Automatic step-detection algorithm for analysis of sarcomere dynamics

Motile systems exhibit a stepwise nature, seen most prominently in muscle contraction. A novel algorithm has been developed that detects steps automatically in sarcomere-length change data and computes their size. The method is based on a nonlinear filter and a step detection protocol that detects local slope values in comparison to a threshold. The algorithm was first evaluated using artificial data with various degrees of Gaussian noise. Steps were faithfully detected even with significant noise. With actual data, the algorithm detected 2.7 nm steps and integer multiples thereof. The results confirm a quantal 2.7 nm step-size reported earlier. As stepwise phenomena become increasingly evident, automatic step-detection algorithms become increasingly useful since the limiting factor is almost always noise. The algorithm presented here offers a versatile and accurate tool that should be useful not only within muscle contraction and motility fields, but in fields which quantal phenomena play a role.     

Response of Photosynthesis Models to Light Limitation

An investigation into the effect of time step on a common photosynthesis algorithm reveals that the predicted phytoplankton production and biomass depend strongly on the length of the time step. This time step dependence is due to the assumption that a light limitation factor derived from integrating the irradiance over the time step is equivalent to the integrated light limitation factor over the time step. This subtle inaccuracy in defining the factor for light limited phytoplankton production produces a substantial difference in the biomass estimates derived from the two models. To illustrate the difference, the light limitation factor integrated over the time step is implemented in the one dimensional water quality model DYRESM-WQ. The new version of DYRESM-WQ is used to simulate chlorophyll N concentrations in Prospect Reservoir, New South Wales. These results are compared to concentrations predicted using the original algorithm. The comparison shows that the new algorithm for phytoplankton production is relatively insensitive to time step, which decreases the difficulty of calibrating the model for chlorophyll a.     

分支分类问题的遗传算法

分支分类问题可归结为聚类问题.通常的分支分类方法一般只能保证得到局部最优解.本文首先给出一种聚类方法,即同步插入法,然后将之转化为离散空间上的优化问题,并应用遗传算法以期得到全局最优解.实验结果表明该方法是正确和可行的.     

An adaptive single-step FDR procedure with applications to DNA microarray analysis

The use of multiple hypothesis testing procedures has been receiving a lot of attention recently by statisticians in DNA microarray analysis. The traditional FWER controlling procedures are not very useful in this situation since the experiments are exploratory by nature and researchers are more interested in controlling the rate of false positives rather than controlling the probability of making a single erroneous decision. This has led to increased use of FDR (False Discovery Rate) controlling procedures. Genovese and Wasserman proposed a single-step FDR procedure that is an asymptotic approximation to the original Benjamini and Hochberg stepwise procedure. In this paper, we modify the Genovese-Wasserman procedure to force the FDR control closer to the level alpha in the independence setting. Assuming that the data comes from a mixture of two normals, we also propose to make this procedure adaptive by first estimating the parameters using the EM algorithm and then using these estimated parameters into the above modification of the Genovese-Wasserman procedure. We compare this procedure with the original Benjamini-Hochberg and the SAM thresholding procedures. The FDR control and other properties of this adaptive procedure are verified numerically.     

A matching algorithm for catalytic residue site selection in computational enzyme design

A loop closure-based sequential algorithm, PRODA_MATCH, was developed to match catalytic residues onto a scaffold for enzyme design in silico. The computational complexity of this algorithm is polynomial with respect to the number of active sites, the number of catalytic residues, and the maximal iteration number of cyclic coordinate descent steps. This matching algorithm is independent of a rotamer library that enables the catalytic residue to take any required conformation during the reaction coordinate. The catalytic geometric parameters defined between functional groups of transition state (TS) and the catalytic residues are continuously optimized to identify the accurate position of the TS. Pseudo-spheres are introduced for surrounding residues, which make the algorithm take binding into account as early as during the matching process. Recapitulation of native catalytic residue sites was used as a benchmark to evaluate the novel algorithm. The calculation results for the test set show that the native catalytic residue sites were successfully identified and ranked within the top 10 designs for 7 of the 10 chemical reactions. This indicates that the matching algorithm has the potential to be used for designing industrial enzymes for desired reactions.     

Prediction of atomic structure from sequence for double helical DNA oligomers

DNA can adopt different conformations depending on the base sequence, solvent, electrolyte composition and concentration, pH, temperature, and interaction with proteins. Here we present a model for calculating the three-dimensional atomic structure of double-stranded DNA oligomers. A theoretical energy function is used for calculating the interactions within the base steps and an empirical backbone function is used to restrict the conformational space accessible to the bases and to account for the conformational coupling of neighboring steps in a sequence. Conformational searching on large structures or a large number of structures is possible, because each base step can be described by just two primary degrees of freedom (slide and shift). A genetic algorithm is used to search for low-energy structures in slide-shift space, and this allows very rapid optimization of DNA oligomers. The other base step parameters have been previously optimized for all possible slide-shift sequence combinations, and a heuristic algorithm is used to add the atomic details of the backbone conformation in the final step of the calculation. The structures obtained by this method are very similar to the corresponding X-ray crystal structures observed experimentally. The average RMSD is 2.24 Angstroms for a set of 20 oligomer structures. For 15 of these sequences, the X-ray crystal structure is the global energy minimum. The other 5 are bistable sequences that have B-form global energy minima but crystallize as A-DNA.     

Searching Optimal Designs in the Presence of Serially Correlated Errors

Based on simple updating formulaé, a computer algorithm for searching optimal designs when the errors are believed to be serially correlated in the one-dimensional situation is described. The performance of the designs found by the algorithm is compared with the optimal designs available in the literature.     

Randomized probe selection algorithm for microarray design

DNA microarray technology, originally developed to measure the level of gene expression, has become one of the most widely used tools in genomic study. The crux of microarray design lies in how to select a unique probe that distinguishes a given genomic sequence from other sequences. Due to its significance, probe selection attracts a lot of attention. Various probe selection algorithms have been developed in recent years. Good probe selection algorithms should produce a small number of candidate probes. Efficiency is also crucial because the data involved are usually huge. Most existing algorithms are usually not sufficiently selective and quite a large number of probes are returned. We propose a new direction to tackle the problem and give an efficient algorithm based on randomization to select a small set of probes and demonstrate that such a small set of probes is sufficient to distinguish each sequence from all the other sequences. Based on the algorithm, we have developed probe selection software RandPS, which runs efficiently in practice. The software is available on our website (http://www.csc.liv.ac.uk/ approximately cindy/RandPS/RandPS.htm). We test our algorithm via experiments on different genomes (Escherichia coli, Saccharamyces cerevisiae, etc.) and our algorithm is able to output unique probes for most of the genes efficiently. The other genes can be identified by a combination of at most two probes.     

Adaptive data collection for intraindividual studies affected by adherence

Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection that considers both the individual variability of the self-reported variable and of the adherence. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon signed rank test shows that the algorithm tends to have both a higher F₁ score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially in studies where data collection is impacted by adherence.     

Inverse folding with RNA-As-Graphs produces a large pool of candidate sequences with target topologies

We present an RNA-As-Graphs (RAG) based inverse folding algorithm, RAG-IF, to design novel RNA sequences that fold onto target tree graph topologies. The algorithm can be used to enhance our recently reported computational design pipeline (Jain et al., NAR 2018). The RAG approach represents RNA secondary structures as tree and dual graphs, where RNA loops and helices are coarse-grained as vertices and edges, opening the usage of graph theory methods to study, predict, and design RNA structures. Our recently developed computational pipeline for design utilizes graph partitioning (RAG-3D) and atomic fragment assembly (F-RAG) to design sequences to fold onto RNA-like tree graph topologies; the atomic fragments are taken from existing RNA structures that correspond to tree subgraphs. Because F-RAG may not produce the target folds for all designs, automated mutations by RAG-IF algorithm enhance the candidate pool markedly. The crucial residues for mutation are identified by differences between the predicted and the target topology. A genetic algorithm then mutates the selected residues, and the successful sequences are optimized to retain only the minimal or essential mutations. Here we evaluate RAG-IF for 6 RNA-like topologies and generate a large pool of successful candidate sequences with a variety of minimal mutations. We find that RAG-IF adds robustness and efficiency to our RNA design pipeline, making inverse folding motivated by graph topology rather than secondary structure more productive.     

Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.     

Coping with complexity: machine learning optimization of cell-free protein synthesis

Biological systems contain complex metabolic pathways with many nonlinearities and synergies that make them difficult to predict from first principles. Protein synthesis is a canonical example of such a pathway. Here we show how cell-free protein synthesis may be improved through a series of iterated high-throughput experiments guided by a machine-learning algorithm implementing a form of evolutionary design of experiments (Evo-DoE). The algorithm predicts fruitful experiments from statistical models of the previous experimental results, combined with stochastic exploration of the experimental space. The desired experimental response, or evolutionary fitness, was defined as the yield of the target product, and new experimental conditions were discovered to have ～ 350% greater yield than the standard. An analysis of the best experimental conditions discovered indicates that there are two distinct classes of kinetics, thus showing how our evolutionary design of experiments is capable of significant innovation, as well as gradual improvement.     

SARS病毒再测序DNA微阵列的探针设计

探针设计是SARS病毒再测序DNA微阵列制作的关键步骤,为了保证探针的杂交条件尽可能一致,采用了作者提出的两种等长变覆盖的探针设计方法,即基于Tm距离的算法和遗传算法。针对SAILS病毒基因组中的两段特异序列设计了一组探针,并与等长移位法和变长变覆盖法的设计结果进行了比较。等长变覆盖法得到的探针集在探针长度一致的情况下,探针的Tm值有较小的标准差和变化范围。结果表明,等长变覆盖法得到的探针具有更好的杂交条件一致性。     

Searching for Optimal Block Designs when Block Effects are Random

An algorithm that searches for optimal block designs when block effects are random is described. Some situations in which such an algorithm will prove useful are considered. An illustrative example and some discussion are given.     

一种基于关键路径法的DNA计算用寡核苷酸序列设计算法

在原有的生物大分子序列比对算法的基础上,结合图论中的关健路径法,提出了一种新的计算两寡核苷酸序列间最大配对程度的算法。采用此算法结合生成并测试的方法,能够寻找给定长度的一组适用于DNA计算的寡核苷酸序列。同时采用DNA芯片杂交方法验证了用该算法设计的一组序列的杂交特异性。     

Lambda-repressed mutants of bacteriophage T5. II. Physiological characterization.

Physiological properties of bacteriophage T5 gene A1 mutants, whose growth is inhibited in λ lysogens, and designated T5 lr, have been studied. In the presence of λ gene rex, which is responsible for lr growth inhibition, gene A1 product is synthesized and functional. However, several physiological defects were observed: phage DNA synthesis is inhibited; late phage-induced proteins are synthesized in markedly decreased amounts after a delay of about 15 minutes; phage DNA transfer into the host goes beyond the first-step transfer fragment but, in most bacteria, is interrupted after penetration of about 55% of the genome. Relationships between these different defects are discussed.     

A Bayesian model with application for adaptive platform trials having temporal changes

Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed. However, temporal effects can be a serious obstacle for conducting clinical trials with complex designs, including the adaptive platform trials that are gaining popularity in recent medical product development. In this paper, we introduce a Bayesian robust prior for mitigating temporal effects based on a hidden Markov model, and propose a particle filtering algorithm for computation. We conduct simulation studies to evaluate the performance of the proposed method and provide illustration examples based on trials of Ebola virus disease therapeutics and hemostat in vascular surgery.     

构建微生物分子分类系统进化树的快速运算法与数据结构

本文介绍了构建系统进化树的NJ方法（NeighborJoiningMethod）所涉及的算法与数据结构。文中给出了基于数据复用性的算法改进,获得了快速算法──FNJ算法,从而将算法的时间复杂度由（N⁵）降低为（N³）;并给出了自动绘制进化分枝图的算法。     

Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm,GEM-Path

The production of 75% of the current drug molecules and 35% of all chemicals could be achieved through bioprocessing (Arundel and Sawaya, 2009). To accelerate the transition from a petroleum-based chemical industry to a sustainable bio-based industry, systems metabolic engineering has emerged to computationally design metabolic pathways for chemical production. Although algorithms able to provide specific metabolic interventions and heterologous production pathways are available, a systematic analysis for all possible production routes to commodity chemicals in Escherichia coli is lacking. Furthermore, a pathway prediction algorithm that combines direct integration of genome-scale models at each step of the search to reduce the search space does not exist. Previous work (Feist et al., 2010) performed a model-driven evaluation of the growth-coupled production potential for E. coli to produce multiple native compounds from different feedstocks. In this study, we extended this analysis for non-native compounds by using an integrated approach through heterologous pathway integration and growth-coupled metabolite production design. In addition to integration with genome-scale model integration, the GEM-Path algorithm developed in this work also contains a novel approach to address reaction promiscuity. In total, 245 unique synthetic pathways for 20 large volume compounds were predicted. Host metabolism with these synthetic pathways was then analyzed for feasible growth-coupled production and designs could be identified for 1271 of the 6615 conditions evaluated. This study characterizes the potential for E. coli to produce commodity chemicals, and outlines a generic strain design workflow to design production strains.