Antonio Mucherino's Research

Keywords: bioinformatics, data mining, distance geometry, optimization, meta-heuristics, parallel computing

Distance Geometry. The Distance Geometry Problem (DGP) asks whether a simple weighted undirected graph G=(V,E,d) can be embedded in a K-dimensional Euclidean space so that the distance between every pair of embedded vertices u and v belonging to V corresponds to the weight d(u,v) assigned to the edge (u,v), when available. The distance information can be given either through an exact value, or by a real-valued interval. The DGP is NP-hard. One of the most interesting applications of the DGP is in biology. Experiments of Nuclear Magnetic Resonance (NMR) are able to find estimates of some distances between atom pairs of molecular conformations, and the problem of finding these conformations by using this distance information is, in fact, a DGP. Over the last years, in collaboration with Leo Liberti, Carlile Lavor and Nelson Maculan and other people, we have been working on the discretization of the DGP.
The reader interested in this topic can make reference to my cv online, as well as to my list of publications, where links to all my papers are available. In 2013, we co-edited a book devoted to the DGP and its applications. A software tool called MD-jeep, which is distributed under the GNU General Public License (v.2), is under development. It implements a branch-and-prune framework for discretizable distance geometry and its sources can be downloaded from this website.

Data Mining. Data mining is the nontrivial extraction of previously unknown, potentially useful and reliable patterns from a given set of data. Data mining techniques can be mainly divided into two categories: classification techniques and clustering techniques. Classification techniques can be used when there is some previous knowledge about the data, whereas clustering techniques try to discover inherent patterns hidden in the data without using any additional information. Classification techniques include the k-Nearest Neighbor (kNN), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). The most used clustering technique is k-means, that is very simple to apply, and which has many variants. Most data mining techniques bring to the formulation of global optimization problems.
I'm co-author, with Panos Pardalos and Petraq Papajorgji, of the textbook Data Mining in Agriculture. The book is devoted to data mining techniques applied to the agricultural field. Both classification and clustering techniques are presented in the book, and many examples of applications in agriculture and related fields are discussed. The implementations in Matlab of simple algorithms help the reader in understanding the topics and allow to perform simple experiments.
Among the data mining techniques, one of the most recent is a supervised technique for biclustering. Given a set of data, biclustering aims at finding simultaneous partitions in biclusters of its samples and of the features which are used for describing the samples. Consistent biclusterings can be exploited for performing supervised classifications, as well as for solving feature selection problems. The problem of identifying a consistent biclustering can be formulated as a 0-1 linear fractional optimization problem. I'm working on a bilevel reformulation of this optimization problem, and on a heuristic which is based on this bilevel reformulation. I recently proposed an extension of this technique to sets of data containing negative entries. An interesting application is to wine fermentations, where the aim is to identify problematic fermentations at the early stages of the process.

Proteins described by a mathematician. Proteins are heteropolymers. They are formed by smaller molecules called amino acids, that are bonded together to form a sort of "chain". Living organisms contain plenty of proteins, which perform several functions of vital importance. Each amino acid along the protein chain contains a Carbon atom called Cα, which is chemically bonded to three different aggregates of atoms. Two of these aggregates are common to each amino acid: the amino group (NH2) and the carboxyl group (COOH). Only one aggregate of atoms makes each amino acid different from one another: the so-called side chain R. Only 20 amino acids are involved into the protein synthesis. These molecules are under genetic control: the amino acid sequence of a protein is contained into the corresponding gene of the DNA molecule. The three-dimensional conformation of a protein molecule is of great interest, because it defines its dynamics in the cell, and therefore its function.
Different properties of the side chains allow for classifying the amino acids on the basis of their features. A very important feature of the amino acids is the polarity of their side chains R. A side chain is polar if it has a global or local electric charge. There is an attraction between positive and negative charged molecules and there is repulsion between molecules with the same charge. Since the protein solvent, i.e. water, is polar and globally uncharged, there is attraction between the water molecules and the polar R groups. It follows that the amino acids having a polar side chain are hydrophilic, because they tend to interact with the solvent. Other amino acids are buried inside the protein because of the hydrophilic amino acid behavior. Therefore, they are called hydrophobic. This feature of the amino acids plays a fundamental role in the stabilization of protein molecules. The attractive and repulsive van der Waals forces are important as well. They can be modeled, for instance, by the Lennard Jones potential (see below). Click here to have more information on the amino acids and on their properties.
Each amino acid of the protein chain is bonded to the previous and the successive amino acid through a peptide bond. More precisely, the carboxyl group of the ith amino acid is bonded to the amino group of the (i+1)th amino acid, i.e. a covalent bond keeps bonded the C atom of the first group and the N atom of the second one. The peptide bond is strong enough to force every atom between two consecutive Cα atoms on the same plane. Therefore, the movement possibilities of the amino acids on the chain are constrained.
More information about proteins and the computational methods for finding their stable conformations can be found on this web page on the Arnold Neumaier web site. Experimentally determined protein conformations can be downloaded from the Protein Data Bank (PDB), that currently contains about 105000 entries.

Why are we so interested in proteins? C. M. Dobson, A. Sali and M. Karplus wrote: "An understand of folding is important for the analysis of many events involved in cellular regulation, the design of proteins with novel functions, the utilization of sequence information from the various genome projects, and the development of novel therapeutic strategies for treating or preventing debilitating human diseases that are associated with the failure of proteins to fold correctly".

Analyzing and simulating the conformation of protein molecules. At the beginning of my career, I performed many analyses on protein conformations, where particular attention was given to their geometric features. I also worked on a model for protein simulations which is mainly based on geometric properties of protein conformations. Differently from other models for protein prediction, the considered model does not rely on chemical and physical forces involved in the folding process, but it rather attempts the simulation of protein conformations by exploiting their geometric properties. Computational experiments proved that the geometric properties of protein conformations play an important role in the folding process. Indeed, among the conformations that were simulated, there are some which are "very similar" to the ones protein molecules can actually have in nature.

Exploring the optimal conformations of clusters of molecules. Finding the ground state conformations of clusters of molecules is one of the most difficult global optimization problems. Such clusters are regulated by potential energy functions, such as the Lennard Jones and the Morse potential energy. The Lennard Jones potential is very simple but sufficiently accurate for noble gas, and it also provides approximations for structures of nickel and gold. The Morse potential has a parameter which determines the width of the potential well; Morse clusters show a wide variety of structural behaviors in function of this parameter. Both potential functions have several local minima in which methods for optimization may get stuck at. For instance, a Lennard Jones cluster formed by 13 atoms has about 1000 local minima, and the number of local minima increases exponentially with the size of the cluster. The Lennard Jones and Morse optimal clusters usually have particular geometric motifs, which can be exploited during the optimization process. The Cambridge Cluster Database contains experimentally obtained clusters. Some of them may just be local minima: the database is open to newly generated clusters having a lower energy function value.

Climbing trees like a monkey. A monkey can survive in a forest of trees because it is able to focus its food researches where it already found good food to eat. During my period of work in the United States, Onur Seref and I proposed a novel method for global optimization which is inspired by the behavior of a monkey. We named this method Monkey Search. The trees the monkey explores contain solutions to the optimization problem to be solved, and branches of the tree represent perturbations able to transform one solution into another. At each step, the monkey chooses the branches to climb by applying a random mechanism, which is based on the objective function values of the new corresponding solutions. The monkey prefers better solutions, but it is not forced to choose them.
Rather than a completely new meta-heuristic approach for global optimization, the Monkey Search can be considered as a general framework (describing the monkey behavior) where other ideas and strategies for global optimization are also employed. Perturbations on candidate solutions can be taken from other methods for optimization (such as Genetic Algorithms, Harmony Search, Ant Colony Optimization, etc) and others can be inspired by the optimization problem itself. This leads to a wide range of variants for this meta-heuristic search. We implemented the Monkey Search in C programming language for solving particular classes of optimization problems, such as the problem of finding Lennard Jones and Morse clusters, as well as for solving the distance geometry problem (see above). Alla Kammerdiner implemented the Monkey Search for solving the Multidimensional Assignment Problem (MAP).

Parallel Computing. There are several applications that require high computing resources. Parallel computing allows to exploit the CPU power of many processors simultaneously for solving difficult problems. There exist parallel computers with different architectures, and the most used parallel computers are nowadays the MIMD machines with distributed memory. We are developing parallel versions of the Branch & Prune algorithm, which we employ for solving the distance geometry problem (see above). The experimental testing of the parallel algorithm has been performed on the French nation-wide grid infrastructure Grid5000.

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