VP motion, and intraventricular pressure gradients. Results indicate that the motion of the ventricle affects the noninvasive estimation of the transvalvular pressure gradient using Doppler ultrasound. The VP motion can lead to an underestimation equal to 12.4 +/- 6.6%.     

A simple statistical analysis of Indian muntjac Giemsa band patterns.

Indian Muntiacus muntjac G-banded chromosomes were used for computerized analysis for standardized karyotype generation. Individual chromosomes on high-contrast photographic negatives were scanned densitometrically. Alignment of each chromosome for analysis was achieved by locating predominant peaks as well as the centromere. This provided better alignment that the use of the chromosome-end locations. The standardized set was obtained by determing the root-mean-square average density along 10-20 homologous chromosomes. The resulting standard karyotype differs from those published earlier. Prophase chromosomes exhibited greater detail than more condensed metaphase chromosomes. Although Indian muntjac chromosomes were used as a model, the method of analysis should be readily adaptable for examining chromosomes of any origin. The analytic technique should be within the capabilities of the smallest cytogenetic laboratories.     

Structural model of the mAb 806-EGFR complex using computational docking followed by computational and experimental mutagenesis

In this work, we combined computational protein-protein docking with computational and experimental mutagenesis to predict the structure of the complex formed by monoclonal antibody 806 (mAb 806) and the epidermal growth factor receptor (EGFR). We docked mAb 806, an antitumor antibody, to its epitope of EGFR residues 287-302. Potential mAb 806-EGFR orientations were generated, and computational mutagenesis was used to filter them according to their agreement with experimental mutagenesis data. Further computational mutagenesis suggested additional mutations, which were tested to arrive at a final structure that was most consistent with experimental mutagenesis data. We propose that this is the EGFR-mAb 806 structure, in which mAb 806 binds to an untethered form of the receptor, consistent with published experimental results. The steric hindrance created by the antibody near the EGFR dimer interface interferes with receptor dimerization, and we postulate this as the structural origin for the antitumor effect of mAb 806.     

A simple stochastic model of spatially complex neurons

A method for studying the coding properties of a multicompartmental integrate-and-fire neuron of arbitrary geometry is presented. Depolarization at each compartment evolves like a leaky integrator with an after-firing reset imposed only at the trigger zone. The frequency of firing at the steady-state regime is related to the properties of the multidimensional input. The decreasing variability of subthreshold depolarization from the dendritic tree to the trigger zone is shown for an input that is corrupted by a white noise. The role of a Poissonian noise is also investigated. The proposed method gives an estimate of the mean interspike interval that can be used to study the input output transfer function of the system. Both types of the stochastic inputs result in broadening the transfer function with respect to the deterministic case.     

CellGeo: A computational platform for the analysis of shape changes in cells with complex geometries

Cell biologists increasingly rely on computer-aided image analysis, allowing them to collect precise, unbiased quantitative results. However, despite great progress in image processing and computer vision, current computational approaches fail to address many key aspects of cell behavior, including the cell protrusions that guide cell migration and drive morphogenesis. We developed the open source MATLAB application CellGeo, a user-friendly computational platform to allow simultaneous, automated tracking and analysis of dynamic changes in cell shape, including protrusions ranging from filopodia to lamellipodia. Our method maps an arbitrary cell shape onto a tree graph that, unlike traditional skeletonization algorithms, preserves complex boundary features. CellGeo allows rigorous but flexible definition and accurate automated detection and tracking of geometric features of interest. We demonstrate CellGeo’s utility by deriving new insights into (a) the roles of Diaphanous, Enabled, and Capping protein in regulating filopodia and lamellipodia dynamics in Drosophila melanogaster cells and (b) the dynamic properties of growth cones in catecholaminergic a–differentiated neuroblastoma cells.     

A computational model of monkey cortical grating cells

Grating cells were discovered in the V1 and V2 areas of the monkey visual cortex by von der Heydt et al. (1992). These cells responded vigorously to grating patterns of appropriate orientation and periodicity. Computational models inspired by these findings were used as texture operator (Kruzinga and Petkov 1995, 1999; Petkov and Kruzinga 1997) and for the emergence and self-organization of grating cells (Brunner et al. 1998; Bauer et al. 1999). The aim of this paper is to create a grating cell operator that demonstrates similar responses to monkey grating cells by applying operator to the same stimuli as in the experiments carried out by von der Heydt et al. (1992). Operator will be tested on images that contain periodic patterns as suggested by De Valois (1988). In order to learn more about the role of grating cells in natural vision, operator is applied to 338 real-world images of textures obtained from three different databases. The results suggest that grating cells respond strongly to regular alternating periodic patterns of a certain orientation. Such patterns are common in images of human-made structures, like buildings, fabrics, and tiles, and to regular natural periodic patterns, which are relatively rare in nature.     

Determination of dynamic ankle ligament strains from a computational model driven by motion analysis based kinematic data

External rotation of the foot has been implicated in high ankle sprains. Recent studies by this laboratory, and others, have suggested that torsional traction characteristics of the shoe-surface interface may play a role in ankle injury. While ankle injuries most often involve damage to ligaments due to excessive strains, the studies conducted by this laboratory and others have largely used surrogate models of the lower extremity to determine shoe-surface interface characteristics based on torque measures alone. The objective of this study was to develop a methodology that would integrate a motion analysis-based kinematic foot model with a computational model of the ankle to determine dynamic ankle ligament strains during external foot rotation. Six subjects performed single-legged, internal rotation of the body with a planted foot while a marker-based motion analysis was conducted to track the hindfoot motion relative to the tibia. These kinematic data were used to drive an established computational ankle model. Ankle ligament strains, as a function of time, were determined. The anterior tibiofibular ligament (ATiFL) experienced the highest strain at 9.2±1.1%, followed by the anterior deltoid ligament (ADL) at 7.8±0.7%, averaged over the six subjects. The peak ATiFL strain occurred prior to peak strain in the ADL in all subjects. This novel methodology may provide new insights into mechanisms of high ankle sprains and offer a basis for future evaluations of shoe-surface interface characteristics using human subjects rather than mechanical surrogate devices.     

A unified model for complex segregation analysis.

Various methods have been proposed for statistical inference of major genes by segregation analysis of human familial data. An attempt is made to resolve some divergences that have occurred in this context by the consideration of a unified model, with some practical applications.     

A computational model for dynamic analysis of the human gait

Biomechanical models are important tools in the study of human motion. This work proposes a computational model to analyse the dynamics of lower limb motion using a kinematic chain to represent the body segments and rotational joints linked by viscoelastic elements. The model uses anthropometric parameters, ground reaction forces and joint Cardan angles from subjects to analyse lower limb motion during the gait. The model allows evaluating these data in each body plane. Six healthy subjects walked on a treadmill to record the kinematic and kinetic data. In addition, anthropometric parameters were recorded to construct the model. The viscoelastic parameter values were fitted for the model joints (hip, knee and ankle). The proposed model demonstrated that manipulating the viscoelastic parameters between the body segments could fit the amplitudes and frequencies of motion. The data collected in this work have viscoelastic parameter values that follow a normal distribution, indicating that these values are directly related to the gait pattern. To validate the model, we used the values of the joint angles to perform a comparison between the model results and previously published data. The model results show a same pattern and range of values found in the literature for the human gait motion.     

Dimensional reduction of a V1 ring model with simple and complex cells

In this paper, we extend a framework for constructing low-dimensional dynamical systems models of mammalian primary visual cortex to a cortical network model that incorporates the full nonlinear effects of complex cells. The procedure consists of capturing the essential dynamics in a low-dimensional subspace using empirical methods, then recasting the equations in the reduced vector space. Previously, we considered visual cortical network models consisting of only simple cells with nearly linear responses to external stimuli. Here we show that fully nonlinear effects can be incorporated by examining the dimensional reduction of an idealized ring model of V1 with both simple and complex cells. We found it expedient to divide the subspace into four separate neuronal populations: excitatory simple, excitatory complex, inhibitory simple and inhibitory complex. In order to reproduce the fluctuation-driven dynamics in this reduced space, we incorporated (1) white noises with different intensities into individual neuronal populations, and (2) firing rate estimates to capture the probability of firing due to subthreshold fluctuations. With a more accurate, fitted connectivity, our modified dimensional reduced models can reproduce the firing rates, circular variances and modulation ratios observed in the original ring model.     

A computational analysis of separating motion signals in transparent random dot kinematograms

When multiple motion directions are presented simultaneously within the same region of the visual field human observers see motion transparency. This perceptual phenomenon requires from the visual system to separate different motion signal distributions, which are characterised by distinct means that correspond to the different dot directions and variances that are determined by the signal and processing noise. Averaging of local motion signals can be employed to reduce noise components, but such pooling could at the same time lead to the averaging of different directional signal components, arising from spatially adjacent dots moving in different directions, which would reduce the visibility of transparent directions. To study the theoretical limitations of encoding transparent motion by a biologically plausible motion detector network, the distributions of motion directions signalled by a motion detector model (2DMD) were analysed here for Random Dot Kinematograms (RDKs). In sparse dot RDKs with two randomly interleaved motion directions, the angular separation that still allows us to separate two directions is limited by the internal noise in the system. Under the present conditions direction differences down to 30 deg could be separated. Correspondingly, in a transparent motion stimulus containing multiple motion directions, more than eight directions could be separated. When this computational analysis is compared to some published psychophysical data, it appears that the experimental results do not reach the predicted limits. Whereas the computer simulations demonstrate that even an unsophisticated motion detector network would be appropriate to represent a considerable number of motion directions simultaneously within the same region, human observers usually are restricted to seeing not more than two or three directions under comparable conditions. This raises the question why human observers do not make full use of information that could be easily extracted from the representation of motion signals at the early stages of the visual system.     

Cellular basis for the response to second-order motion cues in Y retinal ganglion cells.

We perceive motion when presented with spatiotemporal changes in contrast (second-order cue). This requires linear signals to be rectified and then summed in temporal order to compute direction. Although both operations have been attributed to cortex, rectification might occur in retina, prior to the ganglion cell. Here we show that the Y ganglion cell does indeed respond to spatiotemporal contrast modulations of a second-order motion stimulus. Responses in an OFF ganglion cell are caused by an EPSP/IPSP sequence evoked from within the dendritic field; in ON cells inhibition is indirect. Inhibitory effects, which are blocked by tetrodotoxin, clamp the response near resting potential thus preventing saturation. Apparently the computation for second-order motion can be initiated by Y cells and completed by cortical cells that sum outputs of multiple Y cells in a directionally selective manner.     

The active and passive ciliary motion in the embryo node: A computational fluid dynamics model

The breaking of left–right symmetry in the mammalian embryo is believed to occur in a transient embryonic structure, the node, when cilia create a leftward flow of liquid. The two-cilia hypothesis proposes that the node contains two kinds of primary cilia: motile cilia that rotate autonomously to generate the leftward fluid flow and passive cilia that act as mechano-sensors, responding to flow. While studies support this hypothesis, the mechanism by which the sensory cilia respond to the fluid flow is still unclear. In this paper, we present a computational model of two cilia, one active and one passive. By employing computational fluid dynamics, deformable mesh computational techniques and fluid–structure interaction analysis, and solving the three-dimensional unsteady transport equations, we study the flow pattern produced by the movement of the active cilium and the response of the passive cilium to this flow. Our results reveal that clockwise rotation of the active cilium can generate a counter-clockwise elliptical rotation and overall lateral displacement for its neighboring passive one, of measurable magnitude and consistent pattern. This supports the plausibility of the two-cilia hypothesis and helps quantify the motion pattern for the passive cilium induced by this regional flow.     

Regulation of cell-mediated immunity in cryptococcosis. II. Characterization of first-order T suppressor cells (Ts1) and induction of second-order suppressor cells

Cryptococcosis patients frequently have high levels of cryptococcal antigen in their body fluids, and the levels of circulating antigen can generally be used to predict the patient's recovery, with high or rising antigen titers indicating a poor prognosis and low or decreasing levels a good prognosis. In a previous study, we reported on a murine model for studying the effects of cryptococcal antigen on host defense mechanisms. In that work, we demonstrated that an i.v. injection of cryptococcal antigen (CneF) into CBA/J mice, to simulate the antigenemia known to occur in human cryptococcosis, induced a population of T suppressor cells (Ts1) in the lymph nodes (LN). Upon adoptive transfer, the Ts1 cells specifically suppressed the afferent limb of the delayed-type hypersensitivity (DTH) response to cryptococcal antigen. In the present study, we show that the precursors of the Ts1 cells are sensitive to low-dose cyclophosphamide treatment and that the phenotype of the Ts1 cells is Lyt-1+, Ia+ (I-J+). LN cells from CneF-injected mice or a soluble factor derived therefrom can induce in the spleens of recipient mice a second-order suppressor cell population that suppresses the efferent limb of the DTH response. The cells that induce the second-order or efferent suppressor cells have the same phenotype as the cells that appear to suppress the afferent limb of the DTH response. The findings in this study indicate that a complex regulatory mechanism is responsible for the observed suppression of the DTH response in this infectious disease model. Furthermore, the suppressive circuit thus far defined for cryptococcal antigen is similar to the antigen-specific suppressor cell pathway outlined for certain chemically defined haptenic systems.     

A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model

Simple cells in primary visual cortex are believed to extract local contour information from a visual scene. The 2D Gabor function (GF) model has gained particular popularity as a computational model of a simple cell. However, it short-cuts the LGN, it cannot reproduce a number of properties of real simple cells, and its effectiveness in contour detection tasks has never been compared with the effectiveness of alternative models. We propose a computational model that uses as afferent inputs the responses of model LGN cells with center–surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances of the CORF and GF models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths. The RF map of the proposed CORF model, determined with simulated reverse correlation, can be divided in elongated excitatory and inhibitory regions typical of simple cells. The modulated response to shifted gratings that this model shows is also characteristic of a simple cell. Furthermore, the CORF model exhibits cross orientation suppression, contrast invariant orientation tuning and response saturation. These properties are observed in real simple cells, but are not possessed by the GF model. The proposed CORF model outperforms the GF model in contour detection with high statistical confidence (RuG data set: p < 10⁻⁴, and Berkeley data set: p < 10⁻⁴). The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells.     

Response component analysis of simple and complex cells of area 18 during depression of area 17.

Simple and complex cells of visual areas of cats may be reliably classified according to the modulatory index (MI) of their responses. This investigation is aimed at analysing the MI in area 18 when a small region (about 200-400 microm in diameter) of area 17 was inactivated with a microinjection of GABA, in anesthetized cats. Cells were stimulated with sine-wave gratings whose orientation, spatial, and temporal frequencies were optimal for the studied unit. The AC and DC response components, and the MI were computed along with fast Fourier transforms of evoked discharges recorded as peristimulus time histograms. Results showed that these response components were relatively unaffected in simple cells, whereas complex cells exhibited large changes when area 17 was silenced. In particular, a large proportion of complex cells showed a MI greater than 1, thereby adopting a response pattern resembling simple cells. It is suggested that this subpopulation of complex cells receives a direct input from geniculate X cells.     

A simple model of cannibalism.

A simple nonlinear discrete model is derived for the dynamics of a two-age class population consisting of juveniles and adults that includes cannibalism of juveniles by adults. The model is investigated analytically and numerically. It is shown how even this very simple model, by incorporating the negative and positive feedbacks due to cannibalism, can account for several important phenomena concerning the dynamics of cannibalistic populations that have been discussed and studied in the literature. These include the possibilities that the practice of cannibalism can (1) in certain circumstances be a form of self-regulation that promotes stable equilibration, while in other circumstances it can lead to population oscillations; (2) result in a viable population in circumstances when its absence would otherwise result in extinction; and (3) be the source of multiple stable equilibria and hysteresis effects.     

Ion channel mechanisms underlying frequency-firing patterns of the avian nucleus magnocellularis: A computational model

We have previously shown that late-developing avian nucleus magnocellularis (NM) neurons (embryonic [E] days 19–21) fire action potentials (APs) that resembles a band-pass filter in response to sinusoidal current injections of varying frequencies. NM neurons located in the mid- to high-frequency regions of the nucleus fire preferentially at 75 Hz, but only fire a single onset AP to frequency inputs greater than 200 Hz. Surprisingly, NM neurons do not fire APs to sinusoidal inputs less than 20 Hz regardless of the strength of the current injection. In the present study we evaluated intrinsic mechanisms that prevent AP generation to low frequency inputs. We constructed a computational model to simulate the frequency-firing patterns of NM neurons based on experimental data at both room and near physiologic temperatures. The results from our model confirm that the interaction among low- and high-voltage activated potassium channels (K_LVA and K_HVA, respectively) and voltage dependent sodium channels (Na_V) give rise to the frequency-firing patterns observed in vitro. In particular, we evaluated the regulatory role of K_LVA during low frequency sinusoidal stimulation. The model shows that, in response to low frequency stimuli, activation of large K_LVA current counterbalances the slow-depolarizing current injection, likely permitting Na_V closed-state inactivation and preventing the generation of APs. When the K_LVA current density was reduced, the model neuron fired multiple APs per sinusoidal cycle, indicating that K_LVA channels regulate low frequency AP firing of NM neurons. This intrinsic property of NM neurons may assist in optimizing response to different rates of synaptic inputs.