Constrained De Novo peptide identification via multi-objective optimization

Malard, Joel M.; Heredia-Langner, Alejandro; Baxter, Doug; Jarman, Kristin H.; Cannon, W. Roger

doi:10.1109/ipdps.2004.1303208

Cited by 9 publications

(11 citation statements)

References 32 publications

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“…The work presented at HiCOMB'04 [7] showed how constrained peptide identification can be formulated as a multi-objective optimization and how structured population can significantly reduce the overheads of maintaining large populations, including Pareto ranking with its quadratic worstcase time complexity. This paper extends the above work by showing one way to effectively restore the selection pressure that is lost by a purely Pareto-rank based formulation.…”

Section: Peptide Identificationmentioning

confidence: 99%

Peptide identification via constrained multi‐objective optimization: Pareto‐based genetic algorithms

Malard

Heredia-Langner

Cannon

et al. 2005

Concurrency and Computation

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SUMMARYAutomatic peptide identification from collision-induced dissociation tandem mass spectrometry data using optimization techniques is made difficult by large plateaus in the fitness landscapes of scoring functions, by the fuzzy nature of constraints from noisy data and by the existence of diverse but equally justifiable probabilistic models of peak matching. Here, two different scoring functions are combined into a parallel multi-objective optimization framework. It is shown how multi-objective optimization can be used to empirically test for independence between distinct scoring functions. The loss of selection pressure during the evolution of a population of putative peptide sequences by a Pareto-driven genetic algorithm is addressed by alternating between two definitions of fitness according to a numerical threshold. Copyright c 2005 John Wiley & Sons, Ltd.KEY WORDS: data-intensive computation; genetic algorithms; multiobjective optimization; peptide identification; tandem mass spectrometry PEPTIDE IDENTIFICATIONHigh-throughput proteomic techniques seek to characterize the state of the proteome in a cell population. A typical procedure may involve extracting cellular proteins followed by tryptic digestion and then separating the peptides with liquid chromatography. The separated peptides are then identified by tandem mass spectrometry (MS/MS). Ideally, peptides will subsequently be quantified, posttranslational modifications will be determined and the information regarding the peptides will be * Correspondence to: J. M. Malard, Pacific

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Section: Peptide Identificationmentioning

confidence: 99%

Peptide identification via constrained multi‐objective optimization: Pareto‐based genetic algorithms

Malard

Heredia-Langner

Cannon

et al. 2005

Concurrency and Computation

Self Cite

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“…29,30 Another technique attacks the peptide identification problem by stochastic optimization using genetic algorithms to solve multiobjective models and can empirically test for independence between scoring functions. [31][32][33] The algorithm NovoHMM 34 uses a hidden Markov model to solve the peptide sequencing problem, where the observable random variables are the observed mass peaks and the hidden variables correspond to the unknown peptide sequence. Despite the vast potential of de novo methods, they are often computationally expensive and exhibit variable prediction accuracies.…”

Section: Introductionmentioning

confidence: 99%

A mixed‐integer optimization framework for de novo peptide identification

DiMaggio

Floudas

2006

AIChE Journal

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A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum.

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“…234,[236][237][238] Another class of methods which are stochastic and use genetic algorithms were recently proposed. 239,240 The methods in (b) employ a graph theoretical framework coupled with a penalty/reward function which is correlated by empirical observations and/or heuristic methods. 235,[241][242][243][244][245] An alternative technique to the graph-based approaches postulates hypothetical spectra and uses an empirical best-fit objective which tries to match the experimental spectra.…”

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confidence: 99%

Research challenges, opportunities and synergism in systems engineering and computational biology

Floudas

2005

AIChE Journal

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Introduction During the last three decades, the research area of systems engineering has emerged as a domain of fundamental importance and major impact within chemical engineering, as well as a cornerstone area in interdisciplinary research initiatives with computer science, operations research, applied mathematics, materials and life sciences. This is attributed to the unique characteristics of systems engineering which are the combination of analysis and synthesis for the design, optimization, and operation of processes and products. The product and process discovery research efforts are founded on fundamental advances in mathematical modeling, optimization theory and algorithms, and insights derived either from existing operations and/or from biology, chemistry, and physics. The fundamental advances are epitomized through new theoretical, algorithmic, and modeling frameworks for (a) mixed-integer linear and nonlinear optimization, (b) deterministic global optimization, and (c) dynamic simulation and optimization. The proposed modeling and optimization approaches have multiscale applications ranging from macroscopic to mesoscopic to microscopic systems, and which provide a natural and fundamental link between systems engineering, computational chemistry, computational biology and systems biology. Approaches based on mixed-integer linear and nonlinear optimization which were typically identified with process synthesis, scheduling and planning applications, have entered the domains of gene regulatory networks, metabolic networks, signal transduction networks, beta-sheet topology prediction in proteins, de novo peptide and protein design, DNA recombination, phase problem in X-ray crystallography, side chain optimization in protein prediction, peptide identification via tandem mass spectroscopy. Approaches based on deterministic global optimization associated with process design, synthesis, scheduling, and pooling/blending applications, are now in the main stream of product design, structure prediction in protein folding, dynamics of protein folding, NMR protein structure refinement, and de novo protein design. Approaches based on dynamic models and large scale optimization which were identified with process models and their applications, are suited for metabolic and signal transduction networks. As a result, a synergism between systems engineering, computational biology, and systems biology has evolved gradually, opened new research avenues and has reached the stage where fascinating research contributions address important questions in computational biology with methods and tools from systems engineering which combine mathematical rigor with key biological insights.This article provides a perspective on the challenges and opportunities that emerge from the fundamental developments in the research fields of systems engineering and computational biology. The advances in the areas of deterministic global optimization and process scheduling are introduced first, followed by their respective research opportunities. Th...

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Constrained De Novo peptide identification via multi-objective optimization

Abstract: Abstract

Cited by 9 publications

References 32 publications

Peptide identification via constrained multi‐objective optimization: Pareto‐based genetic algorithms

Peptide identification via constrained multi‐objective optimization: Pareto‐based genetic algorithms

A mixed‐integer optimization framework for de novo peptide identification

Research challenges, opportunities and synergism in systems engineering and computational biology

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