A simplified approach to quasi-linear viscoelastic modeling

The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in 1-D is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of "ramp-and-hold" stretching tests were applied to rectangular collagen specimens. The relaxation force data from the "hold" was used to calibrate a new "adaptive QLV model" and several models from literature, and the force data from the "ramp" was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The "adaptive QLV model" based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation.     

A simplified GIS approach to modeling global leaf water isoscapes

The stable hydrogen (delta(2)H) and oxygen (delta(18)O) isotope ratios of organic and inorganic materials record biological and physical processes through the effects of substrate isotopic composition and fractionations that occur as reactions proceed. At large scales, these processes can exhibit spatial predictability because of the effects of coherent climatic patterns over the Earth's surface. Attempts to model spatial variation in the stable isotope ratios of water have been made for decades. Leaf water has a particular importance for some applications, including plant organic materials that record spatial and temporal climate variability and that may be a source of food for migrating animals. It is also an important source of the variability in the isotopic composition of atmospheric gases. Although efforts to model global-scale leaf water isotope ratio spatial variation have been made (especially of delta(18)O), significant uncertainty remains in models and their execution across spatial domains. We introduce here a Geographic Information System (GIS) approach to the generation of global, spatially-explicit isotope landscapes (= isoscapes) of "climate normal" leaf water isotope ratios. We evaluate the approach and the resulting products by comparison with simulation model outputs and point measurements, where obtainable, over the Earth's surface. The isoscapes were generated using biophysical models of isotope fractionation and spatially continuous precipitation isotope and climate layers as input model drivers. Leaf water delta(18)O isoscapes produced here generally agreed with latitudinal averages from GCM/biophysical model products, as well as mean values from point measurements. These results show global-scale spatial coherence in leaf water isotope ratios, similar to that observed for precipitation and validate the GIS approach to modeling leaf water isotopes. These results demonstrate that relatively simple models of leaf water enrichment combined with spatially continuous precipitation isotope ratio and climate data layers yield accurate global leaf water estimates applicable to important questions in ecology and atmospheric science.     

A simplified approach to derive Cleland model for enzymatic reactions

Metabolic modeling can suggest which is the key enzyme activity that needs to be controlled or its activity enhanced for the required production of a metabolite in a pathway. It also helps to find possible drug targets (enzymes to be inhibited). In metabolic modeling, knowing the kinetics of the enzymes involved in a pathway is mandatory. Most enzymatic reactions involve multi-substrates and follow an ordered sequential or ping–pong mechanism. The kinetic parameters involved in the model are obtained by fitting experimental data using a model based on the mechanism. The Cleland model has been used for some years. The grouping of parameters, such as dissociation constant and Michaelis–Menten constant, makes the strategy meaningful and hence the Cleland model is still in use. Although other alternate methods, e.g., the King-Altman method, are available, derivation by determinants can be used to derive a rate expression for the sequential or ping–pong mechanism, they are tedious. Hence, a meaningful modification is suggested in this communication for deriving the enzyme mechanism which is based on Thilakavathi et al. (Biotech Lett 28:1889–1894, 2006) to obtain the Cleland model in an easier way.     

A multiphysics approach for modeling early atherosclerosis

This work is devoted to the development of a mathematical model of the early stages of atherosclerosis incorporating processes of all time scales of the disease and to show their interactions. The cardiovascular mechanics is modeled by a fluid–structure interaction approach coupling a non-Newtonian fluid to a hyperelastic solid undergoing anisotropic growth and a change of its constitutive equation. Additionally, the transport of low-density lipoproteins and its penetration through the endothelium is considered by a coupled set of advection–diffusion-reaction equations. Thereby, the permeability of the endothelium is wall-shear stress modulated resulting in a locally varying accumulation of foam cells triggering a novel growth and remodeling formulation. The model is calibrated and applied to an murine-specific case study, and a qualitative validation of the computational results is performed. The model is utilized to further investigate the influence of the pulsatile blood flow and the compliance of the artery wall to the atherosclerotic process. The computational results imply that the pulsatile blood flow is crucial, whereas the compliance of the aorta has only a minor influence on atherosclerosis. Further, it is shown that the novel model is capable to produce a narrowing of the vessel lumen inducing an adaption of the endothelial permeability pattern.     

Penetration of cells into Millipore diffusion chambers

Diffusion chambers enclosed by Millipore filters with nominal pore sizes of 0.10, 0.22 or 0.45 μm are penetrable by small numbers of host cells, mainly macrophages. The presence, in the chambers, of a chemotactic substance causes the influx of much larger numbers of cells than medium alone.     

A simplified method for micronucleus preparation from hepatic cells

A simplified method for micronucleus preparation from regenerating hepatocytes has been developed. Small pieces of the regenerating portion of the liver are incubated in 1% sodium citrate solution containing collagenase Type 1A (final concentration 0.005% w/v) at 37 C for 10-15 min with occasional gentle agitation. The larger particles are discarded. Drops of the thick homogeneous citrate suspension of liver cells are put on the slides and drawn back immediately into the pipette, leaving only the drop marks. This simplified method, which gives good preparations with many intact hepatocytes, was validated in a model experiment using mitomycin C. The data revealed a distinct dose-response effect.     

Mucosal-associated invariant T cells from induced pluripotent stem cells:A novel approach for modeling human diseases

Mice have frequently been used to model human diseases involving immune dysregulation such as autoimmune and inflammatory diseases.These models help elucidatethe mechanisms underlying the disease and in the development of novel therapies.However,if mice are deficient in certain cells and/or effectors associated with human diseases,how can their functions be investigated in this species?Mucosal-associated invariant T(MAIT)cells,a novel innate-like T cell family member,are a good example.MAIT cells are abundant in humans but scarce in laboratory mice.MAIT cells harbor an invariant T cell receptor and recognize nonpeptidic antigens vitamin B2metabolites from bacteria and yeasts.Recent studies have shown that MAIT cells play a pivotal role in human diseases such as bacterial infections and autoimmune and inflammatory diseases.MAIT cells possess granulysin,a human-specific effector molecule,but granulysin and its homologue are absent in mice.Furthermore,MAIT cells show poor proliferation in vitro.To overcome these problems and further our knowledge of MAIT cells,we have established a method to expand MAIT cells via induced pluripotent stem cells(iP SCs).In this review,we describe recent advances in the field of MAIT cell research and our approach for human disease modeling with iP SCderived MAIT cells.     

A linearized constraint-based approach for modeling signaling networks

With the unparalleled increase in the availability of biological data over the last couple of decades, accurate and computable models are becoming increasingly important for unraveling complex biological phenomena. Past efforts to model signaling networks have utilized various computational methods, including Boolean and constraint-based modeling (CBM) approaches. These approaches are based on solving mixed integer linear programs; hence, they may not scale up for the analysis of large networks and are not amenable for applications based on sampling the full spectrum of the solution space. Here we propose a new CBM approach that is fully linear and does not involve integer variables, thereby overcoming the aforementioned limitations. We describe a novel optimization procedure for model construction and demonstrate the utility of our approach on a reconstructed model of the human epidermal growth factor receptor (EGFR) pathway, spanning 322 species and 211 connections. We compare our model's predictions to experimental phosphorylation data and to the predictions inferred via an additional Boolean-based EGFR signaling model. Our results show high prediction accuracy (75%) and high similarity to the Boolean model. Considering the marked computational advantages in terms of scalability and sampling utilization obtained by having a linear mode, these results demonstrate the potential promise of this framework for the study of cellular signaling.     

A nonparametric Bayesian modeling approach for cytogenetic dosimetry

In cytogenetic dosimetry, samples of cell cultures are exposed to a range of doses of a given agent. In each sample at each dose level, some measure of cell disability is recorded. The objective is to develop models that explain cell response to dose. Such models can be used to predict response at unobserved doses. More important, such models can provide inference for unknown exposure doses given the observed responses. Typically, cell disability is viewed as a Poisson count, but in the present work, a more appropriate response is a categorical classification. In the literature, modeling in this case is very limited. What exists is purely parametric. We propose a fully Bayesian nonparametric approach to this problem. We offer comparison with a parametric model through a simulation study and the analysis of a real dataset modeling blood cultures exposed to radiation where classification is with regard to number of micronuclei per cell.     

A hybrid agent-based approach for modeling microbiological systems

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10³ cells and 1.2×10⁶ molecules. The model produces cell migration patterns that are comparable to laboratory observations.     

A mechanistic approach for modeling temperature-dependent consumer-resource dynamics

Paramount to our ability to manage and protect biological communities from impending changes in the environment is an understanding of how communities will respond. General mathematical models of community dynamics are often too simplistic to accurately describe this response, partly to retain mathematical tractability and partly for the lack of biologically pleasing functions representing the model/environment interface. We address these problems of tractability and plausibility in community/environment models by incorporating the Boltzmann factor (temperature dependence) in a bioenergetic consumer-resource framework. Our analysis leads to three predictions for the response of consumer-resource systems to increasing mean temperature (warming). First, mathematical extinctions do not occur with warming; however, stable systems may transition into an unstable (cycling) state. Second, there is a decrease in the biomass density of resources with warming. The biomass density of consumers may increase or decrease depending on their proximity to the feasibility (extinction) boundary. Third, consumer biomass density is more sensitive to warming than resource biomass density (with some exceptions). These predictions are in line with many current observations and experiments. The model presented and analyzed here provides an advancement in the testing framework for global change scenarios and hypotheses of latitudinal and elevational species distributions.     

A simplified method for staining mast cells with astra blue

The copper phthalocyanin dye astra blue has been used to stain differentially mast cells of the intestine; however; the procedure has not been used widely because of the difficulty in preparing and using the dye solution. Described here is a simple, reliable, and consistent method for selectively staining mast cells using a dye solution that may be prepared in any laboratory without the aid of sophisticated pH metering equipment. Astra blue is mixed with an alcoholic solution containing MgCl2-6H2O and the pH indicator pararosaniline hydrochloride. Concentrated hydrochloric acid is added dropwise, changing the dye mixture from purple to violet and then to blue. In this low range the weakly ionizing ethanol provides a more stable hydrogen ion concentration than the corresponding aqueous solutions used previously. Alcoholic acid fuchsin is a convenient counterstain, and this simple procedure then provides good contrast between the blue staining mast cell granules and the red tissue background.     

A visco-hyperelastic constitutive approach for modeling polyvinyl alcohol sponge

This study proposes the quasi-linear viscoelastic (QLV) model to characterize the time dependent mechanical behavior of poly(vinyl alcohol) (PVA) sponges. The PVA sponges have implications in many viscoelastic soft tissues, including cartilage, liver, and kidney as an implant. However, a critical barrier to the use of the PVA sponge as tissue replacement material is a lack of sufficient study on its viscoelastic mechanical properties. In this study, the nonlinear mechanical behavior of a fabricated PVA sponge is investigated experimentally and computationally using relaxation and stress failure tests as well as finite element (FE) modeling. Hyperelastic strain energy density functions, such as Yeoh and Neo-Hookean, are used to capture the mechanical behavior of PVA sponge at ramp part, and viscoelastic model is used to describe the viscose behavior at hold part. Hyperelastic material constants are obtained and their general prediction ability is verified using FE simulations of PVA tensile experiments. The results of relaxation and stress failure tests revealed that Yeoh material model can define the mechanical behavior of PVA sponge properly compared with Neo-Hookean one. FE modeling results are also affirmed the appropriateness of Yeoh model to characterize the mechanical behavior of PVA sponge. Thus, the Yeoh model can be used in future biomechanical simulations of the spongy biomaterials. These results can be utilized to understand the viscoelastic behavior of PVA sponges and has implications for tissue engineering as scaffold.     

A non-stochastic approach for modeling uncertainty in population dynamics

We developed a non-stochastic methodology to deal with the uncertainty in models of population dynamics. This approach assumed that noise is bounded; it led to models based on differential inclusions rather than stochastic processes, and avoided stochastic calculus. Examples of estimations of extinction times for exponential and logistic population growth with environmental and demographic noise are presented.     

A new mathematical approach for solving carrier-facilitated steady-state diffusion problems

Summary A new mathematical treatment is presented which simplifies the solution of carrier-diffusion problems. The method is generally applicable and is illustrated and tested for a specific, commonly occurring situation: facilitated diffusion of a single substrate through flat layers. Results predicted for total substrate flux are in excellent agreement with control computer calculations. The method also can be used to obtain concentration profiles for each species; here the results are good only if conditions at the boundaries are predicted correctly.     

A system-based approach to modeling the ultrasound signal backscattered by red blood cells

A system-based model is proposed to describe and simulate the ultrasound signal backscattered by red blood cells (RBCs). The model is that of a space-invariant linear system that takes into consideration important biological tissue stochastic scattering properties as well as the characteristics of the ultrasound system. The formation of the ultrasound signal is described by a convolution integral involving a transducer transfer function, a scatterer prototype function, and a function representing the spatial arrangement of the scatterers. The RBCs are modeled as nonaggregating spherical scatterers, and the spatial distribution of the RBCs is determined using the Percus-Yevick packing factor. Computer simulations of the model are used to study the power backscattered by RBCs as a function of the hematocrit, the volume of the scatterers, and the frequency of the incident wave (2-500 MHz). Good agreement is obtained between the simulations and theoretical and experimental data for both Rayleigh and non-Rayleigh scattering conditions. In addition to these results, the renewal process theory is proposed to model the spatial arrangement of the scatterers. The study demonstrates that the system-based model is capable of accurately predicting important characteristics of the ultrasound signal backscattered by blood. The model is simple and flexible, and it appears to be superior to previous one- and two-dimensional simulation studies.     

A fold-recognition approach to loop modeling

A novel approach is proposed for modeling loop regions in proteins. In this approach, a prerequisite sequence-structure alignment is examined for regions where the target sequence is not covered by the structural template. These regions, extended with a number of residues from adjacent stem regions, are submitted to fold recognition. The alignments produced by fold recognition are integrated into the initial alignment to create an alignment between the target sequence and several structures, where gaps in the main structural template are covered by local structural templates. This one-to-many (1:N) alignment is used to create a protein model by existing protein-modeling techniques. Several alternative approaches were evaluated using a set of ten proteins. One approach was selected and evaluated using another set of 31 proteins. The most promising result was for gap regions not located at the C-terminus or N-terminus of a protein, where the method produced an average RMSD 12% lower than the loop modeling provided with the program MODELLER. This improvement is shown to be statistically significant. Figure The method derived from the training set applied to CASP target T0191