Parameter identification in epidemic models

Starting from a recent paper of Pollicott, Wang and Weiss we try to obtain improved representation formulas for the estimation of the time-dependent transmission rate of an epidemic in terms of either incidence or prevalence data. Although the formulas are (trivially) mathematically equivalent to previous formulas, the new representations need no additional estimates and they should be more stable numerically.We review the discrete time and the stochastic continuous time approach. We replace the assumption that recovery follows an exponential distribution and get estimates for the transmission rate for constant duration of the infectious phase.     

钙蛋白酶抑制蛋白转基因小鼠的构建和鉴定

目的：将人的钙蛋白酶抑制蛋白（ calpastatin, CAST）外源基因整合到C57BL/6J小鼠中,构建高表达CAST的转基因小鼠模型。方法利用Gateway技术构建pRP．EX3d-EF1A-CAST-IRES-eGFP载体,回收片段后通过显微注射法将目的基因片段注入到C57 BL/6 J小鼠受精卵中,将其胚胎移植至同期发情的假孕受体母鼠输卵管内获得子代小鼠。采用PCR方法鉴定出阳性的转基因小鼠,确定首建鼠,通过与C57BL/6J小鼠回交后互交数代建系。利用RT-PCR和Western blotting方法检测CAST基因和蛋白在各组织中的表达情况。结果将90枚注射受精卵移植到3只假孕鼠中,3只均怀孕,移植成功率100％,产下23只子鼠,经PCR鉴定得到2只转基因阳性首建鼠,阳性率为9％。子代小鼠进行RT-PCR检查显示,CAST基因在转基因小鼠的心、肝、脾、肺、肾、脑和骨骼肌中均有表达;Western blotting检查显示,CAST蛋白表达在转基因小鼠中显著高于同窝阴性小鼠。结论通过显微注射法成功构建CAST高表达的转基因小鼠,为进一步研究CAST奠定了良好的模型基础。     

Microbial gene identification using interpolated Markov models.

This paper describes a new system, GLIMMER, for finding genes in microbial genomes. In a series of tests on Haemophilus influenzae , Helicobacter pylori and other complete microbial genomes, this system has proven to be very accurate at locating virtually all the genes in these sequences, outperforming previous methods. A conservative estimate based on experiments on H.pylori and H. influenzae is that the system finds >97% of all genes. GLIMMER uses interpolated Markov models (IMMs) as a framework for capturing dependencies between nearby nucleotides in a DNA sequence. An IMM-based method makes predictions based on a variable context; i.e., a variable-length oligomer in a DNA sequence. The context used by GLIMMER changes depending on the local composition of the sequence. As a result, GLIMMER is more flexible and more powerful than fixed-order Markov methods, which have previously been the primary content-based technique for finding genes in microbial DNA.     

Parameter identification in N-compartment mamillary models

We consider a general mamillary model with a central compartment (compartment 1) and n?1 peripheral compartments, each bidirectionally connected to the first. Elimination is allowed from any compartment and effectively occurs from the system. With input introduced into an arbitrary compartment and measurement performed in an arbitrary compartment, explicit equations are given to derive the parameters of the model from the input-output procedure. The calculations include essentially the determination of the roots of a polynomial plus some elementary algebra. If input and measurement are performed in the same compartment, then a set of 2n elementary combinations of the model parameters can be uniquely determined. However, the model parameters themselves can only be localized, each within an interval. These intervals are explicitly calculated and their width discussed.     

The identification of nonlinear biological systems: LNL cascade models

Systems that can be represented by a cascade of a dynamic linear (L), a static nonlinear (N) and a dynamic linear (L) subsystem are considered. Various identification schemes that have been proposed for these LNL systems are critically reviewed with reference to the special problems that arise in the identification of nonlinear biological systems. A simulated LNL system is identified from limited duration input-output data using an iterative identification scheme.     

Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization

Three-dimensional reconstruction of large macromolecules like viruses at resolutions below 10 A requires a large set of projection images. Several automatic and semi-automatic particle detection algorithms have been developed along the years. Here we present a general technique designed to automatically identify the projection images of particles. The method is based on Markov random field modelling of the projected images and involves a pre-processing of electron micrographs followed by image segmentation and post-processing. The image is modelled as a coupling of two fields--a Markovian and a non-Markovian. The Markovian field represents the segmented image. The micrograph is the non-Markovian field. The image segmentation step involves an estimation of coupling parameters and the maximum á posteriori estimate of the realization of the Markovian field i.e, segmented image. Unlike most current methods, no bootstrapping with an initial selection of particles is required.     

COPid: composition based protein identification

In the past, a large number of methods have been developed for predicting various characteristics of a protein from its composition. In order to exploit the full potential of protein composition, we developed the web-server COPid to assist the researchers in annotating the function of a protein from its composition using whole or part of the protein. COPid has three modules called search, composition and analysis. The search module allows searching of protein sequences in six different databases. Search results list database proteins in ascending order of Euclidian distance or descending order of compositional similarity with the query sequence. The composition module allows calculation of the composition of a sequence and average composition of a group of sequences. The composition module also allows computing composition of various types of amino acids (e.g. charge, polar, hydrophobic residues). The analysis module provides the following options; i) comparing composition of two classes of proteins, ii) creating a phylogenetic tree based on the composition and iii) generating input patterns for machine learning techniques. We have evaluated the performance of composition-based (or alignment-free) similarity search in the subcellular localization of proteins. It was found that the alignment free method performs reasonably well in predicting certain classes of proteins. The COPid web-server is available at http://www.imtech.res.in/raghava/copid/.     

Nilotinib based pharmacophore models for BCRABL

Tyrosine kinase inhibitors have revolutionized the treatment of several malignancies, converting lethal diseases in a manageable aspect. Imitanib, a small molecule ABL kinase inhibitor is a highly effective therapy for early phase chronic myeloid leukemia (CML), which has constitutively active ABL kinase activity owing to the over expression of the BCR-ABL fusion protein. But some patients develop imatinib resistance, particularly in the advanced phases of CML.The discovery of resistance mechanisms of imitanib; urge forward the development of second generation drugs. Nilotinib, a second generation drug is more potent inhibitor of BCR-ABL than imatinib. But nilotinib also develops dermatologic events and headache in patients. Large information about BCR-ABL structure and its inhibitors are now available. Based on the pharmacophore modeling approaches, it is possible to decipher the molecular determinants to inhibit BCR-ABL. We conducted a structure based and ligand based study to identify potent natural compounds as BCR-ABL inhibitor. First kinase inhibitors were docked with the receptor (BCR-ABL) and nilotinib was selected as a pharmacophore due its high binding efficiency. Eleven compounds were selected out of 1457 substances which have mutual pharmacopohre features with nilotinib. These eleven compounds were validated and used for docking study to find the drug like molecules. The best molecules from the final set of screening candidates can be evaluated in cell lines and may represent a novel class of BCR-ABL inhibitors.

Abbreviations

CML - Chronic myeloid leukemia, PDGFR - Platelet derived growth factor receptor, TKI - Tyrosine kinase inhibitors.     

Experimental design for the identification of macrokinetic models and model discrimination

An experimental design method for the identification of macrokinetic models was developed applying an extended D-optimal design criterion. The D-optimal design criterion was modified to consider variable measurement variances as well as multivariate macrokinetic models. The macrokinetics of formate dehydrogenase (FDH) production with Candida boidinii were thus identified within 10 steady state experiments in a labscale continuous stirred tank reactor (10 model parameters). Closed loop control (nutristat) was applied to set-up the operating states suggested by this experimental design method. After each set of steady state experiments the quality of macrokinetic parameters was characterized statistically. For model discrimination a parameter discrimination algorithm based on entropy formulations was adapted. Again a multivariate criterion considering variable measurement variances was developed. This discrimination algorithm was applied to discriminate the macrokinetic model of FDH production with Candida boidinii out of 10 different macrokinetic approaches. An unequivocal discrimination result could be obtained calculating model specific probabilities. These were compared with commonly used sum of squares values. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 56: 564-576, 1997.     

A distributed parameter identification problem in neuronal cable theory models

Dendritic and axonal processes of nerve cells, along with the soma itself, have membranes with spatially distributed densities of ionic channels of various kinds. These ionic channels play a major role in characterizing the types of excitable responses expected of the cell type. These densities are usually represented as constant parameters in neural models because of the difficulty in experimentally estimating them. However, through microelectrode measurements and selective ion staining techniques, it is known that ion channels are non-uniformly spatially distributed. This paper presents a non-optimization approach to recovering a single spatially non-uniform ion density through use of temporal data that can be gotten from recording microelectrode measurements at the ends of a neural fiber segment of interest. The numerical approach is first applied to a linear cable model and a transformed version of the linear model that has closed-form solutions. Then the numerical method is shown to be applicable to non-linear nerve models by showing it can recover the potassium conductance in the Morris-Lecar model for barnacle muscle, and recover the spine density in a continuous dendritic spine model by Baer and Rinzel.     

基于颜色特征的昆虫自动鉴定方法

对昆虫实现自动鉴定是将相关专家从大量重复性的鉴定工作中解脱出来的最有效的方法之一。本章文着重论述昆虫自动鉴定系统鉴定的基本原理,开发步骤和使用昆虫自动鉴定系统进行自动鉴定的步骤,基于颜色特征的昆虫自动鉴定系统开发中在图像获取、颜色特征提取方面的难点和相应的解决方案。最后,经过测试开发出的基于颜色特征的蝴蝶自动鉴定系统在对43种蝴蝶的自动鉴定上取得了95.2%准确率。     

The role of physiologically based toxicokinetic models in biologically based risk assessment

Biologically-based cancer risk assessment relies on mathematical models that represent the toxicokinetics and toxicodynamics of the xenobiotic in the body. Physiologically-based toxicokinetic (PBTK) models are used as a tool for predicting the target tissue dose of a xenobiotic from different routes of exposure, extrapolating from high doses to low doses and extrapolating between species. This paper reviews the role of PBTK models and their limitations in biologically-based risk assessment.     

Temporal correlation based learning in neuron models

We study a learning rule based upon the temporal correlation (weighted by a learning kernel) between incoming spikes and the internal state of the postsynaptic neuron, building upon previous studies of spike timing dependent synaptic plasticity (Kempter, R., Gerstner, W., van Hemmen, J.L., Wagner, H., 1998. Extracting Oscillations: Neuronal coincidence detection with noisy periodic spike input. Neural computation 10, 1987–2017; Kempter, R., Gerstner, W., van Hemmen, J.L., 1999. Hebbian learning and spiking neurons. Physical Reviewm E59, 4498–4514; van Hemmen, J.L., 2001. Theory of synaptic plasticity. In: Moss, F., Gielen, S. (Eds.), Handbook of biological physics. vol. 4, Neuro Informatics, neural modelling, Elsevier, Amsterdam, pp. 771–823. Our learning rule for the synaptic weight w _ij is where the t _j,μ are the arrival times of spikes from the presynaptic neuron j and the function u(t) describes the state of the postsynaptic neuron i. Thus, the spike-triggered average contained in the inner integral is weighted by a kernel Γ(s), the learning window, positive for negative, negative for positive values of the time difference s between post- and presynaptic activity. An antisymmetry assumption for the learning window enables us to derive analytical expressions for a general class of neuron models and to study the changes in input-output relationships following from synaptic weight changes. This is a genuinely non-linear effect (Song, S., Miller, K., Abbott, L., 2000. Competitive Hebbian learning through spike timing dependent synaptic plasticity. Nature Neuroscience 3, 919–926).     

The identification of nonlinear biological systems: Wiener and Hammerstein cascade models

Systems that can be represented by a cascade of a dynamic linear subsystem preceded (Hammerstein cascade model) or followed (Wiener cascade model) by a static nonlinearity are considered. Various identification schemes that have been proposed for the Hammerstein and Wiener systems are critically reviewed with reference to the special problems that arise in the identification of nonlinear biological systems. Examples of Wiener and Hammerstein systems are identified from limited duration input-output data using an iterative identification scheme.