Computational Experiments on Algorithms for Haplotype Inference Problems by Pure Parsimony

Mutation in DNA is the principal cause for differences among human beings, and Single Nucleotide Polymorphisms (SNPs) are the most common mutations. Hence, a fundamental task is to complete a map of haplotypes (which identify SNPs) in the human population. Associated with this effort, a key computational problem is the inference of haplotype data from genotype data, since in practice genotype data rather than haplotype data is usually obtained. Different haplotype inference approaches have been proposed, including the utilization of statistical methods and the utilization of the pure parsimony criterion. The problem of haplotype inference by pure parsimony (HIPP) is interesting not only because of its application to haplotype inference, but also because it is a challenging NP-hard problem, being APX-hard. Recent work has shown that a SAT-based approach is the most efficient approach for the problem of haplotype inference by pure parsimony (HIPP), being several orders of magnitude faster than existing integer linear programming and branch and bound solutions. This paper provides a detailed description of SHIPs, a SAT-based approach for the HIPP problem, and presents comprehensive experimental results comparing SHIPs with all other exact approaches for the HIPP problem. These results confirm that SHIPs is currently the most effective approach for the HIPP problem.

Mathematical properties and bounds on haplotyping populations by pure parsimony

A fast haplotype inference method for large population genotype data

Haplotype inference from genotype data is a key computational problem in bioinformatics, since retrieving directly haplotype information from DNA samples is not feasible using existing technology. One of the methods for solving this problem uses the pure parsimony criterion, an approach known as Haplotype Inference by Pure Parsimony (HIPP). Initial work in this area was based on a number of different Integer Linear Programming (ILP) models and branch and bound algorithms. Recent work has shown that the utilization of a Boolean Satisfiability (SAT) formulation and state of the art SAT solvers represents the most efficient approach for solving the HIPP problem. Motivated by the promising results obtained using SAT techniques, this paper investigates the utilization of modern Pseudo-Boolean Optimization (PBO) algorithms for solving the HIPP problem. The paper starts by applying PBO to existing ILP models. The results are promising, and motivate the development of a new PBO model (RPoly) for the HIPP problem, which has a compact representation and eliminates key symmetries. Experimental results indicate that RPoly outperforms the SAT-based approach on most problem instances, being, in general, significantly more efficient.

Effective methods for solving the Bi-criteria p-Center and p-Dispersion problem

It may be very difficult to achieve the optimal shift schedule in call centers which have highly uncertain and peaked demand during short time periods. Overlapping shift systems are usually designed for such cases. This paper studies shift scheduling and rostering problems for inbound call centers where overlapping shift systems are used. An integer programming model that determines which shifts to be opened and how many operators to be assigned to these shifts is proposed for the shift scheduling problem. For the rostering problem both integer programming and constraint programming models are developed to determine assignments of operators to all shifts, weekly days-off, and meal and relief break times of the operators. The proposed models are tested on real data supplied by an outsource call center and optimal results are found in an acceptable computation time. An improvement of 15% in the objective function compared to the current situation is observed with the proposed model for the shift scheduling problem. The computational performances of the proposed integer and constraint programming models for the rostering problem are compared using real data observed at a call center and simulated test instances. In addition, benchmark instances are used to compare our Constraint Programming (CP) approach with the existing models. The results of the comprehensive computational study indicate that the constraint programming model runs more efficiently than the integer programming model for the rostering problem. The originality of this research can be attributed to two contributions: (a) a model for shift scheduling problem and two models for rostering problem are presented in detail and compared using real data and (b) the rostering problem is considered as a task-resource allocation and considerably shorter computation times are obtained by modeling this new problem via CP.

The haplotype inference problem (HIP) asks to find a set of haplotypes which resolve a given set of genotypes. This problem is of enormous importance in many practical fields, such as the investigation of diseases, or other types of genetic mutations. In order to find the haplotypes that are as close as possible to the real set of haplotypes that comprise the genotypes, two models have been suggested which by now have become widely accepted: The perfect phylogeny model and the pure parsimony model. All known algorithms up till now for the above problem may find haplotypes that are not necessarily plausible, i.e. very rare haplotypes or haplotypes that were never observed in the population. In order to overcome this disadvantage we study in this paper, for the first time, a new constrained version of HIP under the above mentioned models. In this new version, a pool of plausible haplotypes $\widetilde{H}$ is given together with the set of genotypes G , and the goal is to find a subset $H \subseteq \widetilde{H}$ that resolves G . For the constrained perfect phylogeny haplotyping (CPPH) problem we provide initial insights and polynomial-time algorithms for some restricted cases that help understanding the complexity of that problem. We also prove that the constrained parsimony haplotyping (CPH) problem is fixed parameter tractable by providing a parameterized algorithm that applies an interesting dynamic programming technique for solving the problem.

ABSTRACTThe solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALBHWP, tasks differ in terms of the qualification requirements of workers, and the qualification levels of workers are hierarchical. In the hierarchical workforce structure, a lower qualified worker can be replaced by higher qualified ones with higher costs, while the vice versa is not applicable. This problem has only been studied for straight assembly lines so far. In this paper, we introduce the ALBHWP for U-shaped assembly lines. We developed integer and constraint programming models for solving the ALBHWP and compared their effectiveness using an extensive set of benchmark instances. We solved the ALBHWP for straight and U-shaped assembly lines comparatively. Constraint programming models have been statistically proven to provide better quality solutions faster than integer programming models. Besides, the CP model outperforms the only available metaheuristic in the literature for the S-ALBHWP in almost all problem sizes. Another observation is that a U-shaped line design is more cost-effective than a straight line design, but solving the ALBHWP for U-shaped lines is more difficult regarding computational complexity.

The haplotype inference (HI) problem is the problem of inferring 2n haplotype pairs from n observed genotype vectors. This is a key problem that arises in studying genetic variation in populations, for example in the ongoing HapMap project [5]. In order to have a hope of finding the haplotypes that actually generated the observed genotypes, we must use some (implicit or explicit) genetic model of the evolution of the underlying haplotypes. The Perfect Phylogeny Haplotyping (PPH) model was introduced in 2002 [9] to reflect the “neutral coalescent” or “perfect phylogeny” model of haplotype evolution. The PPH problem (which can be solved in polynomial time) is to determine whether there is an HI solution where the inferred haplotypes can be derived on a perfect phylogeny (tree). Since the introduction of the PPH model, several extensions and modifications of the PPH model have been examined. The most important modification, to model biological reality better, is to allow a limited number of biological events that violate the perfect phylogeny model. This was accomplished implicitly in [7,12] with the inclusion of several heuristics into an algorithm for the PPH problem [8]. Those heuristics are invoked when the genotype data cannot be explained with haplotypes that fit the perfect phylogeny model. In this paper, we address the issue explicitly, by allowing one recombination or homoplasy event in the model of haplotype evolution. We formalize the problems and provide a polynomial time solution for one problem, using an additional, empirically-supported assumption. We present a related framework for the second problem which gives a practical algorithm. We believe the second problem can be solved in polynomial time.

Haplotype inference by pure parsimony (HIPP) is a well-known paradigm for haplotype inference. In order to assess the biological significance of this paradigm, we generalize the problem of HIPP to the problem of finding all optimal solutions, which we call complete HIPP. We study intrinsic haplotype features, such as backbone haplotypes and fat genotypes as well as equal columns and decomposability. We explicitly exploit these features in three computational approaches which are based on integer linear programming, depth-first branch-and-bound, and a hybrid algorithm that draws on the diverse strengths of the first two approaches. Our experimental analysis shows that our optimized algorithms are significantly superior to the baseline algorithms, often with orders of magnitude faster running time. Finally, our experiments provide some useful insights to the intrinsic features of this interesting problem.KeywordsProblem InstanceInteger Linear ProgramUnique HaplotypeBaseline AlgorithmInternational HapMap ProjectThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems

The pose of the moving platform in parallel robots is possible thanks to the strong coupling, but it consequently is very difficult to obtain its forward displacement. Different methods establishing forward displacement can obtain different numbers of variables and different solving speeds with nonlinear equations. The nonlinear equations with nine variables for forward displacement in the general 6-6 type parallel mechanism were created using the rotation transformation matrix R, translation vector P and the constraint conditions of the rod length. Given the problems of there being only one solution and sometimes no convergence when solving nonlinear equations with the Newton method and the quasi-Newton method, the Euler equation for free rotation in a rigid body was applied to a chaotic system by using chaos anti-control and chaotic sequences were produced. Combining the characteristics of the chaotic sequence with the mathematical programming method, a new mathematical programming method was put forward, which was based on chaos anti-control with the aim of solving all real solutions of nonlinear equations for forward displacement in the general 6-6 type parallel mechanism. The numerical example shows that the new method has some positive characteristics such as that it runs in the initial value range, it has fast convergence, it can find all the possible real solutions that be found out and it proves the correctness and validity of this method when compared with other methods.

In the field of the theory of calculation of building structures, there is a constant refinement of the actual operation of these structures, i.e. design schemes are created that most accurately correspond to actual operating conditions. Creating optimal structures is a very urgent task facing designers. Therefore, it is quite natural to try to solve this problem using mathematical programming methods, which involve: selecting dependent and independent variables, constructing mathematical models and establishing criteria for the effectiveness of the selected model. In this case, the model should be a function that fairly accurately describes the research being carried out using mathematical apparatus (various types of functions, equations, systems of equations and inequalities, etc.). In mathematical programming, any set of independent (controlled) variables is called a solution. Optimal solutions are those that, for one reason or another, are preferable to others. The preference (effectiveness) of the study is quantified by the numerical value of the objective function. “Solution Search” is an add-in for Microsoft Excel that is used to solve optimization problems. Simply put, with the Solver add-in, you can determine the maximum or minimum value of one cell by changing other cells. Most often, this add-in is used to find optimal solutions to problems economically. There are not enough results of using this approach for calculating building structures in the public domain. Therefore, it is quite logical to try to use this add-on in problems of optimization of building structures. In this work, an attempt was made to use mathematical programming methods and this add-on to optimize the geometric dimensions of the structure, when the numerical value of the bending moment in a specific section was chosen as an optimization criterion.

The nonsmooth equations model for seepage problems is proposed based on the basic principles of the seepage dynamic system and the finite element discrete method. The mathematical programming method is therefore applied. The free surface of seepage is plotted through interpolation with pressure intensity on the nodes. The numerical results show the new method is simple and rapid in convergence rate.

This study concerns with the problem of inferring haplotypes from a genotype data for each member in a pedigree using a minimum-recombinant criterion, which is useful for reducing the cost of typical laboratory techniques. Most available methods for finding the exact solutions of this problem showed the feasibility for only short haplotypes. Based on the recent method that uses a dynamic programming algorithm, we propose some improvements to make it feasible with long haplotypes and able to work with the data containing some missing alleles. Our improvements also allow the occurrence of a few Mendelian inconsistent alleles which can possibly appear in the data. The experimental results show that the computing time of our method outperforms the original method for the case of inferring long haplotypes (more than 20 loci) in a moderate-size pedigree (15-25 members)