Novel evolutionary models and applications to sequence alignment problems

In this paper, we present a novel graph-theoretical approach for representing a wide variety of sequence analysis problems within a single model. The model allows incorporation of the operations “insertion”, “deletion”, and “substitution”, and various parameters such as relative distances and weights. Conceptually, we refer the problem as the minimum weight common mutated sequence (MWCMS) problem. The MWCMS model has many applications including multiple sequence alignment problem, the phylogenetic analysis, the DNA sequencing problem, and sequence comparison problem, which encompass a core set of very difficult problems in computational biology. Thus the model presented in this paper lays out a mathematical modeling framework that allows one to investigate theoretical and computational issues, and to forge new advances for these distinct, but related problems. Through the introduction of supernodes, and the multi-layer supergraph, we proved that MWCMS is -complete. Furthermore, it was shown that a conflict graph derived from the multi-layer supergraph has the property that a solution to the associated node-packing problem of the conflict graph corresponds to a solution of the MWCMS problem. In this case, we proved that when the number of input sequences is a constant, MWCMS is polynomial-time solvable. We also demonstrated that some well-known combinatorial problems can be viewed as special cases of the MWCMS problem. In particular, we presented theoretical results implied by the MWCMS theory for the minimum weight supersequence problem, the minimum weight superstring problem, and the longest common subsequence problem. Two integer programming formulations were presented and a simple yet elegant decomposition heuristic was introduced. The integer programming instances have proven to be computationally intensive. Consequently, research involving simultaneous column and row generation and parallel computing will be explored. The heuristic algorithm, introduced herein for multiple sequence alignment, overcomes the order-dependent drawbacks of many of the existing algorithms, and is capable of returning good sequence alignments within reasonable computational time. It is able to return the optimal alignment for multiple sequences of length less than 1500 base pairs within 30 minutes. Its algorithmic decomposition nature lends itself naturally for parallel distributed computing, and we continue to explore its flexibility and scalability in a massive parallel environment.     

Graph algorithms for DNA sequencing – origins,current models and the future

With the ubiquitous presence of next-generation sequencing in modern biological, genetic, pharmaceutical and medical research, not everyone pays attention to the underlying computational methods. Even fewer researchers know what were the origins of the current models for DNA assembly. We present original graph models used in DNA sequencing by hybridization, discuss their properties and connections between them. We also explain how these graph models evolved to adapt to the characteristics of next-generation sequencing. Moreover, we present a practical comparison of state-of-the-art DNA de novo assembly tools representing these transformed models, i.e. overlap and decomposition-based graphs. Even though the competition is tough, some assemblers perform better and certainly large differences may be observed in hardware resources utilization. Finally, we outline the most important trends in the sequencing field, and try to predict their impact on the computational models in the future.     

Convex games with an infinite number of players and sequencing situations

In this paper we study convex games with an infinite countable set of agents and provide characterizations of this class of games. To do so, and in order to overcome some shortcomings related to the difficulty of dealing with infinite orderings, we need to use a continuity property. Infinite sequencing situations where the number of jobs is infinite countable can be related to convex cooperative TU games. It is shown that some allocations turn out to be extreme points of the core of an infinite sequencing game.     

Using Unfold‐PCA for batch‐to‐batch start‐up process understanding and steady‐state identification in a sequencing batch reactor

In chemical and biochemical processes, steady‐state models are widely used for process assessment, control and optimisation. In these models, parameter adjustment requires data collected under nearly steady‐state conditions. Several approaches have been developed for steady‐state identification (SSID) in continuous processes, but no attempt has been made to adapt them to the singularities of batch processes. The main aim of this paper is to propose an automated method based on batch‐wise unfolding of the three‐way batch process data followed by a principal component analysis (Unfold‐PCA) in combination with the methodology of Brown and Rhinehart 2 for SSID. A second goal of this paper is to illustrate how by using Unfold‐PCA, process understanding can be gained from the batch‐to‐batch start‐ups and transitions data analysis. The potential of the proposed methodology is illustrated using historical data from a laboratory‐scale sequencing batch reactor (SBR) operated for enhanced biological phosphorus removal (EBPR). The results demonstrate that the proposed approach can be efficiently used to detect when the batches reach the steady‐state condition, to interpret the overall batch‐to‐batch process evolution and also to isolate the causes of changes between batches using contribution plots. Copyright © 2007 John Wiley & Sons, Ltd.     

生物电化学系统降解多环芳烃萘及微生物群落研究

采用单室空气阴极微生物燃料电池(MFC)反应器构型, 以不同浓度萘为基底物质, 考察MFC的产电性能、 萘降解率、 化学需氧量(COD)和总有机碳含量(TOC)降解率及MFC阴阳极微生物活性和多样性. 结果表明, 循环伏安曲线受不同浓度萘的影响变化较为明显, 随着萘浓度的增大, 最大功率密度呈下降趋势, 且萘对MFC的阴极电极电势影响较大; 当萘的浓度为15 mg/L时, MFC最大功率密度可达(645.841±28.08) mW/m ²; 对萘的降解率高达100%, 且MFC对COD和TOC的降解率随着萘浓度的提高而增大, 但是增大的速率逐渐减小. 对MFC阳极微生物膜进行高通量测序发现, Geobacter是优势菌属, 相对丰度达81%, 阴极主要以Aquamicrobium为主.     

Simulating the Monty Hall problem in a DNA sequencing machine

The Monty Hall problem is a decision problem with an answer that is surprisingly counter-intuitive yet provably correct. Here we simulate and prove this decision in a high-throughput DNA sequencing machine, using a simple encoding. All possible scenarios are represented by DNA oligonucleotides, and gameplay decisions are implemented by sequencing these oligonucleotides from specific positions, with a single run simulating more than 12,000,000 independent games. This work highlights high-throughput DNA sequencing as a new tool that could extend existing capabilities and enable new encoding schemes for problems in DNA computing.     

Laser‐Induced Fluorometry for Capillary Electrophoresis

Laser‐induced fluorometry (LIF) has achieved the detection of single molecules, which ranks it among the most sensitive of detection techniques, whereas capillary electrophoresis (CE) is known as a powerful separation method with resolution that is beyond the reach of many other types of chromatography. Therefore, a coupling of LIF with CE has established an unrivaled analytical technique in terms of sensitivity and resolution. CE‐LIF has demonstrated excellent performance in bioanalytical chemistry for the high‐resolution separation and highly sensitive detection of DNAs, proteins, and small bioactive molecules. This review describes the CE‐LIF methods developed by the author's group that include indirect and direct detection using diode lasers, post‐column derivatization, and Hadamard transformation, as well as applications to the binding assays of specific DNA immunoassays of proteins and to the determination of anticancer drugs.     

Venomics: unravelling the complexity of animal venoms with mass spectrometry

Animal venoms and toxins are now recognized as major sources of bioactive molecules that may be tomorrow's new drug leads. Their complexity and their potential as drug sources have been demonstrated by application of modern analytical technologies, which have revealed venoms to be vast peptide combinatorial libraries. Structural as well as pharmacological diversity is immense, and mass spectrometry is now one of the major investigative tools for the structural investigation of venom components. Recent advances in its use in the study of venom and toxins are reviewed. The application of mass spectrometry techniques to peptide toxin sequence determination by de novo sequencing is discussed in detail, in the light of the search for novel analgesic drugs. We also present the combined application of LC-MALDI separation with mass fingerprinting and ISD fragmentation for the determination of structural and pharmacological classes of peptides in complex spider venoms. This approach now serves as the basis for the full investigation of complex spider venom proteomes, in combination with cDNA analysis.     

High‐Throughput Mutational Analysis of a Twister Ribozyme

Recent discoveries of new classes of self‐cleaving ribozymes in diverse organisms have triggered renewed interest in the chemistry and biology of ribozymes. Functional analysis and engineering of ribozymes often involve performing biochemical assays on multiple ribozyme mutants. However, because each ribozyme mutant must be individually prepared and assayed, the number and variety of mutants that can be studied are severely limited. All of the single and double mutants of a twister ribozyme (a total of 10 296 mutants) were generated and assayed for their self‐cleaving activity by exploiting deep sequencing to count the numbers of cleaved and uncleaved sequences for every mutant. Interestingly, we found that the ribozyme is highly robust against mutations such that 71 % and 30 % of all single and double mutants, respectively, retain detectable activity under the assay conditions. It was also observed that the structural elements that comprise the ribozyme exhibit distinct sensitivity to mutations.