SearcharxivSearch

arXiv subjects

Marc Hellmuth

Publications and source records attributed to Marc Hellmuth.

At least 19 recordsLinked to original sources

The $\ominus$-metric to compare phylogenetic networks

We introduce two novel distances for comparing rooted phylogenetic networks based on the $\ominus$-operator, which removes a vertex while preserving the ancestor relations among the remaining vertices. The distance $d_{\ominus}$ measures the minimum number of such removals needed to obtain isomorphic networks, whereas $d_{\ominus}^-$ ignores shortcut arcs and therefore compares the induced ancestry structures. We show that $d_{\ominus}$ is a metric up to leaf-fixing isomorphism and that $d_{\ominus}^-$ is a metric up to shortcut-free isomorphism. Moreover, both distances extend the Robinson--Foulds distance on phylogenetic trees and are bounded below by the hardwired cluster distances. For several broad network classes, including tree-child, normal, level-$1$, and regular networks, $d_{\ominus}^-$ can be computed in polynomial time. In contrast, computing $d_{\ominus}$ is NP-hard, W[2]-hard when parameterized by the distance value, and admits no polynomial-time constant-factor approximation unless $\mathrm{P}=\mathrm{NP}$. Although computing $d_{\ominus}^-$ is NP-hard in general, for distinct-cluster networks it reduces to \textsc{Vertex Cover}, yielding a fixed-parameter algorithm and a polynomial-time $2$-approximation.

cs.DM

Novel Triple-Based Problems for the Construction of Phylogenetic Networks via Least Common Ancestors

Evolutionary histories are often represented by rooted phylogenetic networks, whose leaves correspond to extant taxa and whose internal vertices represent ancestral lineages. Since such histories must usually be inferred from incomplete data, in particular from genomic sequences of present-day taxa, one often obtains only local information about relative evolutionary proximity. For instance, sequence data may suggest that two taxa $x$ and $y$ are more closely related to each other than either is to a third taxon $z$. This information is classically encoded by a rooted triple $xy|z$. In this paper, we study rooted triples in phylogenetic networks under an ancestor-based interpretation: $xy|z$ is displayed if the unique least common ancestor (LCA) of $x$ and $y$ lies strictly below the unique LCA of $x$ and $z$, respectively of $y$ and $z$, and the latter two LCAs coincide. We also introduce anchored triples $\underline{x}y|z$, which retain only the asymmetric comparison that the LCA of $x$ and $y$ lies below the LCA of $x$ and $z$. This relaxation is natural in networks, where different pairwise ancestral relationships need not behave as they do in trees. We consider several variants of consistency problems for ordinary and anchored triples, both with and without forbidden triples. Somewhat surprisingly, these ancestor-based consistency questions for triples in phylogenetic networks do not appear to have been addressed before despite their direct biological interpretation and the fact that such constraints can be inferred naturally from genomic sequence data. By translating these questions into realization problems for required and forbidden LCA-constraints, we show that all resulting problems can be solved in polynomial time. Moreover, whenever a solution exists, a suitable realizing DAG and phylogenetic network can be constructed within the same time bound.

cs.DM

Encoding Phylogenetic Networks with Least Common Ancestor Constraints

Encoding phylogenetic networks by suitable substructures is a central problem in phylogenetic combinatorics. We study encodings based on least common ancestor (LCA) constraints. For a directed acyclic graph (DAG) $G$ with leaf set $X$, we consider the relation on pairs of leaves in which $(ab,xy)$ records that the LCAs of $a,b$ and $x,y$ are well-defined and that the former is a descendant of the latter. We first identify precisely which part of $G$ is determined by this relation. To this end, we compare the canonical DAG constructed from the LCA relation with the 2-regularization of $G$, obtained by removing all vertices that are not LCAs of one or two leaves and then deleting shortcut edges. We prove that these two DAGs are isomorphic. Hence the obstruction to encoding a graph by its LCA relation is exactly the information lost under 2-regularization. This yields a general reconstruction principle, which we apply to several natural classes of phylogenetic networks. In particular, we show that shortcut-free 2-LCA-relevant DAGs, phylogenetic trees, regular level-1 networks, regular networks with binary clustering systems, regular networks whose clustering systems are closed weak hierarchies, strong-phylogenetic normal networks, separated phylogenetic normal networks, and binary normal networks are encoded by their LCA relations. We also introduce a sparse triple-like restriction consisting only of comparisons of the form $(ab,ac)$, where $a,b,c\in X$ are pairwise distinct. For graphs with the 2-LCA property, we show that this sparse relation, together with the leaf set, determines the full LCA relation after a natural closure operation. Consequently, several of the above classes can be reconstructed, up to isomorphism, from the sparse relation in polynomial time.

cs.DM

Regularizing and Normalizing DAGs and Phylogenetic Networks

Phylogenetic networks and, more generally, directed acyclic graphs (DAGs) represent hierarchical structure beyond trees, for instance in the presence of reticulate evolutionary events such as hybridization or horizontal gene transfer. A central question is which parts of such graphs are essential with respect to leaf-observable information, and which parts can be removed without changing this information. Resolving this question can lead to principled simplification methods for phylogenetic networks, such as the recent normalization approach of Francis et al. In this paper, we study this question from three related perspectives: clusters displayed by a DAG $G$, least common ancestors (LCAs) of subsets of its leaf set, and visibility, a path-based property of vertices. We first introduce an LCA-based simplification procedure called $i$-regularization. For a DAG $G$ and $i\geq 1$, the DAG $\reg_i(G)$ retains precisely those vertices that occur as unique LCAs of leaf subsets of size at most $i$, removes the remaining non-leaf vertices by a graph-editing operation $\ominus$, and then deletes shortcuts. We show that $\reg_i(G)$ admits a Hasse-diagram characterization in terms of the corresponding lca-clusters. We then compare LCA-based regularization with normalization. Using the same $\ominus$-operator, we describe the cover construction underlying normalization, identify visible vertices that are nevertheless removed, and characterize when regularization and normalization coincide. Together, these results provide a unified framework for cluster-based, LCA-based, and visibility-based simplifications of DAGs and phylogenetic networks.

q-bio.PE

Inferring Phylogenetic Networks from Required and Forbidden LCA-Constraints

Least common ancestor (LCA) constraints encode relative-order information in directed acyclic graphs (DAGs) and give rise to a natural constraint-realization problem. Phylogenetic networks provide an important class of DAGs in which such constraints are used to represent local information about evolutionary histories. In this paper, we study the inference of DAGs and phylogenetic networks from LCA-constraints, which specify relative positions of the LCAs associated with pairs of leaves. While previous work has characterized when a set of required LCA-constraints can be realized by a DAG or phylogenetic network, it is natural to consider additional constraints that must be explicitly avoided. We therefore consider the realization problem for pairs $(R,F)$, where $R$ is a set of required LCA-constraints and $F$ is a set of forbidden ones. Since there are several natural ways to formalize what it means for a DAG to avoid a forbidden LCA-constraint, we study three such variants. For each of them, we characterize exactly when there exists a DAG or a phylogenetic network that realizes all constraints in $R$ while avoiding all constraints in $F$ in the respective sense. Our main characterization is based on a closure operator obtained from four elementary inference rules. Based on these characterizations, we derive polynomial-time algorithms that decide the existence of such realizations and construct one whenever it exists. All algorithms developed in this paper are implemented in the freely available Python package RealLCA.

cs.DM

Inferring DAGs and Phylogenetic Networks from Least Common Ancestors

A least common ancestor (LCA) of two leaves in a directed acyclic graph (DAG) is a vertex that is an ancestor of both leaves and has no proper descendant that is also their common ancestor. LCAs capture hierarchical relationships in rooted trees and, more generally, in DAGs. In 1981, Aho et al. introduced the problem of determining whether a set of pairwise LCA constraints on a set $X$, of the form $(i,j)<(k,l)$ with $i,j,k,l\in X$, can be realized by a rooted tree whose leaf set is $X$, such that whenever $(i,j)<(k,l)$, the LCA of $i,j$ is a descendant of that of $k,l$. They also presented a polynomial-time algorithm, BUILD, to solve this problem. However, many such constraint systems cannot be realized by any tree, prompting the question of whether they can be realized by a more general DAG. We extend Aho et al.'s framework from trees to DAGs, providing both theoretical and algorithmic foundations for reasoning about LCA constraints in this broader setting. Given a collection $R$ of LCA constraints, we define its $+$-closure $R^+$, capturing additional LCA relations implied by $R$. Using $R^+$, we construct a canonical DAG $G_R$ and prove that $R$ is DAG-realizable if and only if it is realized by $G_R$. We further adapt this construction to phylogenetic networks, defining a canonical network $N_R$ and prove that it is regular, i.e., it coincides with the Hasse diagram of its underlying set system. Finally, we show that for any DAG-realizable $R$, its classical closure - comprising all LCA constraints that hold in every DAG realizing $R$ - coincides with its $+$-closure. All constructions are computable in polynomial time, and we provide explicit algorithms for each. All algorithms developed in this paper are implemented in the freely available Python package RealLCA.

math.CO

On Finding All Connected Maximum-Sized Common Subgraphs in Multiple Labeled Graphs

We present an exact algorithm for computing all common subgraphs with the maximum number of vertices across multiple graphs. Our approach is further extended to handle the connected Maximum Common Subgraph (MCS), identifying the largest common subgraph in terms of either vertices or edges across multiple graphs, where edges or vertices may additionally be labeled to account for possible atom types or bond types, a classical labeling used in molecular graphs. Our approach leverages modular product graphs and a modified Bron-Kerbosch algorithm to enumerate maximal cliques, ensuring all intermediate solutions are retained. A pruning heuristic efficiently reduces the modular product size, improving computational feasibility. Additionally, we introduce a graph ordering strategy based on graph-kernel similarity measures to optimize the search process. Our method is particularly relevant for bioinformatics and cheminformatics, where identifying conserved structural motifs in molecular graphs is crucial. Empirical results on molecular datasets demonstrate that our approach is scalable and fast.

cs.DS

Global Least Common Ancestor (LCA) Networks

Directed acyclic graphs (DAGs) are fundamental structures used across many scientific fields. A key concept in DAGs is the least common ancestor (LCA), which plays a crucial role in understanding hierarchical relationships. Surprisingly little attention has been given to DAGs that admit a unique LCA for every subset of their vertices. Here, we characterize such global lca-DAGs and provide multiple structural and combinatorial characterizations. We show that global lca-DAGs have a close connection to join semi-lattices and establish a connection to forbidden topological minors. In addition, we introduce a constructive approach to generating global lca-DAGs and demonstrate that they can be recognized in polynomial time. We investigate their relationship to clustering systems and other set systems derived from the underlying DAGs.

math.CO

Orthology and Near-Cographs in the Context of Phylogenetic Networks

Orthologous genes, which arise through speciation, play a key role in comparative genomics and functional inference. In particular, graph-based methods allow for the inference of orthology estimates without prior knowledge of the underlying gene or species trees. This results in orthology graphs, where each vertex represents a gene, and an edge exists between two vertices if the corresponding genes are estimated to be orthologs. Orthology graphs inferred under a tree-like evolutionary model must be cographs. However, real-world data often deviate from this property, either due to noise in the data, errors in inference methods or, simply, because evolution follows a network-like rather than a tree-like process. The latter, in particular, raises the question of whether and how orthology graphs can be derived from or, equivalently, are explained by phylogenetic networks. Here, we study the constraints imposed on orthology graphs when the underlying evolutionary history follows a phylogenetic network instead of a tree. We show that any orthology graph can be represented by a sufficiently complex level-k network. However, such networks lack biologically meaningful constraints. In contrast, level-1 networks provide a simpler explanation, and we establish characterizations for level-1 explainable orthology graphs, i.e., those derived from level-1 evolutionary histories. To this end, we employ modular decomposition, a classical technique for studying graph structures. Specifically, an arbitrary graph is level-1 explainable if and only if each primitive subgraph is a near-cograph (a graph in which the removal of a single vertex results in a cograph). Additionally, we present a linear-time algorithm to recognize level-1 explainable orthology graphs and to construct a level-1 network that explains them, if such a network exists.

q-bio.PE

Characterizing and Transforming DAGs within the I-LCA Framework

We explore the connections between clusters and least common ancestors (LCAs) in directed acyclic graphs (DAGs), focusing on the interplay between so-called $I$-lca-relevant DAGs and DAGs with the $I$-lca-property. Here, $I$ denotes a set of integers. In $I$-lca-relevant DAGs, each vertex is the unique LCA for some subset $A$ of leaves of size $|A|\in I$, whereas in a DAG with the $I$-lca-property there exists a unique LCA for every subset $A$ of leaves satisfying $|A|\in I$. We elaborate on the difference between these two properties and establish their close relationship to pre-$I$-ary and $I$-ary set systems. This, in turn, generalizes results established for (pre-)binary and $k$-ary set systems. Moreover, we build upon recently established results that use a simple operator $\ominus$, enabling the transformation of arbitrary DAGs into $I$-lca-relevant DAGs. This process reduces unnecessary complexity while preserving key structural properties of the original DAG. The set $C_G$ consists of all clusters in a DAG $G$, where clusters correspond to the descendant leaves of vertices. While in some cases $C_H = C_G$ when transforming $G$ into an $I$-lca-relevant DAG $H$, it often happens that certain clusters in $C_G$ do not appear as clusters in $H$. To understand this phenomenon in detail, we characterize the subset of clusters in $C_G$ that remain in $H$ for DAGs $G$ with the $I$-lca-property. Furthermore, we show that the set $W$ of vertices required to transform $G$ into $H = G \ominus W$ is uniquely determined for such DAGs. This, in turn, allows us to show that the ``shortcut-free'' version of the transformed DAG $H$ is always a tree or a galled-tree whenever $C_G$ represents the clustering system of a tree or galled-tree and $G$ has the $I$-lca-property. In the latter case $C_H = C_G$ always holds.

math.CO

Simplifying and Characterizing DAGs and Phylogenetic Networks via Least Common Ancestor Constraints

Rooted phylogenetic networks, or more generally, directed acyclic graphs (DAGs), are widely used to model species or gene relationships that traditional rooted trees cannot fully capture, especially in the presence of reticulate processes or horizontal gene transfers. Such networks or DAGs are typically inferred from observable data (e.g. genomic sequences of extant species), providing only an estimate of the true evolutionary history. However, these inferred DAGs are often complex and difficult to interpret. In particular, many contain vertices that do not serve as least common ancestors (LCAs) for any subset of the underlying genes or species, thus may lack direct support from the observable data. In contrast, LCA vertices are witnessed by historical traces justifying their existence and thus represent ancestral states substantiated by the data. To reduce unnecessary complexity and eliminate unsupported vertices, we aim to simplify a DAG to retain only LCA vertices while preserving essential evolutionary information. In this paper, we characterize $\mathrm{LCA}$-relevant and $\mathrm{lca}$-relevant DAGs, defined as those in which every vertex serves as an LCA (or unique LCA) for some subset of taxa. We introduce methods to identify LCAs in DAGs and efficiently transform any DAG into an $\mathrm{LCA}$-relevant or $\mathrm{lca}$-relevant one while preserving key structural properties of the original DAG or network. This transformation is achieved using a simple operator ``$\ominus$'' that mimics vertex suppression.

q-bio.PE

Nesting of Touching Polygons

Polygons are cycles embedded into the plane; their vertices are associated with $x$- and $y$-coordinates and the edges are straight lines. Here, we consider a set of polygons with pairwise non-overlapping interior that may touch along their boundaries. Ideas of the sweep line algorithm by Bajaj and Dey for non-touching polygons are adapted to accommodate polygons that share boundary points. The algorithms established here achieves a running time of $\mathcal{O}(n+N\log N)$, where $n$ is the total number of vertices and $N<n$ is the total number of "maximal outstretched segments" of all polygons. It is asymptotically optimal if the number of maximal outstretched segments per polygon is bounded. In particular, this is the case for convex polygons.

cs.CG

Network Representation and Modular Decomposition of Combinatorial Structures: A Galled-Tree Perspective

In phylogenetics, reconstructing rooted trees from distances between taxa is a common task. B\"ocker and Dress generalized this concept by introducing symbolic dated maps $\delta:X \times X \to \Upsilon$, where distances are replaced by symbols, and showed that there is a one-to-one correspondence between symbolic ultrametrics and labeled rooted phylogenetic trees. Many combinatorial structures fall under the umbrella of symbolic dated maps, such as 2-dissimilarities, symmetric labeled 2-structures, or edge-colored complete graphs, and are here referred to as strudigrams. Strudigrams have a unique decomposition into non-overlapping modules, which can be represented by a modular decomposition tree (MDT). In the absence of prime modules, strudigrams are equivalent to symbolic ultrametrics, and the MDT fully captures the relationships $\delta(x,y)$ between pairs of vertices $x,y$ in $X$ through the label of their least common ancestor in the MDT. However, in the presence of prime vertices, this information is generally hidden. To provide this missing structural information, we aim to locally replace the prime vertices in the MDT to obtain networks that capture full information about the strudigrams. While starting with the general framework of prime-vertex replacement networks, we then focus on a specific type of such networks obtained by replacing prime vertices with so-called galls, resulting in labeled galled-trees. We introduce the concept of galled-tree explainable (GATEX) strudigrams, provide their characterization, and demonstrate that recognizing these structures and reconstructing the labeled networks that explain them can be achieved in polynomial time.

math.CO

The Complement of the Djokovic-Winkler Relation

The Djokovi\'{c}-Winkler relation $\Theta$ is a binary relation defined on the edge set of a given graph that is based on the distances of certain vertices and which plays a prominent role in graph theory. In this paper, we explore the relatively uncharted ``reflexive complement'' $\overline\Theta$ of $\Theta$, where $(e,f)\in \overline\Theta$ if and only if $e=f$ or $(e,f)\notin \Theta$ for edges $e$ and $f$. We establish the relationship between $\overline\Theta$ and the set $\Delta_{ef}$, comprising the distances between the vertices of $e$ and $f$ and shed some light on the intricacies of its transitive closure $\overline\Theta^*$. Notably, we demonstrate that $\overline\Theta^*$ exhibits multiple equivalence classes only within a restricted subclass of complete multipartite graphs. In addition, we characterize non-trivial relations $R$ that coincide with $\overline\Theta$ as those where the graph representation is disconnected, with each connected component being the (join of) Cartesian product of complete graphs. The latter results imply, somewhat surprisingly, that knowledge about the distances between vertices is not required to determine $\overline\Theta^*$. Moreover, $\overline\Theta^*$ has either exactly one or three equivalence classes.

math.CO

Unique Least Common Ancestors and Clusters in Directed Acyclic Graphs

We investigate the connections between clusters and least common ancestors (LCAs) in directed acyclic graphs (DAGs). We focus on the class of DAGs having unique least common ancestors for certain subsets of their minimal elements since these are of interest, particularly as models of phylogenetic networks. Here, we use the close connection between the canonical k-ary transit function and the closure function on a set system to show that pre-k-ary clustering systems are exactly those that derive from a class of DAGs with unique LCAs. Moreover, we show that k-ary T-systems and k-weak hierarchies are associated with DAGs that satisfy stronger conditions on the existence of unique LCAs for sets of size at most k.

cs.DM

The weighted total cophenetic index: A novel balance index for phylogenetic networks

Phylogenetic networks play an important role in evolutionary biology as, other than phylogenetic trees, they can be used to accommodate reticulate evolutionary events such as horizontal gene transfer and hybridization. Recent research has provided a lot of progress concerning the reconstruction of such networks from data as well as insight into their graph theoretical properties. However, methods and tools to quantify structural properties of networks or differences between them are still very limited. For example, for phylogenetic trees, it is common to use balance indices to draw conclusions concerning the underlying evolutionary model, and more than twenty such indices have been proposed and are used for different purposes. One of the most frequently used balance index for trees is the so-called total cophenetic index, which has several mathematically and biologically desirable properties. For networks, on the other hand, balance indices are to-date still scarce. In this contribution, we introduce the \textit{weighted} total cophenetic index as a generalization of the total cophenetic index for trees to make it applicable to general phylogenetic networks. As we shall see, this index can be determined efficiently and behaves in a mathematical sound way, i.e., it satisfies so-called locality and recursiveness conditions. In addition, we analyze its extremal properties and, in particular, we investigate its maxima and minima as well as the structure of networks that achieve these values within the space of so-called level-$1$ networks. We finally briefly compare this novel index to the two other network balance indices available so-far.

q-bio.PE

Fitch Graph Completion

Horizontal gene transfer is an important contributor to evolution. According to Walter M.\ Fitch, two genes are xenologs if they are separated by at least one HGT. More formally, the directed Fitch graph has a set of genes is its vertices, and directed edges $(x,y)$ for all pairs of genes $x$ and $y$ for which $y$ has been horizontally transferred at least once since it diverged from the last common ancestor of $x$ and $y$. Subgraphs of Fitch graphs can be inferred by comparative sequence analysis. In many cases, however, only partial knowledge about the ``full'' Fitch graph can be obtained. Here, we characterize Fitch-satisfiable graphs that can be extended to a biologically feasible ``full'' Fitch graph and derive a simple polynomial-time recognition algorithm. We then proceed to showing that finding the Fitch graphs with total maximum (confidence) edge-weights is an NP-hard problem.

cs.DS

Solving NP-hard Problems on \textsc{GaTEx} Graphs: Linear-Time Algorithms for Perfect Orderings, Cliques, Colorings, and Independent Sets

The class of $\mathsf{Ga}$lled-$\mathsf{T}$ree $\mathsf{Ex}$plainable ($\mathsf{GaTEx}$) graphs has recently been discovered as a natural generalization of cographs. Cographs are precisely those graphs that can be uniquely represented by a rooted tree where the leaves correspond to the vertices of the graph. As a generalization, $\mathsf{GaTEx}$ graphs are precisely those that can be uniquely represented by a particular rooted acyclic network, called a galled-tree. This paper explores the use of galled-trees to solve combinatorial problems on $\mathsf{GaTEx}$ graphs that are, in general, NP-hard. We demonstrate that finding a maximum clique, an optimal vertex coloring, a perfect order, as well as a maximum independent set in $\mathsf{GaTEx}$ graphs can be efficiently done in linear time. The key idea behind the linear-time algorithms is to utilize the galled-trees that explain the $\mathsf{GaTEx}$ graphs as a guide for computing the respective cliques, colorings, perfect orders, or independent sets.

cs.DM