Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models

Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene–gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.     

拓扑指数在生物序列相似性比较中的应用

在生物序列的二维图形表示的基础上,利用Balaban指数和信息分布指数比较生物序列的相似性,我们以包括人类等9种不同物种的DNA序列和yar029w等6种蛋白质为例来说明该方法的使用．     

生物实验数据的单因素方差分析

结合实例详细地叙述了生物学实验数据的单因素方差分析计算方法,介绍了根据计算结果如何分析组群间的差异显著性,具有较强的实用意义.     

A Nonlocal Continuum Model for Biological Aggregation

We construct a continuum model for biological aggregations in which individuals experience long-range social attraction and short-range dispersal. For the case of one spatial dimension, we study the steady states analytically and numerically. There exist strongly nonlinear states with compact support and steep edges that correspond to localized biological aggregations, or clumps. These steady-state clumps are reached through a dynamic coarsening process. In the limit of large population size, the clumps approach a constant density swarm with abrupt edges. We use energy arguments to understand the nonlinear selection of clump solutions, and to predict the internal density in the large population limit. The energy result holds in higher dimensions as well, and is demonstrated via numerical simulations in two dimensions.     

Hidden Markov Models and their Applications in Biological Sequence Analysis

Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and many others.Key Words: Hidden Markov model (HMM), pair-HMM, profile-HMM, context-sensitive HMM (csHMM), profile-csHMM, sequence analysis.     

Analysis of Time-Varying Biological Data Using Rainflow Cycle Counting

A wide range of biological investigations lead to time-history data. The characterization of such data can be difficult particularly in the presence of signal noise or superimposed signals. Several methods are described which can be brought to bear including FFT, thresholding, peak counting, and range counting. However, each of these approaches has significant disadvantages. In this paper we describe a novel method, known as rainflow cycle counting, for characterizing time varying biological time-history data in terms of spiking or oscillation amplitude and frequency. Rainflow counting is a straightforward algorithm for identifying complete cycles in the data and determining their amplitudes. The approach is simple, reliable, easily lends itself to automation, and robust even in the presence of superimposed signals or background noise. After describing the method, its use and behavior are demonstrated on three sample histories of intracellular calcium concentration in chondrocytes exposed to fluid shear stress. The method is also applied to a more challenging data set that has had an artificial random error included. The results demonstrate that the rainflow counting algorithm identifies signal oscillations and appropriately determines their amplitudes even when superimposed or distorted by background noise. These attractive properties make rainflow counting a powerful approach for quantifying and characterizing biological time histories.     

基因芯片数据分析过程:从原始数据到生物学意义

基因芯片实验要得到可靠的生物学结论,必须基于优化的实验设计和科学的数据分析。讨论了与基因芯片数据分析方法相关的实验设计方面的几个问题,简述了差异表达分析、聚类分析及功能富集分析等分析方法及其进展,并介绍了部分软件及应用。     

Aggregation Properties of Biological Amphiphiles: Micelles and Vesicles

Abstract

Selfaggregation of amphiphilic molecules in aqueous solutions is discussed in terms of their geometrical properties. Gangliosides, sialic-acid containing glycosphingolipids, are an interesting example of biological amphiphiles which can selfaggregate into different shapes ranging from vesicles to micelles, depending on the relative extension and conformation of their saccharidic headgroups. The remarkable differences in the mechanical properties of ganglioside and lecitine vesicles are also discussed by means of geometrical considerations.     

Investigation of the Effect of Data Error in the Analysis of Biological Tracer Data

Data obtained from tracer studies often consist of serial measurements after administration of radioisotope. Very little work has been published on how the error in the data affects the mathematical analysis. Computer simulation was here employed to produce data with error of different magnitude and form for each of several values of rate constant and amplitude. The data were terminated when the value of the last point was 5% of the value of the first point, and also in other ways arranged to simulate experimental situations. The sets of simulated data for a two compartment system were analyzed by the gaussian iterative technique. With a rate constant ratio of at least four the technique converged for data errors of 5% or less. The calculated error in the rate constants ranged from 2 to 85%, and in the amplitudes from 1 to 50%, for data error of 0.5 to 10%. The lesser rate constant and amplitude had the greater errors. If a wrong assumption was made in the analysis about the variation of data error over the time interval of measurement, then the calculated values of parameter standard deviations were greatly in error. The results can be used to decide what experimental accuracy is needed for a given accuracy of model parameters for a variety of biological problems.     

Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

Modern high-throughput biotechnologies such as microarray and next-generation sequencing produce a massive amount of information for each sample assayed. However, in a typical high-throughput experiment, only limited amount of data are observed for each individual feature, thus the classical “large p, small n” problem. Bayesian hierarchical model, capable of borrowing strength across features within the same dataset, has been recognized as an effective tool in analyzing such data. However, the shrinkage effect, the most prominent feature of hierarchical features, can lead to undesirable over-correction for some features. In this work, we discuss possible causes of the over-correction problem and propose several alternative solutions. Our strategy is rooted in the fact that in the Big Data era, large amount of historical data are available which should be taken advantage of. Our strategy presents a new framework to enhance the Bayesian hierarchical model. Through simulation and real data analysis, we demonstrated superior performance of the proposed strategy. Our new strategy also enables borrowing information across different platforms which could be extremely useful with emergence of new technologies and accumulation of data from different platforms in the Big Data era. Our method has been implemented in R package “adaptiveHM,” which is freely available from https://github.com/benliemory/adaptiveHM.     

N-Acetyl-L-Cysteine Prevents Stress-Induced Desmin Aggregation in Cellular Models of Desminopathy

Mutations within the human desmin gene are responsible for a subcategory of myofibrillar myopathies called desminopathies. However, a single inherited mutation can produce different phenotypes within a family, suggesting that environmental factors influence disease states. Although several mouse models have been used to investigate organ-specific desminopathies, a more general mechanistic perspective is required to advance our knowledge toward patient treatment. To improve our understanding of disease pathology, we have developed cellular models to observe desmin behaviour in early stages of disease pathology, e.g., upon formation of cytoplasmic desmin aggregates, within an isogenic background. We cloned the wildtype and three mutant desmin cDNAs using a Tet-On Advanced® expression system in C2C12 cells. Mutations were selected based on positioning within desmin and capacity to form aggregates in transient experiments, as follows: DesS46Y (head domain; low aggregation), DesD399Y (central rod domain; high aggregation), and DesS460I (tail domain; moderate aggregation). Introduction of these proteins into a C2C12 background permitted us to compare between desmin variants as well as to determine the role of external stress on aggregation. Three different types of stress, likely encountered during muscle activity, were introduced to the cell models—thermal (heat shock), redox-associated (H₂O₂ and cadmium chloride), and mechanical (stretching) stresses—after which aggregation was measured. Cells containing variant DesD399Y were more sensitive to stress, leading to marked cytoplasmic perinuclear aggregations. We then evaluated the capacity of biochemical compounds to prevent this aggregation, applying dexamethasone (an inducer of heat shock proteins), fisetin or N-acetyl-L-cysteine (antioxidants) before stress induction. Interestingly, N-acetyl-L-cysteine pre-treatment prevented DesD399Y aggregation during most stress. N-acetyl-L-cysteine has recently been described as a promising antioxidant in myopathies linked to selenoprotein N or ryanodin receptor defects. Our findings indicate that this drug warrants further study in animal models to speed its potential development as a therapy for DesD399Y-linked desminopathies.