Theoretical Prediction of Disrupted Min Oscillation in Flattened Escherichia coli

The dynamics of the Min-protein system help Escherichia coli regulate the process of cell division by identifying the center of the cell. While this system exhibits robust bipolar oscillations in wild-type cell shapes, recent experiments have shown that when the cells are mechanically deformed into wide, flattened out, irregular shapes, the spatial regularity of these oscillations breaks down. We employ widely used stochastic and deterministic models of the Min system to simulate cells with flattened shapes. The deterministic model predicts strong bipolar oscillations, in contradiction with the experimentally observed behavior, while the stochastic model, which is based on the same reaction-diffusion equations, predicts more spatially irregular oscillations. We further report simulations of flattened but more symmetric shapes, which suggest that the flattening and lateral expansion may contribute as much to the irregular oscillation behavior as the asymmetry of the cell shapes.     

A network model of successive partitioning-limited solute diffusion through the stratum corneum

As the most exposed point of contact with the external environment, the skin is an important barrier to many chemical exposures, including medications, potentially toxic chemicals and cosmetics. Traditional dermal absorption models treat the stratum corneum lipids as a homogenous medium through which solutes diffuse according to Fick's first law of diffusion. This approach does not explain non-linear absorption and irregular distribution patterns within the stratum corneum lipids as observed in experimental data. A network model, based on successive partitioning-limited solute diffusion through the stratum corneum, where the lipid structure is represented by a large, sparse, and regular network where nodes have variable characteristics, offers an alternative, efficient, and flexible approach to dermal absorption modeling that simulates non-linear absorption data patterns. Four model versions are presented: two linear models, which have unlimited node capacities, and two non-linear models, which have limited node capacities. The non-linear model outputs produce absorption to dose relationships that can be best characterized quantitatively by using power equations, similar to the equations used to describe non-linear experimental data.     

Landscape as a model: the importance of geometry

In all models, but especially in those used to predict uncertain processes (e.g., climate change and nonnative species establishment), it is important to identify and remove any sources of bias that may confound results. This is critical in models designed to help support decisionmaking. The geometry used to represent virtual landscapes in spatially explicit models is a potential source of bias. The majority of spatial models use regular square geometry, although regular hexagonal landscapes have also been used. However, there are other ways in which space can be represented in spatially explicit models. For the first time, we explicitly compare the range of alternative geometries available to the modeller, and present a mechanism by which uncertainty in the representation of landscapes can be incorporated. We test how geometry can affect cell-to-cell movement across homogeneous virtual landscapes and compare regular geometries with a suite of irregular mosaics. We show that regular geometries have the potential to systematically bias the direction and distance of movement, whereas even individual instances of landscapes with irregular geometry do not. We also examine how geometry can affect the gross representation of real-world landscapes, and again show that individual instances of regular geometries will always create qualitative and quantitative errors. These can be reduced by the use of multiple randomized instances, though this still creates scale-dependent biases. In contrast, virtual landscapes formed using irregular geometries can represent complex real-world landscapes without error. We found that the potential for bias caused by regular geometries can be effectively eliminated by subdividing virtual landscapes using irregular geometry. The use of irregular geometry appears to offer spatial modellers other potential advantages, which are as yet underdeveloped. We recommend their use in all spatially explicit models, but especially for predictive models that are used in decisionmaking.     

Phenomenological and molecular-level Petri net modeling and simulation of long-term potentiation

Petri net-based modeling methods have been used in many research projects to represent biological systems. Among these, the hybrid functional Petri net (HFPN) was developed especially for biological modeling in order to provide biologists with a more intuitive Petri net-based method. In the literature, HFPNs are used to represent kinetic models at the molecular level. We present two models of long-term potentiation previously represented by differential equations which we have transformed into HFPN models: a phenomenological synapse model and a molecular-level model of the CaMKII regulation pathway. Through simulation, we obtained results similar to those of previous studies using these models. Our results open the way to a new type of modeling for systems biology where HFPNs are used to combine different levels of abstraction within one model. This approach can be useful in fully modeling a system at the molecular level when kinetic data is missing or when a full study of a system at the molecular level it is not within the scope of the research.     

Modified Gaussian estimation for correlated binary data

In this paper, we develop a Gaussian estimation (GE) procedure to estimate the parameters of a regression model for correlated (longitudinal) binary response data using a working correlation matrix. A two‐step iterative procedure is proposed for estimating the regression parameters by the GE method and the correlation parameters by the method of moments. Consistency properties of the estimators are discussed. A simulation study was conducted to compare 11 estimators of the regression parameters, namely, four versions of the GE, five versions of the generalized estimating equations (GEEs), and two versions of the weighted GEE. Simulations show that (i) the Gaussian estimates have the smallest mean square error and best coverage probability if the working correlation structure is correctly specified and (ii) when the working correlation structure is correctly specified, the GE and the GEE with exchangeable correlation structure perform best as opposed to when the correlation structure is misspecified.     

Electrical properties of frog skeletal muscle fibers interpreted with a mesh model of the tubular system.

This paper presents the construction, derivation, and test of a mesh model for the electrical properties of the transverse tubular system (T-system) in skeletal muscle. We model the irregular system of tubules as a random network of miniature transmission lines, using differential equations to describe the potential between the nodes and difference equations to describe the potential at the nodes. The solution to the equations can be accurately represented in several approximate forms with simple physical and graphical interpretations. All the parameters of the solution are specified by impedance and morphometric measurements. The effect of wide circumferential spacing between T-system openings is analyzed and the resulting restricted mesh model is shown to be approximated by a mesh with an access resistance. The continuous limit of the mesh model is shown to have the same form as the disk model of the T-system, but with a different expression for the tortuosity factor. The physical meaning of the tortuosity factor is examined, and a short derivation of the disk model is presented that gives results identical to the continuous limit of the mesh model. Both the mesh and restricted mesh models are compared with experimental data on the impedance of muscle fibers of the frog sartorius. The derived value for the resistivity of the lumen of the tubules is not too different from that of the bathing solution, the difference probably arising from the sensitivity of this value to errors in the morphometric measurements.     

PSysCal: a parallel tool for calibration of ecosystem models

The methods used for ecosystem modelling are generally based on differential equations. Nowadays, new computational models based on concurrent processing of multiple agents (multi-agents) or the simulation of biological processes with the Population Dynamic P-System models (PDPs) are gaining importance. These models have significant advantages over traditional models, such as high computational efficiency, modularity and its ability to model the interaction between different biological processes which operate concurrently. By this, they are becoming useful for simulating complex dynamic ecosystems, untreatable with classical techniques. On the other hand, the main counterpart of P-System models is the need for calibration. The model parameters represent the field measurements taken by experts. However, the exact values of some of these parameters are unknown and experts define a numerical interval of possible values. Therefore, it is necessary to perform a calibration process to fit the best value of each interval. When the number of unknown parameters increases, the calibration process becomes computationally complex and storage requirements increase significantly. In this paper, we present a parallel tool (PSysCal) for calibrating next generation PDP models. The results shown that the calibration time is reduced exponentially with the amount of computational resources. However, the complexity of the calibration process and a limitation in the number of available computational resources make the calibration process intractable for large models. To solve this, we propose a heuristic technique (PSysCal+H). The results show that this technique significantly reduces the computational cost, it being practical for solving large model instances even with limited computational resources.     

新型染料木素磺酸酯的前药判定与大鼠体内生物利用度

为寻找染料木素(GE)新的前药,采用建立的生物样品中药物浓度测定的液相色谱法对新型大豆异黄酮染料木素磺酸酯(GB)进行前药判定以及大鼠体内药物动力学研究,以考察前药中染料木素的口服生物利用度是否改善.在大鼠体内药物代谢实验中,灌胃给予GB的大鼠血浆中能检测到明显的GE.在临床前药物动力学实验中,该前体静注给药和以40 mg/kg灌胃药后,GE在大鼠体内的动力学过程均符合一室模型.GB中GE的相对口服生物利用度为原药的110.9％.研究表明,相对于原药GE,前药中GE的相对口服生物利用度达到预期的改善,该前药有进一步研究意义.     

Statistical models of synaptic transmission evaluated using the expectation-maximization algorithm.

Amplitude fluctuations of evoked synaptic responses can be used to extract information on the probabilities of release at the active sites, and on the amplitudes of the synaptic responses generated by transmission at each active site. The parameters that describe this process must be obtained from an incomplete data set represented by the probability density of the evoked synaptic response. In this paper, the equations required to calculate these parameters using the Expectation-Maximization algorithm and the maximum likelihood criterion have been derived for a variety of statistical models of synaptic transmission. These models are ones where the probabilities associated with the different discrete amplitudes in the evoked responses are a) unconstrained, b) binomial, and c) compound binomial. The discrete amplitudes may be separated by equal (quantal) or unequal amounts, with or without quantal variance. Alternative models have been considered where the variance associated with the discrete amplitudes is sufficiently large such that no quantal amplitudes can be detected. These models involve the sum of a normal distribution (to represent failures) and a unimodal distribution (to represent the evoked responses). The implementation of the algorithm is described in each case, and its accuracy and convergence have been demonstrated.     

Insights into an Unusual Nonribosomal Peptide Synthetase Biosynthesis: IDENTIFICATION AND CHARACTERIZATION OF THE GE81112 BIOSYNTHETIC GENE CLUSTER*

The GE81112 tetrapeptides (1–3) represent a structurally unique class of antibiotics, acting as specific inhibitors of prokaryotic protein synthesis. Here we report the cloning and sequencing of the GE81112 biosynthetic gene cluster from Streptomyces sp. L-49973 and the development of a genetic manipulation system for Streptomyces sp. L-49973. The biosynthetic gene cluster for the tetrapeptide antibiotic GE81112 (getA-N) was identified within a 61.7-kb region comprising 29 open reading frames (open reading frames), 14 of which were assigned to the biosynthetic gene cluster. Sequence analysis revealed the GE81112 cluster to consist of six nonribosomal peptide synthetase (NRPS) genes encoding incomplete di-domain NRPS modules and a single free standing NRPS domain as well as genes encoding other biosynthetic and modifying proteins. The involvement of the cloned gene cluster in GE81112 biosynthesis was confirmed by inactivating the NRPS gene getE resulting in a GE81112 production abolished mutant. In addition, we characterized the NRPS A-domains from the pathway by expression in Escherichia coli and in vitro enzymatic assays. The previously unknown stereochemistry of most chiral centers in GE81112 was established from a combined chemical and biosynthetic approach. Taken together, these findings have allowed us to propose a rational model for GE81112 biosynthesis. The results further open the door to developing new derivatives of these promising antibiotic compounds by genetic engineering.     

Phenotyping of genetically engineered mice: humane, ethical, environmental, and husbandry issues

The growing use of genetically engineered (GE) mice in scientific research has raised many concerns about the animal welfare of such mice. The types of welfare concerns may differ within the three stages that comprise the establishment of GE animal models: development, production, and research use. The role and impact of the members of the research team on these concerns may also vary with each stage. To make both scientific and animal welfare decisions at each stage, it is necessary to have a thorough knowledge of the animal model-in this case, the phenotypic expression of the GE animal. Phenotype screening is the analysis of visible or measurable characteristics of an animal that result from the genotype and its interaction with the environment. Phenotypes expressed that are relevant to the research program are usually carefully investigated; however, those that may have an impact on the animal's welfare but have little or no impact on the disease process under study are often less carefully studied. Thorough analysis and documentation of the animal welfare aspects of phenotype provide the research team with the information they need to control the environment to minimize negative animal welfare effects. Such information is also essential to allow members of the institutional animal care and use committee to perform necessary cost:benefit ethical review of proposed GE animal studies. Investigators who submit information about models for publication should document all aspects of a phenotype, including the area of scientific interest as well as those areas that affect animal welfare, for clarity and for subsequent research with the respective models.     

Scale effect on rice pollen‐mediated gene flow: implications in assessing transgene flow from genetically engineered plants

Transgene flow from genetically engineered (GE) crops to non‐GE varieties and wild relatives via pollen‐mediated gene flow (PMGF) may create food and environmental biosafety concerns. Assessing the level of PMGF from GE crops is required before commercialization. Whether the level of PMGF estimated at relatively small scales can sufficiently represent the actual scenario at large production scales remains unresolved. Here, we estimated average PMGF frequencies from three insect‐resistant GE rice lines to their non‐GE counterparts at four scales ranging from 9 to 576 m², having the number of GE to non‐GE plants constantly at the ratio of 8:1. Based on nearly 1.3 million examined seedlings from non‐GE rice plots, very low frequencies (<0.1%) of transgene flow were detected. The highest frequencies were found in plots at the smallest scales. Scale had a significantly negative effect on the frequency of PMGF in rice, with decreased gene flow at increased scale. An extended PMGF model could well represent the experimental data. Field experiments at relatively small scales combined with mathematical modelling could provide useful prediction on the level of rice transgene flow at large production scales. This is probably applicable for other crop species with wind‐ and self‐pollination.     

The dynamics of production and destruction: Analytic insight into complex behavior

This paper analytically explores the properties of simple differential-difference equations that represent dynamic processes with feedback dependent on prior states of the system. Systems with pure negative and positive feedback are examined, as well as those with mixed (positive/negative) feedback characteristics. Very complex time dependent behaviors may arise from these processes. Indeed, the same mechanism may, depending on system parameters and initial conditions, produce simple, regular, repetitive patterns and completely irregular random-like fluctuations.For the differential-delay equations considered here we prove the existence of: (i) stable and unstable limit cycles, where the stable cycles may have an arbitrary number of extrema per period; and (ii) chaos, meaning the presence of infinitely many periodic solutions of different period and of infinitely many irregular and mixing solutions.     

Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center''s (CIMMYT''s) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT''s maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.     

Triage protein fold prediction

We have constructed, in a completely automated fashion, a new structure template library for threading that represents 358 distinct SCOP folds where each model is mathematically represented as a Hidden Markov model (HMM). Because the large number of models in the library can potentially dilute the prediction measure, a new triage method for fold prediction is employed. In the first step of the triage method, the most probable structural class is predicted using a set of manually constructed, high-level, generalized structural HMMs that represent seven general protein structural classes: all-alpha, all-beta, alpha/beta, alpha+beta, irregular small metal-binding, transmembrane beta-barrel, and transmembrane alpha-helical. In the second step, only those fold models belonging to the determined structural class are selected for the final fold prediction. This triage method gave more predictions as well as more correct predictions compared with a simple prediction method that lacks the initial classification step. Two different schemes of assigning Bayesian model priors are presented and discussed.     

新型大豆异黄酮磺酸酯的临床前药物动力学

为寻找新的大豆异黄酮前药,采用建立的生物样品中药物浓度测定的液相色谱法对新型大豆异黄酮染料木素磺酸酯(GBS)进行前药判定以及大鼠体内药物动力学研究,以考察前药中染料木素(GE)的口服相对生物利用度是否改善。在大鼠体内药物代谢实验中,灌胃给予的大鼠血浆中能检测到GE的存在。在临床前药物动力学实验中,该前体药以40mg/kg GE在大鼠体内的动力学过程符合一室模型。GBS中GE的相对口服生物利用度为原药的198.6%。结果表明:相对于原药GE,前药中GE的相对口服生物利用度得到极大地改善。该前药有进一步研究的意义。     

A simulation support system for solving large physiological models on microcomputers

Although physiological modeling and computer simulation have become useful research tools to test new scientific theories and to design and analyze laboratory experiments, developing a new model can be a tedious process because the investigator must often write very complex and specific routines for data input and output. To facilitate the design of new models (as well as the use of existing models), we have developed MODSIM, a FORTRAN-based simulation support system for the IBM PC computer than can accommodate very large dynamic models having up to several thousand equations. It provides the investigator with utilities for continuous on-line graphical and/or tubular output, as well as facilities for dynamic interaction with the model. The user must only supply a model as a list of mathematical equations written in FORTRAN, along with the initial values of the model variables and parameters. The model is precompiled, compiled, and then linked to the MODSIM utilities. Without further programming, the user can then solve the model, select variables for graphical output, and stop the model at any time to analyze the data or to change a parameter before resuming the simulation. This simulation system makes it very easy to develop new models that actively interact with the experimental research of the investigator.     

Constitutive equations for the lung tissue

The mechanical behavior of the lung tissue (expressed by its constitutive equations) has considerable influence on the normal and pathological function of the lung. It determines the stress field in the tissue, thus affecting the impedence and energy consumption during breathing as well as the localization of certain lung diseases. The lung tissue has a complex mechanical response. It arises from the tissue's structure--a cluster of a very large number of closely packed airsacks (alveoli) and air ducts. Each of the alveoli has a shape of irregular polyhedron. It is bounded by the alveolar wall membrane. In the present study, a stochastic approach to the tissue's structure will be employed. The density distribution function of the membrane's orientation in space is considered as the predominant structural parameter. Based on this model the present theory relates the behavior of both the alveolar membrane and that of its liquid interface to the tissue's general constitutive properties. The resulting equations allow for anisotropic and visco-elastic effects. A protocol for material characterization along the present model is proposed as well. The methodology of the present theory is quite general and can be similarly used with other structural models of the lung tissue (e.g., models in which the effect of the alveolar ducts is included).