Molecular Mechanics Model Research Articles

Assessing Solid Catalysts with Ionic Liquid Layers (SCILL) from Molecular Dynamics Simulations: On the Role of Local Charge Polarization.

Recent developments in molecular mechanics modelling of metal catalyst surfaces with interfaces to complex ad-layers or bulk liquids enable the study of 10 nm scale systems by molecular dynamics simulations of up to microseconds. Therein, electronic polarization as otherwise benchmarked by quantum calculations is mimicked via atom-centered partial charges that are adjusted dynamically to account for changes in local environment. Apart from thermal fluctuations, this encompasses molecule association and dissociation processes as well as externally applied voltage. Here, we elaborate the concept of employing the charge equilibration method to the molecular dynamics simulation of solid catalysts, namely metal surfaces and substrate-supported metal nanoparticles. This showcases the association of reactants and their interplay with local charge polarization upon co-adsorption of ionic liquids or application of external voltage - thus paving the way to understanding complex interfaces in (electro-)catalysis from molecular dynamics simulation.

Minocycline reduces neurobehavioral deficits evoked by chronic unpredictable stress in adult zebrafish

Chronic stress-related brain disorders are widespread and debilitating, and often cause lasting neurobehavioral deficits. Minocycline, a common antibiotic and an established inhibitor of microglia, emerges as potential treatment of these disorders. The zebrafish (Danio rerio) is an important emerging model organism in translational neuroscience and stress research. Here, we evaluated the potential of minocycline to correct microglia-mediated behavioral, genomic and neuroimmune responses induced by chronic unpredictable stress (CUS) in adult zebrafish. We demonstrated that CUS evoked overt behavioral deficits in the novel tank, light–dark box and shoaling tests, paralleled by elevated stress hormones (CRH, ACTH and cortisol), and upregulated brain expression of the ‘neurotoxic M1′ microglia-specific biomarker gene (MHC-2) and pro-inflammatory cytokine genes (IL-1β, IL-6 and IFN-γ). CUS also elevated peripheral (whole-body) pro-inflammatory (IL-1β, IFN-γ) and lowered anti-inflammatory cytokines (IL-4 and IL-10), as well as reduced whole-brain serotonin, dopamine and norepinephrine levels, and increased brain dopamine and serotonin turnover. In contrast, minocycline attenuated most of these effects, also reducing CUS-elevated peripheral levels of IL-6 and IFN-γ. Collectively, this implicates microglia in zebrafish responses to chronic stress, and suggests glial pathways as potential evolutionarily conserved drug targets for treating stress-evoked neuropathogenesis. Our findings also support the growing translational value of zebrafish models for understanding complex molecular mechanisms of brain pathogenesis and its therapy.

Primary and Orthotopic Murine Models of Nasopharyngeal Carcinoma Reveal Molecular Mechanisms Underlying its Malignant Progression.

Nasopharyngeal carcinoma (NPC), a squamous cell carcinoma originating in the nasopharynx, is a leading malignancy in south China and other south and east Asia areas. It is frequently associated with Epstein-Barr virus (EBV) infection, while there are also some NPC patients without EBV infection. Here, it is shown that the EBV+ (EBV positive) and EBV- (EBV negative) NPCs contain both shared and distinct genetic abnormalities, among the latter are increased mutations in TP53. To investigate the functional roles of NPC-associated genetic alterations, primary, orthotopic, and genetically defined NPC models were developed in mice, a key tool missed in the field. These models, initiated with gene-edited organoids of normal nasopharyngeal epithelium, faithfully recapitulated the pathological features of human disease. With these models, it is found that Trp53 and Cdkn2a deficiency are crucial for NPC initiation and progression. And latent membrane protein1 (LMP1), an EBV-coding oncoprotein, significantly promoted the distal metastasis. Further, loss of TGFBR2, which is frequently disrupted both in EBV- and EBV+ NPCs, dramatically accelerated the progression and lung metastasis of NPC probably by altering tumor microenvironment. Taken together, this work establishes a platform to dissect the genetic mechanisms underlying NPC pathogenesis and might be of value for future translational studies.

The Molecular Basis of Differentiation Wave Activity in Embryogenesis

As development varies greatly across the tree of life, it may seem difficult to suggest a model that proposes a single mechanism for understanding collective cell behaviors and the coordination of tissue formation. Here we propose a mechanism called differentiation waves, which unify many disparate results involving developmental systems from across the tree of life. We demonstrate how a relatively simple model of differentiation proceeds not from function-related molecular mechanisms, but from so-called differentiation waves. A phenotypic model of differentiation waves is introduced, and its relation to molecular mechanisms is proposed. These waves contribute to a differentiation tree, which is an alternate way of viewing cell lineage and local action of the molecular factors. We construct a model of differentiation wave-related molecular mechanisms (genome, epigenome, and proteome) based on bioinformatic data from the nematode Caenorhabditis elegans. To validate this approach across different modes of development, we evaluate protein expression across different types of development by comparing Caenorhabditis elegans with several model organisms: fruit flies (Drosophila melanogaster), yeast (Saccharomyces cerevisiae), and mouse (Mus musculus). Inspired by gene regulatory networks, two Models of Interactive Contributions (fully-connected MICs and ordered MICs) are used to suggest potential genomic contributions to differentiation wave-related proteins. This, in turn, provides a framework for understanding differentiation and development.

Molecular Dynamics Simulation of Silicone Oil Polymerization from Combined QM/MM Modeling.

We outline a molecular simulation protocol for elucidating the formation of silicone oil from trimethlyl- and dimethlysilanediole precursor mixtures. While the fundamental condensation reactions are effectively described by quantum mechanical calculations, this is combined with molecular mechanics models in order to assess the extended relaxation processes. Within a small series of different precursor mixtures used as starting points, we demonstrate the evolution of the curing degree and heat formation in the course of polymer chain growth. Despite the increasing complexity of the amorphous agglomerate of polymer chains, our approach shows an appealing performance for tackling both elastic and viscous relaxation. Indeed, the finally obtained polymer systems feature 99% curing and thus offer realistic insights into the growth mechanisms of coexisting/competing polymer strands.

Analyze the temperature-dependent elastic properties of single-walled boron nitride nanotubes by a modified energy method

The elastic properties of boron nitride nanotubes (BNNTs) were investigated utilizing an enhanced energy method. By considering small deformations and applying the principle of minimum potential energy, the variations in atomic bonds and bond angles within the nanotube structure were determined. The modified model incorporated the contribution of inversion energy to the overall potential energy of the system, leading to the derivation of analytical expressions for the Young's modulus, shear modulus, and strain energy of both armchair and zigzag BNNTs under varying temperatures. The results indicate that compared to zigzag BNNTs, the impact of inversion energy on the elastic constants of armchair BNNTs is more significant, especially at small diameters (<1 nm). In thermal environment, this study demonstrates that the change in Young's modulus of BNNTs is lower than that of carbon nanotubes (CNTs), confirming the superior thermal stability of BNNTs over CNTs. Furthermore, molecular structure mechanics (MSM) and continuum mechanics models were employed to analyze the strain energy of BNNTs. The effects of different bonds, bond angles, and inversion angles on strain energy were analyzed in a thermal environment, revealing distinct differences between the two types of BNNTs. These findings provide more accurate theoretical guidance for thermal applications based on the stretching of BNNTs.

The analysis of extension evolution law and damage behavior characteristic for line microcracks on Si3N4 bearing balls

To predict the relation between extension evolution law and damage of line microcracks on silicon nitride bearing balls. Firstly, the 3D point cloud features of line microcracks are obtained based on multi-image 3D reconstruction. Next, the point cloud diagram data is optimized to fit the molecular dynamics geometric model of line microcracks extension process. Then, according to the Si3N4 molecular dynamics deep learning potential, the molecular dynamics mechanical model is established. Finally, Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is used to numerically solve the extension evolution of line microcracks. The experiment result shows that from the change analysis of single-layer surface mesh, the microcracks extension is concentrated in the central region of the indentation. The new microcracks spread along both sides of the original microcracks. After uniformly distributed load is applied to line microcracks, there is a phenomenon of concentrated swirling microcracks formation under the indentation region. By analyzing the atomic coordination number nephogram, the microcracks extension occurs when the proportion of amorphous atoms is up to 7 % during loading. And the plastic deformation occurs when the proportion of amorphous atoms is dropped to 6 % during unloading. In addition, in the process of indentation loading, plastic deformation and slip zone occur on Si3N4 bearing balls surface. However, the strain is accumulated in the middle region during unloading. The slip zone moves inward and becomes thin, and the elastic deformation partially is recovered.

Nonlocal-Strain-Gradient-Based Anisotropic Elastic Shell Model for Vibrational Analysis of Single-Walled Carbon Nanotubes

In this study, a new anisotropic elastic shell model with a nonlocal strain gradient is developed to investigate the vibrations of simply supported single-walled carbon nanotubes (SWCNTs). The Sanders–Koiter shell theory is used to obtain strain–displacement relationships. Eringen’s nonlocal elasticity and Mindlin’s strain gradient theories are adopted to derive the constitutive equations, where the anisotropic elasticity constants are expressed via Chang’s molecular mechanics model. An analytical method is used to solve the equations of motion and to obtain the natural frequencies of SWCNTs. First, the anisotropic elastic shell model without size effects is validated through comparison with the results of molecular dynamics simulations reported in the literature. Then, the effects of the nonlocal and material parameters on the natural frequencies of SWCNTs with different geometries and wavenumbers are analyzed. From the numerical simulations, it is confirmed that the natural frequencies decrease as the nonlocal parameter increases, while they increase as the material parameter increases. As new results, the reduction in natural frequencies with increasing SWCNT radius and the increase in natural frequencies with increasing wavenumber are both amplified as the material parameter increases, while they are both attenuated as the nonlocal parameter increases.

3D animation for integrative modeling of dynamic molecular mechanisms

3D animation for integrative modeling of dynamic molecular mechanisms

Simulating the Fluorescence of the Locally Excited State of DMABN in Solvents of Different Polarities.

4-(N,N-Dimethylamino)benzonitrile (DMABN) is a luminescent probe that can be used for tracking changes in the surrounding solvent due to the large change in polarity between its ground and excited states. An important characteristic of DMABN is that it exhibits dual fluorescence with two different emission energies that can be monitored, allowing for better characterization of the surrounding system. The first excited state is called the locally excited (LE) state and is characterized by the movement of charge over the conjugated ring structure. In nonpolar solvents and in the gas phase, the fluorescence of DMABN is entirely attributed to the transition from the near-planar LE state. In more polar environments, emission occurs from both the LE and a second excited state, corresponding to a twisted intramolecular charge-transfer (ICT) structure. For the sake of simplicity, this work considers transitions between only the ground and LE state. Molecular mechanical force field models of DMABN in its ground and LE states have been developed to investigate the sensitivity of the LE state to the polarity of the solvent. Both nonpolarizable and polarizable force fields were developed to simulate the molecule in a series of 10 different solvents of different polarities. The calculated Stokes shift of DMABN increases with increasing orientation polarizability of the surrounding solvent, which is the expected trend, as seen in experimental studies.

QMMM 2023: A program for combined quantum mechanical and molecular mechanical modeling and simulations

Combined quantum mechanical and molecular mechanical (QM/MM) methods play an important role in multiscale modeling and simulations. QMMM 2023 is a general-purpose program for single-point calculations, geometry optimizations, transition-state optimizations, and molecular dynamics (MD) at the QM/MM level. It calls a QM package and an MM package to perform the required single-level calculations and combines them into a QM/MM energy by a variety of schemes. QMMM 2023 supports GAMESS-US, Gaussian, and ORCA as QM packages and Tinker as the MM package. Four types of treatments are available for embedding the QM subsystem in the MM environment: mechanical embedding with gas-phase calculations of the QM region, electronic embedding that allows polarization of the QM region by the MM environment, polarizable embedding for mutual polarization of the QM and MM regions, and flexible embedding for both mutual polarization and partial charge transfer between the QM and MM regions. Boundaries between QM and MM regions that pass through covalent bonds can be treated by several methods, including the redistributed charge (RC) scheme, redistributed charge and dipole (RCD) scheme, balanced-RC scheme, balanced-RCD scheme, screened charge scheme that takes account of charge penetration effects, and smeared charge scheme that delocalizes the MM charges near the QM–MM boundary. Geometry optimization can be done using the optimizer implemented in QMMM 2023 or the Berny optimizer in Gaussian through external calls to Gaussian. Molecular dynamics simulations can be performed at the pure-MM level, pure-QM level, fixed-partitioning QM/MM level, and adaptive-partitioning QM/MM level. The adaptive-partitioning treatments permit on-the-fly relocation of the QM–MM boundary by dynamically reclassifying atoms or groups into the QM or MM subsystems. Program SummaryProgram Title: QMMM 2023.CPC Library link to program files:https://doi.org/10.17632/zmkc9db4zj.1.Licensing provisions: Apache-2.0 for the program and CC-BY-4.0 for the manual.Programming languages: Fortran90, Perl, and shell scripting.Nature of problem: Multiscale modeling and simulations by the combined quantum mechanical and molecular mechanical (QM/MM) method.Solution method: The program performs single-point calculations, geometry optimizations, transition state optimizations, and molecular dynamics (MD) by the QM/MM method by interfacing calls to binary executables in a QM package (GAMESS-US, Gaussian, or ORCA) and an MM package (TINKER) for the required calculations. QMMM 2023 supports various types of embedding schemes and multiple ways of treating a QM-MM boundary passing through a covalent bond.Additional comments including restrictions and unusual features: (1) QMMM 2023 supports polarizable-embedding and flexible-embedding schemes based on the electronegativity equalization principle and allowing charge transfer between the QM and MM regions. (2) For a QM-MM boundary passing through covalent bonds, all link-atom-based treatments preserve the charge of the MM region (and MM bond dipoles if desired) while effectively avoiding the over-polarization of the QM region. Further tuning is available to account for the charge penetration effects at the QM-MM boundary. (3) For dynamics simulations, Hamiltonian-based adaptive-partitioning algorithms allow on-the-fly reclassification of atoms or groups into the QM or MM regions without abrupt changes in the energy of the entire system or in the forces on all atoms.

The mechanism of UP1 binding and unfolding of human telomeric DNA G-quadruplex

The human telomere contains multiple copies of the DNA sequence d(TTAGGG) which can fold into higher order intramolecular G-quadruplexes and regulate the maintenance of telomere length and chromosomal integrity. The nucleic acid binding protein heteronuclear ribonucleoprotein A1 (hnRNP A1) and its N-terminus proteolytic product UP1 have been shown to efficiently bind and unfold telomeric DNA G-quadruplex. However, the understanding of the molecular mechanism of the UP1 binding and unfolding telomeric G-quadruplexes is still limited. Here, we performed biochemical and biophysical characterizations of UP1 binding and unfolding of human telomeric DNA G-quadruplex d[AGGG(TTAGGG)3], and in combination of systematic site-direct mutagenesis of two tandem RNA recognition motifs (RRMs) in UP1, revealed that RRM1 is responsible for initial binding and unfolding, whereas RRM2 assists RRM1 to complete the unfolding of G-quadruplex. Isothermal titration calorimetry (ITC) and circular dichroism (CD) studies of the interactions between UP1 and DNA G-quadruplex variants indicate that the “TAG” binding motif in Loop2 of telomeric G-quadruplex is critical for UP1 recognition and G-quadruplex unfolding initiation. Together we depict a model for molecular mechanism of hnRNP A1 (UP1) binding and unfolding of the human telomeric DNA G-quadruplex.

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery.

Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field.

Predictive Modeling of Molecular Mechanisms in Hydrogen Production and Storage Materials.

Hydrogen has been widely considered to hold promise for solving challenges associated with the increasing demand for green energy. While many chemical and biochemical processes produce molecular hydrogen as byproducts, electrochemical approaches using water electrolysis are considered to be a predominant method for clean and green hydrogen production. We discuss the current state-of-the-art in molecular hydrogen production and storage and, more significantly, the increasing role of computational modeling in predictively designing and deriving insights for enhancing hydrogen storage efficiency in current and future materials of interest. One of the key takeaways of this review lies in the continued development and implementation of large-scale atomistic simulations to enable the use of designer electrolyzer-electrocatalysts operating under targeted thermophysical conditions for increasing green hydrogen production and improving hydrogen storage in advanced materials, with limited tradeoffs for storage efficiency.

Nanostructure modeling for thermal properties of zigzag carbon nanotubes

In the nanotube design application, the case of dynamic damage is key problem attracted many engineering researchers. The thermal effect on the axial buckling of zigzag double walled carbon nanotubes (DWCNTs) embedded in an elastic medium is investigated in this article based on the nonlocal Timoshenko beam model. The effects of the van der Waals (vdW) forces between inner and outer tubes and the surrounding elastic medium are taken into account based on a Winkler model. Moreover, a modified molecular mechanics model is developed to study the effect of thermal environment on mechanical properties of zigzag double carbon nanotubes, in which the covalent bonds are traded as a truss. It is found that the axial buckling properties of zigzag DWCNTs increase with the increase of the temperature at room or low temperature, while the axial buckling properties of zigzag DWCNTs decrease with the increase of the temperature at high temperature. Moreover, the buckling strain considering the thermal effect is larger than the buckling strain ignoring the effect of temperature change, and increases with the increase of temperature change, which is contrary to the case of high temperature.

Holes interaction of a graphene membrane under pressure for water desalination

Single-layer graphene is the basic structural element of several carbon allotropes, including graphite, fullerens and carbon nanotubes. The mechanical behavior of graphene has been intensely studied in the last years and it has been shown that it is significantly affected by the presence of structural holes, e.g., atom vacancies in the graphene lattice, which can dramatically reduce the failure strength. The presence of holes in the graphene lattice is nearly inevitable, either because of the production process or because of the environmental and operating conditions of the devices. Moreover, in some engineering applications, such as the desalination of sea water through a graphene filter, atom vacancies could be deliberately introduced to achieve new functionalities. Hence, the interaction between multiple holes becomes extremely important for the overall mechanical properties in a single-layer graphene. Here, we study the mechanical behavior of perforated single-layer graphene membrane through a molecular mechanics model, which is solved numerically using the Riks arc length method. We show that the distance between multiple holes in tensile tests determines a very different failure strength of the single-layer graphene. Finally, the effect of holes is also investigated in the case of out of plane deformation. Such analysis lays the foundations for the development of more sophisticated nano-materials that can achieve non-trivial mechanical functionalities through the presence of holes.

An Electrostatically Embedded QM/MM Scheme for Electrified Interfaces.

Atomistic modeling of electrified interfaces remains a major issue for detailed insights in electrocatalysis, corrosion, electrodeposition, batteries, and related devices such as pseudocapacitors. In these domains, the use of grand-canonical density functional theory (GC-DFT) in combination with implicit solvation models has become popular. GC-DFT can be conveniently applied not only to metallic surfaces but also to semiconducting oxides and sulfides and is, furthermore, sufficiently robust to achieve a consistent description of reaction pathways. However, the accuracy of implicit solvation models for solvation effects at interfaces is in general unknown. One promising way to overcome the limitations of implicit solvents is going toward hybrid quantum mechanical (QM)/molecular mechanics (MM) models. For capturing the electrochemical potential dependence, the key quantity is the capacitance, i.e., the relation between the surface charge and the electrochemical potential. In order to retrieve the electrochemical potential from a QM/MM hybrid scheme, an electrostatic embedding is required. Furthermore, the charge of the surface and of the solvent regions has to be strictly opposite in order to consistently simulate charge-neutral unit cells in MM and in QM. To achieve such a QM/MM scheme, we present the implementation of electrostatic embedding in the VASP code. This scheme is broadly applicable to any neutral or charged solid/liquid interface. Here, we demonstrate its use in the context of GC-DFT for the hydrogen evolution reaction (HER) over a noble-metal-free electrocatalyst, MoS2. We investigate the effect of electrostatic embedding compared to the implicit solvent model for three contrasting active sites on MoS2: (i) the sulfur vacancy defect, which is rather apolar; (ii) a Mo antisite defect, where the active site is a surface bound highly polar OH group; and (iii) a reconstructed edge site, which is generally believed to be responsible for most of the catalytic activity. According to our results, the electrostatic embedding leads to almost indistinguishable results compared to the implicit solvent for the apolar system but has a significant effect on polar sites. This demonstrates the reliability of the hybrid QM/MM, electrostatically embedded solvation model for electrified interfaces.

Molecular Dynamics Study of Cured ED-20 Epoxy Resin for Predicting the Glass Transition Temperature and Relationship with Structure Features.

A structural model of ED-20 epoxy resin cured by triethylenetetramine was constructed using a molecular dynamics (MD) simulation method. In order to model the epoxy resin hardening, we modified a typical stepwise protocol introducing cross-links between the amino groups and epoxide carbon atoms that allowed us to reproduce experimentally observed glass transition temperature at the value of 360-364 K. The set of MD trajectories of the final molecular-mechanical model can be useful for analysis of alterations in structural and physical properties of the epoxy resin depending on scaleable parameters. The relationships among qualitative alterations in macromolecular structure, the glass transition temperature, and the number of imposed cross-links were elucidated. Analysis of intermolecular interactions between the largest macromolecules and other molecules of a system made it possible to observe the features of the transition from glassy to the viscoelastic state of the studied polymer during the temperature rise.

Epigenetic Alterations in Canine Malignant Lymphoma: Future and Clinical Outcomes.

Canine malignant lymphoma is a common neoplasia in dogs, and some studies have used dogs as a research model for molecular mechanisms of lymphomas in humans. In two species, chemotherapy is the treatment of choice, but the resistance to conventional anticancer drugs is frequent. The knowledge of molecular mechanisms of development and progression of neoplasia has expanded in recent years, and the underlying epigenetic mechanisms are increasingly well known. These studies open up new ways of discovering therapeutic biomarkers. Histone deacetylases and demethylase inhibitors could be a future treatment for canine lymphoma, and the use of microRNAs as diagnosis and prognosis biomarkers is getting closer. This review summarises the epigenetic mechanisms underlying canine lymphoma and their possible application as treatment and biomarkers, both prognostic and diagnostic.

Network models in bioinformatics: modeling and analysis for complex diseases.

Networks are present in different aspects of our life: communication networks, World Wide Web, Social Networks, and can be used to conveniently describe biological and clinical data, such as the interactions of proteins in an organism or the connections of neurons in the brain. Therefore, network science, focusing on the network representations of physical, biological and social phenomena and leading to predictive models of these phenomena, currently represents a vast field of application and research for many scientific and social disciplines. The mathematical background for the study and analysis of networks has its roots in the theory of graphs that allows studying real phenomena in a quantitative way. According to the formalism coming from graph theory, nodes of the graph represent entities, whereas edges represent the associations among them. Currently, in bioinformatics and systems biology, there is a growing interest in analyzing associations among biological molecules at a network level. Since the study of associations in a system-level scale has shown great potential, the use of networks has become the de facto standard for representing such associations, and its application fields span from molecular biology to brain connectome analysis [1]. Molecules of different types, e.g. genes, proteins, ribonucleic acids and metabolites, have fundamental roles in the mechanisms of the cellular processes. The study of their structure and interactions is crucial for different reasons, comprising the development of new drugs and the discovery of disease pathways. Thus, the modeling of the complete set of interactions and associations among biological molecules as a graph is convenient for a variety of reasons. Networks provide a simple and intuitive representation of heterogeneous and complex biological processes. Moreover, they facilitate modeling and understanding of complicated molecular mechanisms combining graph theory, machine learning and deep learning techniques. While proteomics and genomics data, represented as data streams or data tables, are mainly used to screen large populations in case–control studies (e.g. for early detection of diseases), interactomics data are represented as graphs and they add a new dimension of analysis, allowing, for instance, the graph-based comparison of organism’s properties. In general, complex biological systems represented as networks provide an integrated way to look into the dynamic behavior of the cellular system through the interactions of components. For instance, biological networks also referred to as Protein–Protein Interaction Networks, model biochemical interactions among proteins. Nodes represent the proteins from a given organism, and the edges represent the protein–protein interactions [2]. Also, gene regulatory network (GRN) is a collection of genes in a cell, which interact each other and with other substances in the cell, such as proteins or metabolites, thereby governing the rates at which genes in the network are transcribed into mRNA. Similarly, the graph-based modeling of the whole system of the brain elements and their relations, so-called brain connectome, is based on the representation of the regions of interest as nodes, and the representation of functional or anatomical connections as edges [3]. Furthermore, recent discoveries in biology have elucidated that the interplay of molecules of different types (e.g. genes, proteins and ribonucleic acids) is a constitutive block of mechanisms inside cells. Consequently, models describing the interplay should be able to consider the presence of multiple different agents and associations, i.e. multiple different types of nodes and edges, that yield to the so-called heterogeneous networks [4].