Computational Modeling Tools Research Articles

Recent Progress in Histone Deacetylase Inhibitors as Anticancer Agents.

Histone Deacetylase (HDAC) inhibitors are a relatively new class of anti-cancer agents that play important roles in epigenetic or non-epigenetic regulation, inducing death, apoptosis, and cell cycle arrest in cancer cells. Recently, their use has been clinically validated in cancer patients resulting in the approval by the FDA of four HDAC inhibitors, vorinostat, romidepsin, belinostat and panobinostat, used for the treatment of cutaneous/peripheral T-cell lymphoma and multiple myeloma. Many more HDAC inhibitors are at different stages of clinical development for the treatment of hematological malignancies as well as solid tumors. Also, clinical trials of several HDAC inhibitors for use as anti-cancer drugs (alone or in combination with other anti-cancer therapeutics) are ongoing. In the intensifying efforts to discover new, hopefully, more therapeutically efficacious HDAC inhibitors, molecular modelingbased rational drug design has played an important role. In this review, we summarize four major structural classes of HDAC inhibitors (hydroxamic acid derivatives, aminobenzamide, cyclic peptide and short-chain fatty acids) that are in clinical trials and different computer modeling tools available for their structural modifications as a guide to discover additional HDAC inhibitors with greater therapeutic utility.

Abstract A28: Systematic identification of astrocyte-tumor crosstalk regulating brain metastatic tumors

Abstract Twenty-four to 45% of all cancer patients develop brain metastasis (BM) with the majority from lung, breast, and melanoma primary cancers, but few patients with BM live longer than a year. Therefore, it is of paramount importance to obtain a deep understanding of mechanisms that promote BM growth, which could be specifically leveraged to develop novel therapeutic strategies. Astrocytes, the most common type of glial cells comprising ~50% of all human brain cells, are a well-characterized perilesional component of BM. Recent discoveries, including ours, provide compelling evidence that molecular crosstalk between astrocytes and cancer cells is integral to BM development. Although seminal findings indicate that interactions with astrocytes occur at both early and late stages of tumor colonization process, our understanding of the reciprocal astrocyte-cancer cell crosstalk is limited. In our studies, we employed the Cell-Cell Communication Explorer (CCCExplorer), a unique computational biology modeling tool, to systematically identify astrocyte-tumor crosstalk signaling in regulating BM. Syngeneic mouse models for BM from breast and lung cancer were injected with mouse cell lines 4T1 (breast cancer) into Balb/c mice and 393N1 (lung cancer) into C57BL/6 mice through internal carotid artery. Whole-brain astrocytes and tumor cells were purified at day 3 post tumor injection (early astrocyte activation) and were profiled by RNA-seq. Even at early stages of BM, transcriptional responses of astrocytes are dramatic, and different tumor origins can induce similar molecular responses in astrocytes. The RNA-seq data were input into the CCCExplorer software to identify four types of cell-cell interaction signaling, i.e., astrocyte→tumor paracrine signaling, astrocyte↔astrocyte autocrine signaling, tumor→astrocyte paracrine signaling, and tumor↔tumor autocrine signaling. For example, our preliminary results show that 30 receptors, 8 transcription factors, and 9 signaling pathways within the 4T1 breast tumor cells were activated by 27 potential ligands secreted by astrocytes. Pathways known to contribute to brain metastatic tumor growth, including TGF-β, PI3K-AKT, gap junction, and MAPK signaling, were reliably identified. Importantly, our unique approach was also able to discern potentially novel inducers of these pathways, such as PCDH7-EGFR, IHNBB-ACVR2A, IL6-IL6R, and CCL5-CCR5. Using in vitro astrocyte and tumor cell coculture assays and astrocyte-specific gene knockout using lenti-CRIPRv2, we demonstrated the significance of the astrocyte→tumor IL6-IL6R, CCL5-CCR5, and PCDH7-EGFR paracrine signaling in early BM. Given that more complicated paracrine signaling networks may evolve during different stages of BM development, we will continue to use CCCExplorer to identify both anti- and prometastatic stimuli from astrocytes to cancer cells and evaluate potential therapeutic targets through in vitro assays and in vivo animal studies. Citation Format: Haowen Nikola Liu, Jianting Sheng, Hong Zhao, Stephen Wong. Systematic identification of astrocyte-tumor crosstalk regulating brain metastatic tumors [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr A28.

Computational Approaches for Studying Slag–Matte Interactions in the Flash Smelting Furnace (FSF) Settler

Computational methods have become reliable tools in many disciplines for research and industrial design. There are, however, an ever-increasing number of details waiting to be included in the models and software, including, e.g., chemical reactions and many physical phenomena, such as particle and droplet behavior and their interactions. The dominant method for copper production, flash smelting, has been extensively investigated, but the settler part of the furnace containing molten high temperature melts termed slag and matte, still lacks a computational modeling tool. In this paper, two commercial modeling software programs have been used for simulating slag–matte interactions in the settler, the target being first to develop a robust computational fluid dynamics (CFD) model and, second, to apply a new approach for molten droplet behavior in a continuum. The latter is based on CFD coupled with the discrete element method (DEM), which was originally developed for modeling solid particle–particle interactions and movement, and is applied here for individual droplets for the first time. The results suggest distinct settling flow phenomena and the significance of droplet coalescence for settling velocity and efficiency. The computing capacity requirement for both approaches is the main limiting factor preventing full-scale geometry modeling with detailed droplet interactions.

The available methods of computer modeling of biofilms and their development trends

The object of this research is the available means of computer modeling of bacterial communities-biofilms. Such communities include the majority (95-99%) of bacteria and are ubiquitous. Biofilms are much more resistant than single bacteria to antibiotics and other antibacterial means due to weak permeability of intercellular environment and moderation of metabolism of some specimen of the community. Examination of influence techniques on biofilms is an important problem of biology; its solution requires various computer modeling tools that allow achieving significant scientific results. The subject of this research is the mathematical models used in modern techniques of biofilm modeling, and as well as the software for their implementation. The conclusion is made that the available methods of biofilm modeling are capable of successful reproduction of multiple behavioral aspects of these bacteria communities, including growth, destruction, and self-regulation; however, certain relevant problems are yet to be resolved. This is associated with the fact that the development of modern software for two-dimensional and three-dimensional agent modeling of biofilms – BSim, iDynoMiCS, CellModeller, require competent programmers for describing the interactions between simulated objects. Most promising direction in further development of this software implies a more active usage of tools for describing behavior and interaction of agents applied in the sphere of artificial intelligence, for example, fin-state automaton or production rules systems, with mandatory preservation of biological content of the models.

A Study of Nonstructural Protein 1 of SARS Coronavirus

Severe acute respiratory syndrome coronavirus (SARS‐CoV) encoded nonstructural protein 1 (nsp1) orchestrates a multi‐pronged mechanism to down regulate host gene expression, also known as host shutoff. Nsp1 is a 180 amino acid long protein with primarily flexible structure that allows nsp1 to interact with various cellular factors during host shutoff to inhibit translation and promote mRNA decay. Mutations of the surface residues of nsp1 either attenuates or augments its host shutoff function prompting the hypothesis that these residues bind cellular proteins that facilitate nsp1 during host shutoff. To identify cellular proteins that interact with nsp1 and assist its function, we conducted a quantitative proteomics experiment by comparing protein complexes associated with nsp1 and its inactive mutant (K164AH165A) and computer modeling to predict structural changes. By comparing protein complexes, we identified a previously undetected mechanism by which nsp1 exploits the nuclear pore complex and disrupts nuclear‐cytoplasmic transport of biomolecules. Expression of nsp1 in 293 cells disrupts Nup93 localization around the nuclear envelope without triggering proteolytic degradation of the protein while other nucleoporins and the nuclear lamina remain unperturbed, suggesting a significant alteration of the nuclear pore complex in the presence of nsp1. However, expression of mutant nsp1, which does not bind to Nup93, does not alter the nuclear pore complex. To better understand the structural basis of this protein’s function, we have utilized computational modeling tools, more specifically, I‐TASSER (Iterative Threading ASSEmbly Refinement). Structural models of various mutants of nsp1 were obtained (by using I‐TASSER) in order to explore the structural impact of different mutations. Our preliminary explorations show significant topological changes on nsp1’s surface for specific mutations. These perturbations of surface properties can provide a steric‐based mechanism of interfering with nsp1’s binding or recognition function therefore, aggravating its host shutoff function.

Nanoscale Structure Determination of Murray Valley Encephalitis and Powassan Virus Non-Coding RNAs.

Viral infections are responsible for numerous deaths worldwide. Flaviviruses, which contain RNA as their genetic material, are one of the most pathogenic families of viruses. There is an increasing amount of evidence suggesting that their 5’ and 3’ non-coding terminal regions are critical for their survival. Information on their structural features is essential to gain detailed insights into their functions and interactions with host proteins. In this study, the 5’ and 3’ terminal regions of Murray Valley encephalitis virus and Powassan virus were examined using biophysical and computational modeling methods. First, we used size exclusion chromatography and analytical ultracentrifuge methods to investigate the purity of in-vitro transcribed RNAs. Next, we employed small-angle X-ray scattering techniques to study solution conformation and low-resolution structures of these RNAs, which suggest that the 3’ terminal regions are highly extended as compared to the 5’ terminal regions for both viruses. Using computational modeling tools, we reconstructed 3-dimensional structures of each RNA fragment and compared them with derived small-angle X-ray scattering low-resolution structures. This approach allowed us to reinforce that the 5’ terminal regions adopt more dynamic structures compared to the mainly double-stranded structures of the 3’ terminal regions.

Computational Tools for Modeling and Analysis of Power Generation and Transmission Systems of the Smart Grid

The traditional power generation and distribution systems will be supplanted by the Internet of Energy, which accelerates the necessity to know the appropriate computation tools to perform any research in this future smart grid arena. However, there is a plethora of computational tools in this area, which challenges the researchers to find an appropriate tool based on their research objectives. Therefore, this article presents a comprehensive study about existing simulation tools related to electrical power generation, transmission, distribution, and associated systems. It provides an overview of more than 150 simulation software in these areas. The tools are classified and discussed based on both traditional and CEN-CENELEC-ETSI smart-grid reference architecture. Typical applications, sources, availability, and strengths of each tool are listed. Each tool has its own strengths and limitations to perform a certain task, and necessary information are provided to help researchers to find an appropriate computational tool for their specific research goal.

RECENT ADVANCES IN TITANIUM TECHNOLOGY IN THE UNITED STATES

Substantial progress has continued in the US since the last (13th) World Titanium Conference with regards to titanium technology, products, processing, computational modeling tools and applications. Titanium components produced by additive manufacturing have been qualified and achieved production applications in commercial and military aircraft hardware. New high-performance titanium alloys have been developed for higher temperature service and applications requiring more demanding static and dynamic properties. Current production processes for titanium have been optimized, and new processes have been developed to further reduce cost and improve product quality. Titanium suppliers, OEM’s, government labs and academia are working closely together to address fundamental industry-wide issues. Computer modeling is now used extensively in industrial and research facilities to accelerate the pace and success of these developments.

A case study on solar, wind and hydro energy resource potential of Prizren region in Kosovo

Prizren is a city located in the south part of Kosovo with approximately 90,000 inhabitants and land area of 640 km2. The region is covered with distinct geographical features, that favor penetration and deployment of various forms of renewable energy. Particularly, solar, wind and hydro energy potential are considered to be the most available options here. In this study, the potential of these renewable energy sources and their viability for energy production are evaluated using a computational modeling tool. The potential investment opportunities are analyzed based on a 50-year life time project. The results suggest that integrating renewable energy to the existing energy system will enable Prizren region and partly the entire Kosovo for coping with load fluctuations in energy demand. Also, it is expected that the added renewable energy in the existing energy mix will, in a broad perspective, lead to meet the European Union’s target of accelerated renewable energy penetration by the year 2030, and in turn to reduce the greenhouse gas emission to the environment.

Особенности моделирования электрического и магнитного поверхностного эффекта от переменных электромагнитных полей в Femm

The electromagnetic field theory still remains among of the most difficult sections of electrical engineering that are studied by the students of relevant universities. Computer modeling is a modern tool that helps master this section in a more efficient way. This is especially important in view of the trend toward increasing the role of independent work assigned to students in the modern educational programs. However, even quite advanced computer modeling tools still contain some hidden features. The article addresses the problem of overcoming the difficulties encountered in using the FEMM computer program for modeling alternating electromagnetic fields. It has been found that the way in which the FEMM computerprogram models the influence of the electrical skin effect in alternating current conductors is incorrect. At the same time, the FEMM computer program models the influence of the magnetic skin effect in billets free from extraneous currents placed in the magnetic field of an inductor at a specified frequency is a correct way. Examples of field models for the considered cases are given.

The use of simulation when designing road junctions

The high level of motorization in most countries of the world is the reason for the increase in the number of car accidents. Currently, it is understood that a further increase in the number of cars without significant changes in the transport infrastructure will only aggravate the already difficult situation on roads. The article addresses the issues of road traffic injuries and statistical information on road accidents. The analysis of the work of leading transport engineers presents the results of studies on the use of computer modeling tools for designing road conditions at various levels. From the presented analysis, it is possible to conclude that the most suitable tool for assessing the safety of road junctions is the PTV VISSIM software package, which also allows to export vehicle trajectories to SSAM software. In the framework of the analytic survey of the intersection safety, based on the analysis of vehicle trajectories, the parameters of the future conflict of the planned road junctions were established. Studies have shown that a road junction at different levels makes possible to triple the safety of junctions in comparison with the roundabout. The use of simulation allows not to wait for emergency cases, but to evaluate hypothetical structures and alternative layout options for road junctions, where it is not possible to use traditional, statistical models for assessing the probability of car accidents.

Chemistry Teachers’ Awareness, Understanding, and Confidence toward Computational Tools for Molecular Visualization

Recent advances in cheminformatics and bioinformatics research have generated software and computational tools for molecular modeling and visualization that can be incorporated to improve chemistry teaching and learning in high school. Nevertheless, there have never been any study simultaneously reporting chemistry teachers’ awareness, understanding, and confidence toward contemporary computer-aided molecular modeling and visualization tools. This study examined 32 high school chemistry teachers’ knowledge, understanding and confidence toward nine new computational programs on molecular modeling and visualization namely ChemDraw, HyperChem, UCSF Chimera, Marvin Sketch, PsiPred, MBC (PS2), Rampage, Vienna RNA Package and Mode RNA following two days of professional workshop-based training. After completing the training and assessments, the teachers showed an enhancement in awareness, understanding, and confidence toward those nine computational programs. Intensive activities consisting of theoretical lectures, hands-on practices, assignments, and case study presentations seem to provide valuable resources to the increase in teachers’ knowledge, understanding, and skill that incorporates computational technology. Hence, the impact of this research pointed toward the value of teachers’ professional development that creates a platform to reduce the barriers of access, resources, knowledge, and skills. This study is expected to help improving teachers’ awareness, understanding, and confidence necessarily required for further implementation of available technology for instructional purposes.Â

Population Life-course exposure to health effects model (PLETHEM): An R package for PBPK modeling

An outstanding challenge in the acceptance of alternatives to animal testing is the systematic incorporation of computational models into decision making pipelines. Fifteen years ago, the US EPA Office of Research and Development's framework for computational toxicology emphasized the need for computational methods to bridge the source-to-outcome continuum. This can be achieved by linking exposure estimation methods, physiologically based pharmacokinetic (PBPK) modeling, and computational systems biology pathway modeling tools into a standardized framework. To that end, we have developed the Population Life-course Exposure to Health Effects Model (PLETHEM) suite, a modular open source modeling platform that provides users the ability to create, run, share, and audit PBPK models. The platform consists of a database of chemicals, QSAR models, life-stage specific physiological and metabolic parameters needed to parameterize PBPK models, an R-based engine to perform model simulations, and an interactive user interface to define and select parameter sets for the models. PLETHEM implements easy to use interfaces for a generic PBPK model and a high-throughput IVIVE model. These model interfaces along with the included databases provide capabilities necessary for rapid analysis of chemicals using PBPK modeling. PLETHEM includes the ability to run Monte Carlo analyses to investigate population variance and a set of life-stage equations to investigate life-stage-based sensitivities. The PLETHEM database also incorporates ontogeny profiles for key metabolic enzymes that can be used to calculate in vivo metabolic clearance using measured in vitro clearance. In addition, PLETHEM has an ability to link to a number of EPA and OECD exposure estimation programs. These models, which estimate exposures in the workplace and the general populations, can be used to drive PBPK model-based estimates of resulting internal exposures to support risk assessments. PLETHEM is now freely available as an R package through the Bitbucket and GitHub open source repositories.

ECHO, the executable CHOndrocyte: A computational model to study articular chondrocytes in health and disease

Computational modeling can be used to investigate complex signaling networks in biology. However, most modeling tools are not suitable for molecular cell biologists with little background in mathematics. We have built a visual-based modeling tool for the investigation of dynamic networks. Here, we describe the development of computational models of cartilage development and osteoarthritis, in which a panel of relevant signaling pathways are integrated. In silico experiments give insight in the role of each of the pathway components and reveal which perturbations may deregulate the basal healthy state of cells and tissues.We used a previously developed computational modeling tool Analysis of Networks with Interactive Modeling (ANIMO) to generate an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We performed in silico experiments to characterize molecular mechanisms of cell fate decisions. The model was used to mimic biological scenarios during cell differentiation using RNA-sequencing data of a variety of stem cell sources as input. In a case-study, we wet-lab-tested the model-derived hypothesis that expression of DKK1 (Dickkopf-1) and FRZB (Frizzled related protein, WNT antagonists) and GREM1 (gremlin 1, BMP antagonist) prevents IL1β (Interleukin 1 beta)-induced MMP (matrix metalloproteinase) expression, thereby preventing cartilage degeneration, at least in the short term. We found that a combination of DKK1, FRZB and GREM1 may play a role in modulating the effects of IL1β induced inflammation in human primary chondrocytes.

Abstract C108: Systematic identification of astrocyte-tumor crosstalk regulating brain metastatic tumors

Abstract Twenty-four to 45 percent of all cancer patients develop brain metastasis (BM) with the majority from lung, breast, and melanoma primary cancers, but few patients with BM lives longer than a year. Therefore, it is of paramount importance to obtain a deep understanding of mechanisms that promote BM growth which could be specifically leveraged to develop novel therapeutic strategies. Astrocytes, the most common type of glial cells comprising ~50% of all human brain cells, are a well characterized perilesional component of BM. Recent discoveries, including ours, provide compelling evidence that molecular crosstalk between astrocytes and cancer cells is integral to BM development. Although seminal findings indicate that interactions with astrocytes occur at both early and late stages of tumor colonization process, our understanding of the reciprocal astrocyte-cancer cell crosstalk is limited. In our studies, we employed the Cell-Cell Communication Explorer (CCCExplorer), a unique computational biology modeling tool, to systematically identify astrocyte-tumor crosstalk signaling in regulating BM. Syngeneic mouse models for BM from breast and lung cancer were injected with mouse cell lines 4T1 (breast cancer) into Balb/c mice and 393N1 (lung cancer) into C57BL/6 mice through internal carotid artery. Whole brain astrocytes and tumor cells were purified at day 3 post tumor injection (early astrocyte activation) and were profiled by RNA-seq. Even at early stages of BM, transcriptional responses of astrocytes are dramatic, and different tumor origins can induce similar molecular responses in astrocytes. The RNA-seq data were input into the CCCExplorer software to identify four types of cell-cell interaction signaling, i.e., astrocyte→tumor paracrine signaling, astrocyte↔astrocyte autocrine signaling, tumor→astrocyte paracrine signaling, and tumor↔tumor autocrine signaling. For example, our preliminary results show that 30 receptors, 8 transcription factors, and 9 signaling pathways within the 4T1 breast tumor cells were activated by 27 potential ligands secreted by astrocytes. Pathways known to contribute to brain metastatic tumor growth including TGF-β, PI3K-AKT, gap junction, and MAPK signaling were reliably identified. Importantly, our unique approach was also able to discern potentially novel inducers of these pathways, such as PCDH7-EGFR, IHNBB-ACVR2A, IL6-IL6R, and CCL5-CCR5. Using in vitro astrocyte and tumor cell co-culture assays and astrocyte-specific gene knockout using lenti-CRIPRv2, we demonstrated the significance of the astrocyte→tumor IL6-IL6R, CCL5-CCR5, and PCDH7-EGFR paracrine signaling in early BM. Given that more complicated paracrine signaling networks may evolve during different stages of BM development, we will continue to use CCCExplorer to identify both anti- and pro-metastatic stimuli from astrocytes to cancer cells and evaluate potential therapeutic targets through in vitro assays and in vivo animal studies. Citation Format: Haowen Liu, Jianting Sheng, HONG ZHAO, Stephen Wong. Systematic identification of astrocyte-tumor crosstalk regulating brain metastatic tumors [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C108. doi:10.1158/1535-7163.TARG-19-C108

Chemical Cross-Linking and Mass Spectrometric Analysis of the Endogenous Yeast Exosome Complexes.

Chemical cross-linking and mass spectrometric readout (CX-MS) has become a useful toolkit for structural analysis of protein complexes. CX-MS enables rapid detection of a larger number of cross-link peptides from the chemically cross-linked protein assembly, providing invaluable cross-link spatial restraints to understand the architecture of the complex. Since CX-MS is complementary with other structural and computational modeling tools, it can be used for integrative structural determination of large native protein assemblies. However, due to technical limitations, current CX-MS applications have still been predominantly confined to complexes reconstituted from recombinant proteins where large amount of purified materials are available. Cross-linking and hybrid structural proteomic analysis of endogenous protein complexes remains a challenge. In this chapter, we present a protocol that efficiently couples affinity capture of endogenous complexes with sensitive CX-MS analysis, with particular application to the yeast RNA processing exosome complexes.

Ocean Energy Systems Wave Energy Modelling Task: Modelling, Verification and Validation of Wave Energy Converters

The International Energy Agency Technology Collaboration Programme for Ocean Energy Systems (OES) initiated the OES Wave Energy Conversion Modelling Task, which focused on the verification and validation of numerical models for simulating wave energy converters (WECs). The long-term goal is to assess the accuracy of and establish confidence in the use of numerical models used in design as well as power performance assessment of WECs. To establish this confidence, the authors used different existing computational modelling tools to simulate given tasks to identify uncertainties related to simulation methodologies: (i) linear potential flow methods; (ii) weakly nonlinear Froude–Krylov methods; and (iii) fully nonlinear methods (fully nonlinear potential flow and Navier–Stokes models). This article summarizes the code-to-code task and code-to-experiment task that have been performed so far in this project, with a focus on investigating the impact of different levels of nonlinearities in the numerical models. Two different WECs were studied and simulated. The first was a heaving semi-submerged sphere, where free-decay tests and both regular and irregular wave cases were investigated in a code-to-code comparison. The second case was a heaving float corresponding to a physical model tested in a wave tank. We considered radiation, diffraction, and regular wave cases and compared quantities, such as the WEC motion, power output and hydrodynamic loading.

Structural modeling and role of HAX-1 as a positive allosteric modulator of human serine protease HtrA2.

HAX-1, a multifunctional protein involved in cell proliferation, calcium homeostasis, and regulation of apoptosis, is a promising therapeutic target. It regulates apoptosis through multiple pathways, understanding of which is limited by the obscurity of its structural details and its intricate interaction with its cellular partners. Therefore, using computational modeling, biochemical, functional enzymology and spectroscopic tools, we predicted the structure of HAX-1 as well as delineated its interaction with one of it pro-apoptotic partner, HtrA2. In this study, three-dimensional structure of HAX-1 was predicted by threading and ab initio tools that were validated using limited proteolysis and fluorescence quenching studies. Our pull-down studies distinctly demonstrate that the interaction of HtrA2 with HAX-1 is directly through its protease domain and not via the conventional PDZ domain. Enzymology studies further depicted that HAX-1 acts as an allosteric activator of HtrA2. This 'allosteric regulation' offers promising opportunities for the specific control and functional modulation of a wide range of biological processes associated with HtrA2. Hence, this study for the first time dissects the structural architecture of HAX-1 and elucidates its role in PDZ-independent activation of HtrA2.

Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives.

The sea represents a major source of biodiversity. It exhibits many different ecosystems in a huge variety of environmental conditions where marine organisms have evolved with extensive diversification of structures and functions, making the marine environment a treasure trove of molecules with potential for biotechnological applications and innovation in many different areas. Rapid progress of the omics sciences has revealed novel opportunities to advance the knowledge of biological systems, paving the way for an unprecedented revolution in the field and expanding marine research from model organisms to an increasing number of marine species. Multi-level approaches based on molecular investigations at genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, and metabolomic levels are essential to discover marine resources and further explore key molecular processes involved in their production and action. As a consequence, omics approaches, accompanied by the associated bioinformatic resources and computational tools for molecular analyses and modeling, are boosting the rapid advancement of biotechnologies. In this review, we provide an overview of the most relevant bioinformatic resources and major approaches, highlighting perspectives and bottlenecks for an appropriate exploitation of these opportunities for biotechnology applications from marine resources.

A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body’s most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step, we provided a test signal to the system, at the edge of the hearing threshold. As next step, we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content.