Computational simulations of stem-cell factor/c-Kit receptor interaction

Stem-cell factor (SCF) is a noncovalent homodimeric cytokine that exhibits profound biological function in the early stages of hematopoiesis by binding to a cell surface tyrosine kinase receptor that is encoded by the c-Kit proto-oncogene. The results obtained from a combined implementation of homology-based molecular modeling and computational simulations in the study of species-specific SCF/c-Kit interactions are reported. The structural models of the human and rat SCF ligands are based on the close structural similarity to the cytokine M-CSF, whose Cα structure has recently become available. The constant domains of the human Fc fragment are used as a template for the ligand binding domains of the c-Kit receptor. The factors responsible for the stabilization of the SCF quaternary structure and the molecular determinants for ligand recognition and ligand specificity have been identified by assessing the conformational, topographical, and dynamic features of the isolated ligands and of the ligand-receptor complexes. © 1996 Wiley-Liss, Inc.     

GID2, an F-box subunit of the SCF E3 complex, specifically interacts with phosphorylated SLR1 protein and regulates the gibberellin-dependent degradation of SLR1 in rice

The phytohormone gibberellin (GA) controls growth and development in plants. Previously, we identified a rice F-box protein, gibberellin-insensitive dwarf2 (GID2), which is essential for GA-mediated DELLA protein degradation. In this study, we analyzed the biological and molecular biological properties of GID2. Expression of GID2 preferentially occurred in rice organs actively synthesizing GA. Domain analysis of GID2 revealed that the C-terminal regions were essential for the GID2 function, but not the N-terminal region. Yeast two-hybrid assay and immunoprecipitation experiments demonstrated that GID2 is a component of the SCF complex through an interaction with a rice ASK1 homolog, OsSkp15. Furthermore, an in vitro pull-down assay revealed that GID2 specifically interacted with the phosphorylated Slender Rice 1 (SLR1). Taken these results together, we conclude that the phosphorylated SLR1 is caught by the SCFGID2 complex through an interacting affinity between GID2 and phosphorylated SLR1, triggering the ubiquitin-mediated degradation of SLR1.     

Antibody-antigen interactions measured by surface plasmon resonance: global fitting of numerical integration algorithms

The interactions between adenylate kinase (AK) and a monoclonal antibody against AK (McAb3D3) were examined by means of optical biosensor technology, and the sensograms were fitted to four models using numerical integration algorithms. The interaction of a solution of McAb3D3 with immobilized AK follows a double exponential function and the data fitted well to an inhomogeneous ligand model. The interaction of a solution AK with immobilized McAb3D3 follows a single exponential function and the data fitted well to a pseudo-first order reaction model. The true association constants of AK binding to McAb3D3 in solution were obtained from competition BIAcore measurements. The difference in results obtained with solid-phase BIAcore and competition BIAcore may be due to rebinding of the dissociated analyte to the immobilized surface. The results obtained with BIAcore are compared to those obtained by ELISA methods. We suggest that the best method for analysis of BIAcore data is direct, global fitting of sensorgrams to numerical integration algorithms corresponding to the different possible models for binding.     

Two-stage, self-cycling process for the production of bacteriophages

Background

A two-stage, self-cycling process for the production of bacteriophages was developed. The first stage, containing only the uninfected host bacterium, was operated under self-cycling fermentation (SCF) conditions. This automated method, using the derivative of the carbon dioxide evolution rate (CER) as the control parameter, led to the synchronization of the host bacterium. The second stage, containing both the host and the phage, was operated using self-cycling infection (SCI) with CER and CER-derived data as the control parameters. When each infection cycle was terminated, phages were harvested and a new infection cycle was initiated by adding host cells from the SCF (first stage). This was augmented with fresh medium and the small amount of phages left from the previous cycle initiated the next infection cycle. Both stages were operated independently, except for this short period of time when the SCF harvest was added to the SCI to initiate the next cycle.

Results

It was demonstrated that this mode of operation resulted in stable infection cycles if the growth of the host cells in the SCF was synchronized. The final phage titers obtained were reproducible among cycles and were as good as those obtained in batch productions performed under the same conditions (medium, temperature, initial multiplicity of infection, etc.). Moreover, phages obtained in different cycles showed no important difference in infectivity. Finally, it was shown that cell synchronization of the host cells in the first stage (SCF) not only maintained the volumetric productivity (phages per volume) but also led to higher specific productivity (phage per cell per hour) in the second stage (SCI).

Conclusions

Production of bacteriophage T4 in the semi-continuous, automated SCF/SCI system was efficient and reproducible from cycle to cycle. Synchronization of the host in the first stage prior to infection led to improvements in the specific productivity of phages in the second stage while maintaining the volumetric productivity. These results demonstrate the significant potential of this approach for both upstream and downstream process optimization.     

Genetic noise control via protein oligomerization

Background

Biochemical equilibria are usually modeled iteratively: given one or a few fitted models, if there is a lack of fit or over fitting, a new model with additional or fewer parameters is then fitted, and the process is repeated. The problem with this approach is that different analysts can propose and select different models and thus extract different binding parameter estimates from the same data. An alternative is to first generate a comprehensive standardized list of plausible models, and to then fit them exhaustively, or semi-exhaustively.

Results

A framework is presented in which equilibriums are modeled as pairs (g, h) where g = 0 maps total reactant concentrations (system inputs) into free reactant concentrations (system states) which h then maps into expected values of measurements (system outputs). By letting dissociation constants K _d be either freely estimated, infinity, zero, or equal to other K _d , and by letting undamaged protein fractions be either freely estimated or 1, many g models are formed. A standard space of g models for ligand-induced protein dimerization equilibria is given. Coupled to an h model, the resulting (g, h) were fitted to dTTP induced R1 dimerization data (R1 is the large subunit of ribonucleotide reductase). Models with the fewest parameters were fitted first. Thereafter, upon fitting a batch, the next batch of models (with one more parameter) was fitted only if the current batch yielded a model that was better (based on the Akaike Information Criterion) than the best model in the previous batch (with one less parameter). Within batches models were fitted in parallel. This semi-exhaustive approach yielded the same best models as an exhaustive model space fit, but in approximately one-fifth the time.

Conclusion

Comprehensive model space based biochemical equilibrium model selection methods are realizable. Their significance to systems biology as mappings of data into mathematical models warrants their development.     

The Arabidopsis SLEEPY1 gene encodes a putative F-box subunit of an SCF E3 ubiquitin ligase

The Arabidopsis SLY1 (SLEEPY1) gene positively regulates gibberellin (GA) signaling. Positional cloning of SLY1 revealed that it encodes a putative F-box protein. This result suggests that SLY1 is the F-box subunit of an SCF E3 ubiquitin ligase that regulates GA responses. The DELLA domain protein RGA (repressor of ga1-3) is a repressor of GA response that appears to undergo GA-stimulated protein degradation. RGA is a potential substrate of SLY1, because sly1 mutations cause a significant increase in RGA protein accumulation even after GA treatment. This result suggests SCF(SLY1)-targeted degradation of RGA through the 26S proteasome pathway. Further support for this model is provided by the observation that an rga null allele partially suppresses the sly1-10 mutant phenotype. The predicted SLY1 amino acid sequence is highly conserved among plants, indicating a key role in GA response.     

Optimum DNA curvature using a hybrid approach involving an artificial neural network and genetic algorithm

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the RL value. Using the ANN-GA methodology, a number of sequences possessing high RL values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high RL values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.     

Parameter optimization by using differential elimination: a general approach for introducing constraints into objective functions

Background

The investigation of network dynamics is a major issue in systems and synthetic biology. One of the essential steps in a dynamics investigation is the parameter estimation in the model that expresses biological phenomena. Indeed, various techniques for parameter optimization have been devised and implemented in both free and commercial software. While the computational time for parameter estimation has been greatly reduced, due to improvements in calculation algorithms and the advent of high performance computers, the accuracy of parameter estimation has not been addressed.

Results

We propose a new approach for parameter optimization by using differential elimination, to estimate kinetic parameter values with a high degree of accuracy. First, we utilize differential elimination, which is an algebraic approach for rewriting a system of differential equations into another equivalent system, to derive the constraints between kinetic parameters from differential equations. Second, we estimate the kinetic parameters introducing these constraints into an objective function, in addition to the error function of the square difference between the measured and estimated data, in the standard parameter optimization method. To evaluate the ability of our method, we performed a simulation study by using the objective function with and without the newly developed constraints: the parameters in two models of linear and non-linear equations, under the assumption that only one molecule in each model can be measured, were estimated by using a genetic algorithm (GA) and particle swarm optimization (PSO). As a result, the introduction of new constraints was dramatically effective: the GA and PSO with new constraints could successfully estimate the kinetic parameters in the simulated models, with a high degree of accuracy, while the conventional GA and PSO methods without them frequently failed.

Conclusions

The introduction of new constraints in an objective function by using differential elimination resulted in the drastic improvement of the estimation accuracy in parameter optimization methods. The performance of our approach was illustrated by simulations of the parameter optimization for two models of linear and non-linear equations, which included unmeasured molecules, by two types of optimization techniques. As a result, our method is a promising development in parameter optimization.

     

Automated ensemble modeling with modelMaGe: analyzing feedback mechanisms in the Sho1 branch of the HOG pathway

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback.modelMaGe is an open source project and is distributed under the Gnu General Public License (GPL) and is available from http://modelmage.org.     

Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications

MOTIVATION: The analysis of metabolic processes is becoming increasingly important to our understanding of complex biological systems and disease states. Nuclear magnetic resonance spectroscopy (NMR) is a particularly relevant technology in this respect, since the NMR signals provide a quantitative measure of the metabolite concentrations. However, due to the complexity of the spectra typical of biological samples, the demands of clinical and high-throughput analysis will only be fully met by a system capable of reliable, automatic processing of the spectra. An initial step in this direction has been taken by Targeted Profiling (TP), employing a set of known and predicted metabolite signatures fitted against the signal. However, an accurate fitting procedure for (1)H NMR data is complicated by shift uncertainties in the peak systems caused by measurement imperfections. These uncertainties have a large impact on the accuracy of identification and quantification and currently require compensation by very time consuming manual interactions. Here, we present an approach, termed Extended Targeted Profiling (ETP), that estimates shift uncertainties based on a genetic algorithm (GA) combined with a least squares optimization (LSQO). The estimated shifts are used to correct the known metabolite signatures leading to significantly improved identification and quantification. In this way, use of the automated system significantly reduces the effort normally associated with manual processing and paves the way for reliable, high-throughput analysis of complex NMR spectra. RESULTS: The results indicate that using simultaneous shift uncertainty correction and least squares fitting significantly improves the identification and quantification results for (1)H NMR data in comparison to the standard targeted profiling approach and compares favorably with the results obtained by manual expert analysis. Preservation of the functional structure of the NMR spectra makes this approach more realistic than simple binning strategies.     

Firing-rate models capture essential response dynamics of LGN relay cells

Firing-rate models provide a practical tool for studying signal processing in the early visual system, permitting more thorough mathematical analysis than spike-based models. We show here that essential response properties of relay cells in the lateral geniculate nucleus (LGN) can be captured by surprisingly simple firing-rate models consisting of a low-pass filter and a nonlinear activation function. The starting point for our analysis are two spiking neuron models based on experimental data: a spike-response model fitted to data from macaque (Carandini et al. J. Vis., 20(14), 1–2011, 2007), and a model with conductance-based synapses and afterhyperpolarizing currents fitted to data from cat (Casti et al. J. Comput. Neurosci., 24(2), 235–252, 2008). We obtained the nonlinear activation function by stimulating the model neurons with stationary stochastic spike trains, while we characterized the linear filter by fitting a low-pass filter to responses to sinusoidally modulated stochastic spike trains. To account for the non-Poisson nature of retinal spike trains, we performed all analyses with spike trains with higher-order gamma statistics in addition to Poissonian spike trains. Interestingly, the properties of the low-pass filter depend only on the average input rate, but not on the modulation depth of sinusoidally modulated input. Thus, the response properties of our model are fully specified by just three parameters (low-frequency gain, cutoff frequency, and delay) for a given mean input rate and input regularity. This simple firing-rate model reproduces the response of spiking neurons to a step in input rate very well for Poissonian as well as for non-Poissonian input. We also found that the cutoff frequencies, and thus the filter time constants, of the rate-based model are unrelated to the membrane time constants of the underlying spiking models, in agreement with similar observations for simpler models.     

人干细胞因子受体c-Kit稳定表达细胞株的构建

 干细胞因子 (SCF)是一种重要的造血因子 ,其受体c Kit具有酪氨酸激酶活性 .SCF c Kit介导的细胞内信号转导在造血、肥大细胞的生成及其功能、以及生殖细胞和黑色素细胞的发育中起着关键的作用 .通过构建人c kitcDNA的pcDNA3.1真核表达载体 ,转染不表达人c kit的小鼠髓系祖细胞FDC P1.经Zeocin抗性筛选 ,PCR、RT PCR、Western印迹分析、流式细胞仪分析等方法检测到人c kit基因的稳定整合和表达 ,并分布于细胞表面 ,证明获得了稳定表达人c Kit受体的细胞株 .用MTT法检测重组细胞株增殖特性 ,表明重组人SCF可刺激其增殖 .为进一步研究人c Kit受体介导的细胞内信号转导及检测重组人干细胞因子生物学活性提供了有效的细胞模型     

Optimum DNA Curvature Using a Hybrid Approach Involving an Artificial Neural Network and Genetic Algorithm

Abstract

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the R_L value. Using the ANN-GA methodology, a number of sequences possessing high R_L values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high R_L values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.     

Biological effectiveness of low energy protons. I. Survival of Chinese hamster cells

The biological effectiveness of monoenergetic protons was investigated with the track-segment method. Protons were accelerated by a Tandem Van de Graaff accelerator and their final energies were 3.0 and 7.4 MeV. The biological system used was Chinese hamster V-79 cells and their survival ability following proton irradiation was investigated. Cobalt-60 gamma-rays were used as reference radiation to assess proton relative biological effectiveness (RBE). Survival curves were obtained for the gamma-ray and proton irradiations, and the relation S = exp (-alpha D-beta D2) was fitted to the data and the parameters alpha and beta were determined. The RBE values, calculated on the basis of the mean inactivation dose D and other pertinent parameters, were found to be 1.7 +/- 0.1 and 2.8 +/- 0.2 for 7.4 and 3.0 MeV protons, respectively. Comparisons were made with the results published by other investigators and it was concluded that in this low energy range the biological effectiveness increases substantially with decreasing proton energy.     

The permeation of organic acids through lecithin bilayers. Resemblance to diffusion in polymers.

1. The fluxes of aliphatic acids and their derivatives through black lipid membranes made of egg lecithin in decane were measured by means of a proton titration method. 2. Permeability coefficients were calculated and these were divided by the partition coefficient of the diffusing solute in different solvent systems: n-decane, olive oil, ether and octanol. The logarithms of the diffusion coefficients thus obtained were plotted against the logarithm of the molecular weight. The data could not be fitted to a single regression line in any solvent system. 3. When the logarithm of the diffusion coefficients were correlated to the logarithm of the molecular volume (equals molecular weight/ specific gravity) all the diffusants could be fitted to the same regression line, indicating that the molecular volume is a better index of molecular size and shape than the molecular weight. 4. Analysis of the experimental results assuming a model of diffusion through soft polymers (Lieb, W.R. and Stein, W. D. (1971) Current Topics in Membranes and Transport, vol. 2, pp. 1-39, Academic Press, New York) showed that decane and olive oil are not adequate model solvents for planar lecithin membranes but ether and octanol are good models. 5. The differential mass selectivity coefficient was found to be similar to that for soft polymers and biological membranes, i.e. greater than 3.0. 6. Water could be fitted by the same regression line, thus emphasizing the generality of passive transfer and implying that water crosses lipid membranes as single molecules.     

Evolutionary model selection with a genetic algorithm: a case study using stem RNA

The choice of a probabilistic model to describe sequence evolution can and should be justified. Underfitting the data through the use of overly simplistic models may miss out on interesting phenomena and lead to incorrect inferences. Overfitting the data with models that are too complex may ascribe biological meaning to statistical artifacts and result in falsely significant findings. We describe a likelihood-based approach for evolutionary model selection. The procedure employs a genetic algorithm (GA) to quickly explore a combinatorially large set of all possible time-reversible Markov models with a fixed number of substitution rates. When applied to stem RNA data subject to well-understood evolutionary forces, the models found by the GA 1) capture the expected overall rate patterns a priori; 2) fit the data better than the best available models based on a priori assumptions, suggesting subtle substitution patterns not previously recognized; 3) cannot be rejected in favor of the general reversible model, implying that the evolution of stem RNA sequences can be explained well with only a few substitution rate parameters; and 4) perform well on simulated data, both in terms of goodness of fit and the ability to estimate evolutionary rates. We also investigate the utility of several distance measures for comparing and contrasting inferred evolutionary models. Using widely available small computer clusters, our approach allows, for the first time, to evaluate the performance of existing RNA evolutionary models by comparing them with a large pool of candidate models and to validate common modeling assumptions. In addition, the new method provides the foundation for rigorous selection and comparison of substitution models for other types of sequence data.