Riccardo Dondi

The Haplotyping problem: an overview of computational models and solutions

The investigation of genetic differences among humans has given evidence that mutations in DNA sequences are responsible for some genetic diseases. The most common mutation is the one that involves only a single nucleotide of the DNA sequence, which is called a single nucleotide polymorphism (SNP). As a consequence, computing a complete map of all SNPs occurring in the human populations is one of the primary goals of recent studies in human genomics. The construction of such a map requires to determine the DNA sequences that from all chromosomes. In diploid organisms like humans, each chromosome consists of two sequences calledhaplotypes. Distinguishing the information contained in both haplotypes when analyzing chromosome sequences poses several new computational issues which collectively form a new emerging topic of Computational Biology known asHaplotyping.This paper is a comprehensive study of some new combinatorial approaches proposed in this research area and it mainly focuses on the formulations and algorithmic solutions of some basic biological problems. Three statistical approaches are briefly discussed at the end of the paper.

Reconciling a gene tree to a species tree under the duplication cost model

The general problem of reconciling the information from evolutionary trees representing the relationships between distinct gene families is of great importance in bioinformatics and has been popularized among the computer science researchers by Ma et al. [From gene trees to species trees, SIAM J. Comput. 30(3) (2000) 729-752] where the authors pose the intriguing question if a certain definition of minimum tree that reconciles a gene tree and a species tree is correct. We answer affirmatively to this question; moreover, we show an efficient algorithm for computing such minimum-leaf reconciliation trees and prove the uniqueness of such trees. We then tackle some different versions of the biological problem by showing that the exemplar problem, arising from the exemplar analysis of multigene genomes, is NP-hard even when the number of copies of a given label is at most two. Finally, we introduce two novel formulations for the problem of recombining evolutionary trees, extending the gene duplication problem studied in [Ma et al., From gene trees to species trees, SIAM J. Comput. 30(3) (2000) 729-752; M. Fellows et al., On the multiple gene duplication problem, in: Proc. Ninth Internat. Symp. on Algorithms and Computation (ISAAC98), 1998; R. Page, Maps between trees and cladistic analysis of historical associations among genes, Systematic Biology 43 (1994) 58-77; R.M. Page, J. Cotton, Vertebrate phylogenomics: reconciled trees and gene duplications, in: Proc. Pacific Symp. on Biocomputing 2002 (PSB2002), 2002, pp. 536-547; R. Guigo et al., Reconstruction of ancient molecular phylogeny, Mol. Phy. and Evol. 6(2) (1996) 189-213], and we give an exact algorithm (via dynamic programming) for one of these formulations.

A novel method for signal transduction network inference from indirect experimental evidence.

In this paper, we introduce a new method of combined synthesis and inference of biological signal transduction networks. A main idea of our method lies in representing observed causal relationships as network paths and using techniques from combinatorial optimization to find the sparsest graph consistent with all experimental observations. Our contributions are twofold: (a) We formalize our approach, study its computational complexity and prove new results for exact and approximate solutions of the computationally hard transitive reduction substep of the approach (Sections 2 and 5). (b) We validate the biological usability of our approach by successfully applying it to a previously published signal transduction network by Li et al. (2006) and show that our algorithm for the transitive reduction substep performs well on graphs with a structure similar to those observed in transcriptional regulatory and signal transduction networks.

Parameterized complexity of k-anonymity: hardness and tractability

The problem of publishing personal data without giving up privacy is becoming increasingly important. A precise formalization that has been recently proposed is the k-anonymity, where the rows of a table are partitioned into clusters of sizes at least k and all rows in a cluster become the same tuple after the suppression of some entries. The natural optimization problem, where the goal is to minimize the number of suppressed entries, is hard even when the stored values are over a binary alphabet or the table consists of a bounded number of columns. In this paper we study how the complexity of the problem is influenced by different parameters. First we show that the problem is W[1]-hard when parameterized by the value of the solution (and k). Then we exhibit a fixed-parameter algorithm when the problem is parameterized by the number of columns and the number of different values in any column. Finally, we prove that k-anonymity is still APX-hard even when restricting to instances with 3 columns and k=3.

Exemplar Longest Common Subsequence

In this paper, we investigate the computational and approximation complexity of the Exemplar Longest Common Subsequence (ELCS) of a set of sequences (ELCS problem), a generalization of the Longest Common Subsequence problem, where the input sequences are over the union of two disjoint sets of symbols, a set of mandatory symbols and a set of optional symbols. We show that different versions of the problem are APX-hard even for instances with two sequences. Moreover, we show that the related problem of determining the existence of a feasible solution of the ELCS of two sequences is NP-hard. On the positive side, we first present an efficient algorithm for the ELCS problem over instances of two sequences where each mandatory symbol can appear in total at most three times in the sequences. Furthermore, we present two fixed-parameter algorithms for the ELCS problem over instances of two sequences where the parameter is the number of mandatory symbols.

A novel method for signal transduction network inference from indirect experimental evidence

In this paper, we introduce a new method of combined synthesis and inference of biological signal transduction networks. A main idea of our method lies in representing observed causal relationships as network paths and using techniques from combinatorial optimization to find the sparsest graph consistent with all experimental observations. Our contributions are twofold: (a) We formalize our approach, study its computational complexity and prove new results for exact and approximate solutions of the computationally hard transitive reduction substep of the approach (Sections 2 and 5). (b) We validate the biological usability of our approach by successfully applying it to a previously published signal transduction network by Li et al. (2006) and show that our algorithm for the transitive reduction substep performs well on graphs with a structure similar to those observed in transcriptional regulatory and signal transduction networks.

Finding approximate and constrained motifs in graphs

One of the most relevant topics in the analysis of biological networks is the identification of functional motifs inside a network. A recent approach introduced in literature, called Graph Motif, represents the network as a vertex-colored graph, and the motif M as a multiset of colors. An occurrence of a motif M in a vertex-colored graph G is a connected induced subgraph of G whose vertex set is colored exactly as M. In this paper we investigate three different variants of the Graph Motif problem. The first two variants, Minimum Adding Motif (Min-Add Graph Motif) and Minimum Substitution Motif (Min-Sub Graph Motif), deal with approximate occurrences of a motif in the graph, while the third variant, Constrained Graph Motif (CGM), constrains the motif to contain a given set of vertices. We investigate the computational and parameterized complexity of the three problems. We show that Min-Add Graph Motifand Min-Sub Graph Motifare both NP-hard, even when M is a set, and the graph is a tree with maximum degree 4 in which each color appears at most twice. Then, we show that Min-Sub Graph Motifis fixed-parameter tractable when parameterized by the size of M. Finally, we consider the parameterized complexity of the CGMproblem; we give a fixed-parameter algorithm for graphs of bounded treewidth, and show that the problem is W[2]-hard when parameterized by |M|, even if the input graph has diameter 2.

On the Approximation of Correlation Clustering and Consensus Clustering

The Correlation Clustering problem has been introduced recently [N. Bansal, A. Blum, S. Chawla, Correlation Clustering, in: Proc. 43rd Symp. Foundations of Computer Science, FOCS, 2002, pp. 238-247] as a model for clustering data when a binary relationship between data points is known. More precisely, for each pair of points we have two scores measuring the similarity and dissimilarity respectively, of the two points, and we would like to compute an optimal partition where the value of a partition is obtained by summing up the similarity scores of pairs involving points from the same cluster and the dissimilarity scores of pairs involving points from different clusters. A closely related problem is Consensus Clustering, where we are given a set of partitions and we would like to obtain a partition that best summarizes the input partitions. The latter problem is a restricted case of Correlation Clustering. In this paper we prove that Minimum Consensus Clustering is APX-hard even for three input partitions, answering an open question in the literature, while Maximum Consensus Clustering admits a PTAS. We exhibit a combinatorial and practical 45-approximation algorithm based on a greedy technique for Maximum Consensus Clustering on three partitions. Moreover, we prove that a PTAS exists for Maximum Correlation Clustering when the maximum ratio between two scores is at most a constant.

Reconciling gene trees to a species tree

In this paper we deal with the general problem of recombining the information from evolutionary trees representing the relationships between distinct gene families. First we solve a problem from [8] regarding the construction of a minimum reconciled tree by giving an efficient algorithm. Then we show that the exemplar problem, arising from the exemplar analysis of multigene genomes [2], is NP-hard even when the number of copies of a given label is at most two. Finally we introduce two novel formulations for the problem of recombining evolutionary trees, extending the notion of the gene duplication problem studied in [8,11,9,10,6], and we give an exact algorithm (via dynamic programming) for one of the formulations given.

Weak pattern matching in colored graphs: Minimizing the number of connected components

In the context of metabolic network analysis, Lacroix et al.11 introduced the problem of finding occurrences of motifs in vertex-colored graphs, where a motif is a multiset of colors and an occurrence of a motif is a subset of connected vertices which are colored by all colors of the motif. We consider in this paper the above-mentioned problem in one of its natural optimization forms, referred hereafter as the Min-CC problem: Find an occurrence of a motif in a vertex-colored graph, called the target graph, that induces a minimum number of connected components. Our results can be summarized as follows. We prove the Min-CC problem to be APX–hard even in the extremal case where the motif is a set and the target graph is a path. We complement this result by giving a polynomial-time algorithm in case the motif is built upon a fixed number of colors and the target graph is a path. Also, extending recent research8 , we prove the Min- CC problem to be fixed-parameter tractable when parameterized by the size of the motif, and we give a faster algorithm in case the target graph is a tree. Furthermore, we prove the Min-CC problem for trees not to be approximable within ratio c log n for some constant c > 0, where n is the order of the target graph, and to be W[2]–hard when parameterized by the number of connected components in the occurrence of the motif. Finally, we give an exact efficient exponential-time algorithm for the Min-CC problem in case the target graph is a tree.