A grid layout algorithm for automatic drawing of biochemical networks

MOTIVATION: Visualization is indispensable in the research of complex biochemical networks. Available graph layout algorithms are not adequate for satisfactorily drawing such networks. New methods are required to visualize automatically the topological architectures and facilitate the understanding of the functions of the networks. RESULTS: We propose a novel layout algorithm to draw complex biochemical networks. A network is modeled as a system of interacting nodes on squared grids. A discrete cost function between each node pair is designed based on the topological relation and the geometric positions of the two nodes. The layouts are produced by minimizing the total cost. We design a fast algorithm to minimize the discrete cost function, by which candidate layouts can be produced efficiently. A simulated annealing procedure is used to choose better candidates. Our algorithm demonstrates its ability to exhibit cluster structures clearly in relatively compact layout areas without any prior knowledge. We developed Windows software to implement the algorithm for CADLIVE. AVAILABILITY: All materials can be freely downloaded from http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/ SUPPLEMENTARY INFORMATION: http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/     

MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments

Background

Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures.

Results

We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, “MEGADOCK”, by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis.

Conclusions

We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.

     

Boolean modeling techniques for protein co-expression networks in systems medicine

Introduction: Application of systems biology/systems medicine approaches is promising for proteomics/biomedical research, but requires selection of an adequate modeling type.

Areas covered: This article reviews the existing Boolean network modeling approaches, which provide in comparison with alternative modeling techniques several advantages for the processing of proteomics data. Application of methods for inference, reduction and validation of protein co-expression networks that are derived from quantitative high-throughput proteomics measurements is presented. It’s also shown how Boolean models can be used to derive system-theoretic characteristics that describe both the dynamical behavior of such networks as a whole and the properties of different cell states (e.g. healthy or diseased cell states). Furthermore, application of methods derived from control theory is proposed in order to simulate the effects of therapeutic interventions on such networks, which is a promising approach for the computer-assisted discovery of biomarkers and drug targets. Finally, the clinical application of Boolean modeling analyses is discussed.

Expert commentary: Boolean modeling of proteomics data is still in its infancy. Progress in this field strongly depends on provision of a repository with public access to relevant reference models. Also required are community supported standards that facilitate input of both proteomics and patient related data (e.g. age, gender, laboratory results, etc.).     

TinkerCell: modular CAD tool for synthetic biology

Background

Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. Using a CAD application, it would be possible to construct models using available biological "parts" and directly generate the DNA sequence that represents the model, thus increasing the efficiency of design and construction of synthetic networks.

Results

An application named TinkerCell has been developed in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various third-party C and Python programs that are hosted by TinkerCell via an extensive C and Python application programming interface (API). TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at http://www.tinkercell.com.

Conclusion

An ideal CAD application for engineering biological systems would provide features such as: building and simulating networks, analyzing robustness of networks, and searching databases for components that meet the design criteria. At the current state of synthetic biology, there are no established methods for measuring robustness or identifying components that fit a design. The same is true for databases of biological parts. TinkerCell's flexible modeling framework allows it to cope with changes in the field. Such changes may involve the way parts are characterized or the way synthetic networks are modeled and analyzed computationally. TinkerCell can readily accept third-party algorithms, allowing it to serve as a platform for testing different methods relevant to synthetic biology.     

Larval host records of butterflies in Japan

Using Japanese literature, we created a consolidated list of host records of butterflies in Japan. The list used the host records described in eight major illustrated reference books, two checklists, and 14 other pieces of literature. The presence of larvae on plants, the observation of larvae eating plants or insects in the field were considered as host records. We collected all species recorded in Japan. Scientific, family, and Japanese names of butterflies were consolidated using the BINRAN database (http://binran.lepimages.jp/). Scientific and Japanese names of host plants were based on the YList database (http://ylist.info/). If scientific names of host plants were not found in YList, we used scientific names based on The Plant List (http://www.theplantlist.org/). Family names of host plants were based on the Catalogue of Life database (http://www.catalogueoflife.org/). Scientific, family, and Japanese names of host insects were based on the MOKUROKU database (http://konchudb.agr.agr.kyushu-u.ac.jp/mokuroku/) for Hymenoptera and the catalogue of the Paraneoptera of Japan published by the Entomological Society of Japan for Hemiptera. We also provided the references of each host record and the original names described in the referred literature. Two datasets, HostDB and ReferenceDB, were created to include 3600 records of butterfly larval hosts in Japan, along with scientific and Japanese names of each species and a literature list. These datasets will be useful for basic and applied biological studies of butterflies. Data files are stored in the Ecological Research Data Archives (http://db.cger.nies.go.jp/JaLTER/ER_DataPapers/) and available from http://hostbj.lepumus.net/. These datasets are published under the Creative Commons License Attribution-ShareAlike 4.0 (CC BY-SA, https://creativecommons.org/licenses/by-sa/4.0/).     

Standardization in synthetic biology: an engineering discipline coming of age

Background: Leaping DNA read-and-write technologies, and extensive automation and miniaturization are radically transforming the field of biological experimentation by providing the tools that enable the cost-effective high-throughput required to address the enormous complexity of biological systems. However, standardization of the synthetic biology workflow has not kept abreast with dwindling technical and resource constraints, leading, for example, to the collection of multi-level and multi-omics large data sets that end up disconnected or remain under- or even unexploited.

Purpose: In this contribution, we critically evaluate the various efforts, and the (limited) success thereof, in order to introduce standards for defining, designing, assembling, characterizing, and sharing synthetic biology parts. The causes for this success or the lack thereof, as well as possible solutions to overcome these, are discussed.

Conclusion: Akin to other engineering disciplines, extensive standardization will undoubtedly speed-up and reduce the cost of bioprocess development. In this respect, further implementation of synthetic biology standards will be crucial for the field in order to redeem its promise, i.e. to enable predictable forward engineering.     

A systems biology-led insight into the role of the proteome in neurodegenerative diseases

Introduction: Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases.

Areas covered: Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer’s disease, Parkinson’s disease and other neurodegenerative disorders, as well as of affected human tissues.

Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.     

Regulatory links between PLC enzymes and Ras superfamily GTPases: Signalling via PLC

A growing body of work implies that links between PLC isoforms, in particular PLC, and small G-proteins from Ras superfamily could be important in regulation of a number of cellular processes. Through successful use of biochemistry and structural biology, several interactions have been characterized providing some ideas about the regulatory mechanisms. A number of signalling pathways have also been suggested that could involve direct interaction of Ras and Rho GTPases with PLC. Importantly, several studies combining cell biology and genetics have provided new insights into functions of PLC and highlighted the importance of this approach to extend further and consolidate currently incomplete picture regarding its roles in development and disease.     

Acceleration of Soybean Genomics Using Large Collections of DNA Markers for Gene Discovery

Legumes are known to provide nutritious proteins and vegetableoils while at the same time providing industrial products suchas biodiesel. It is estimated that approximately 25% of worldcrop production is derived from legumes. Recently, knowledgeof the molecular biology and genomics of legumes have been extendedsignificantly using two model species, Lotus japonicus (http://www.kazusa.or.jp/lotus/)and Medicago     

Improvements in algal lipid production: a systems biology and gene editing approach

Background: In the wake of rising energy demands, microalgae have emerged as potential sources of sustainable and renewable carbon-neutral fuels, such as bio-hydrogen and bio-oil.

Purpose: For rational metabolic engineering, the elucidation of metabolic pathways in fine detail and their manipulation according to requirements is the key to exploiting the use of microalgae. Emergence of site-specific nucleases have revolutionized applied research leading to biotechnological gains. Genome engineering as well as modulation of the endogenous genome with high precision using CRISPR systems is being gradually employed in microalgal research. Further, to optimize and produce better algal platforms, use of systems biology network analysis and integration of omics data is required. This review discusses two important approaches: systems biology and gene editing strategies used on microalgal systems with a focus on biofuel production and sustainable solutions. It also emphasizes that the integration of such systems would contribute and compliment applied research on microalgae.

Conclusions: Recent advances in microalgae are discussed, including systems biology, gene editing approaches in lipid bio-synthesis, and antenna engineering. Lastly, it has been attempted here to showcase how CRISPR/Cas systems are a better editing tool than existing techniques that can be utilized for gene modulation and engineering during biofuel production.     

Modeling biological systems using Dynetica--a simulator of dynamic networks

We present Dynetica, a user-friendly simulator of dynamic networks for constructing, visualizing, and analyzing kinetic models of biological systems. In addition to generic reaction networks, Dynetica facilitates construction of models of genetic networks, where many reactions are gene expression and interactions among gene products. Further, it integrates the capability of conducting both deterministic and stochastic simulations. AVAILABILITY AND SUPPLEMENTARY INFORMATION: Dynetica 1.0, example models, and the user's guide are available at http://www.its.caltech.edu/~you/Dynetica/Dynetica_page.htm     

Chemistry and biology for the small molecules targeting characteristics of cancer cells

Despite the marked progress of cancer research, cancer is the predominant cause of death in Japan, and therefore development of effective therapeutic drugs is expected.

Chemical biology is a research field utilizing small molecules to investigate biological phenomena. One of the most important aims of chemical biology is to find the small molecules, and natural products are ideal screening sources due to their structural diversity. Therefore, natural product screening based on the progress of chemical biology prompted us to find small molecules targeting cancer characteristics. Another contribution of chemical biology is to facilitate the target identification of small molecule. Therefore, among a variety of methods to uncover protein function, chemical biology is a remarkable approach in which small molecules are used as probes to elucidate protein functions related to cancer development.

Abbreviations: EGF: Epidermal growth factor; PDGF: Platelet-derived growth factor; CRPC: Castration-resistant prostate cancer; AR: Androgen receptor; FTase: Farnesyl transferase; 5-LOX: 5-Lipoxygenase; LT: Leukotriene; CysLT1: Cysteinyl leukotriene receptor 1; GPA: Glucopiericidin A; PA: Piericidin A; XN: Xanthohumol; VCP: Valosin-containing protein; ACACA: Acetyl-CoA carboxylase-α.     

Monitoring proteolytic processing events by quantitative mass spectrometry

Introduction: Protease activity plays a key role in a wide variety of biological processes including gene expression, protein turnover and development. misregulation of these proteins has been associated with many cancer types such as prostate, breast, and skin cancer. thus, the identification of protease substrates will provide key information to understand proteolysis-related pathologies.

Areas covered: Proteomics-based methods to investigate proteolysis activity, focusing on substrate identification, protease specificity and their applications in systems biology are reviewed. Their quantification strategies, challenges and pitfalls are underlined and the biological implications of protease malfunction are highlighted.

Expert commentary: Dysregulated protease activity is a hallmark for some disease pathologies such as cancer. Current biochemical approaches are low throughput and some are limited by the amount of sample required to obtain reliable results. Mass spectrometry based proteomics provides a suitable platform to investigate protease activity, providing information about substrate specificity and mapping cleavage sites.     

Optimized submerged batch fermentation strategy for systems scale studies of metabolic switching in Streptomyces coelicolor A3(2)

Background

Given the complex mechanisms underlying biochemical processes systems biology researchers tend to build ever increasing computational models. However, dealing with complex systems entails a variety of problems, e.g. difficult intuitive understanding, variety of time scales or non-identifiable parameters. Therefore, methods are needed that, at least semi-automatically, help to elucidate how the complexity of a model can be reduced such that important behavior is maintained and the predictive capacity of the model is increased. The results should be easily accessible and interpretable. In the best case such methods may also provide insight into fundamental biochemical mechanisms.

Results

We have developed a strategy based on the Computational Singular Perturbation (CSP) method which can be used to perform a "biochemically-driven" model reduction of even large and complex kinetic ODE systems. We provide an implementation of the original CSP algorithm in COPASI (a COmplex PAthway SImulator) and applied the strategy to two example models of different degree of complexity - a simple one-enzyme system and a full-scale model of yeast glycolysis.

Conclusion

The results show the usefulness of the method for model simplification purposes as well as for analyzing fundamental biochemical mechanisms. COPASI is freely available at http://www.copasi.org.     

Middle-down proteomics: a still unexploited resource for chromatin biology

Introduction: Analysis of histone post-translational modifications (PTMs) by mass spectrometry (MS) has become a fundamental tool for the characterization of chromatin composition and dynamics. Histone PTMs benchmark several biological states of chromatin, including regions of active enhancers, active/repressed gene promoters and damaged DNA. These complex regulatory mechanisms are often defined by combinatorial histone PTMs; for instance, active enhancers are commonly occupied by both marks H3K4me1 and H3K27ac. The traditional bottom-up MS strategy identifies and quantifies short (aa 4–20) tryptic peptides, and it is thus not suitable for the characterization of combinatorial PTMs.

Areas covered: Here, we review the advancement of the middle-down MS strategy applied to histones, which consists in the analysis of intact histone N-terminal tails (aa 50–60). Middle-down MS has reached sufficient robustness and reliability, and it is far less technically challenging than PTM quantification on intact histones (top-down). However, the very few chromatin biology studies applying middle-down MS resulting from PubMed searches indicate that it is still very scarcely exploited, potentially due to the apparent high complexity of method and analysis.

Expert commentary: We will discuss the state-of-the-art workflow and examples of existing studies, aiming to highlight its potential and feasibility for studies of cell biologists interested in chromatin and epigenetics.     

Genomic database resources for Dictyostelium discoideum

Dictyostelium is an attractive model system for the study of mechanisms basic to cellular function or complex multicellular developmental processes. Recent advances in Dictyostelium genomics have generated a wide spectrum of resources. However, much of the current genomic sequence information is still not currently available through GenBank or related databases. Thus, many investigators are unaware that extensive sequence data from Dictyostelium has been compiled, or of its availability and access. Here, we discuss progress in Dictyostelium genomics and gene annotation, and highlight the primary portals for sequence access, manipulation and analysis (http://genome.imb-jena.de/dictyostelium/; http://dictygenome.bcm.tmc.edu/; http://www.sanger. ac.uk/Projects/D_discoideum/; http://www.csm.biol. tsukuba.ac.jp/cDNAproject.html).     

Three-way interaction model with switching mechanism as an effective strategy for tracing functionally-related genes

Introduction: Identification of functionally-related genes is an important step in understanding biological systems. The most popular strategy to infer functional dependence is to study pairwise correlations between gene expression levels. However, certain functionally-related genes may have a low expression correlation due to their nonlinear interactions. The use of a three-way interaction (3WI) model with switching mechanism (SM) is a relatively new strategy to trace functionally-related genes. The 3WI model traces the dynamic and nonlinear nature of the co-expression relationship of two genes by introducing their link to the expression level of a third gene.

Areas covered: In this paper, we reviewed a variety of existing methods for tracing the 3WIs. Furthermore, we provide a comprehensive review of the previous biological studies based on 3WI models.

Expert commentary: Comparison of features of these methods indicates that the modified liquid association algorithm has the best efficiency for tracing 3WI between others. The limited number of biological studies based on the 3WI suggests that high computational demand of the available algorithms is a major challenge to apply this approach for analyzing high-throughput omics data.     

Integrative omics - from data to biology

Introduction: Multi-omic approaches are promising a broader view on cellular processes and a deeper understanding of biological systems. with strongly improved high-throughput methods the amounts of data generated have become huge, and their handling challenging.

Area Covered: New bioinformatic tools and pipelines for the integration of data from different omics disciplines continue to emerge, and will support scientists to reliably interpret data in the context of biological processes. comprehensive data integration strategies will fundamentally improve systems biology and systems medicine. to present recent developments of integrative omics, the göttingen proteomics forum (gpf) organized its 6th symposium on the 23rd of november 2017, as part of a series of regular gpf symposia. more than 140 scientists attended the event that highlighted the challenges and opportunities but also the caveats of integrating data from different omics disciplines.

Expert commentary: The continuous exponential growth in omics data require similar development in software solutions for handling this challenge. Integrative omics tools offer the chance to handle this challenge but profound investigations and coordinated efforts are required to boost this field.     

Visualizing global properties of large complex networks

For complex biological networks, graphical representations are highly desired for understanding some design principles, but few drawing methods are available that capture topological features of a large and highly heterogeneous network, such as a protein interaction network. Here we propose the circular perspective drawing (CPD) method to visualize global structures of large complex networks. The presented CPD combines the quasi-continuous search (QCS) analogous to the steepest descent method with a random node swapping strategy for an enhanced calculation speed. The CPD depicts a network in an aesthetic manner by showing connection patterns between different parts of the network instead of detailed links between nodes. Global structural features of networks exhibited by CPD provide clues toward a comprehensive understanding of the network organizations. Availability: Software is freely available at http://www.cadlive.jp.     

A new nitrobenzoxadiazole-based GSTP1-1 inhibitor with a previously unheard of mechanism of action and high stability

Context: The nitrobezoxadiazole derivative NBDHEX is a potent inhibitor of glutathione transferase P1-1 (GSTP1-1) endowed with outstanding anticancer activity in different tumor models.

Objective: To characterize by in vitro biochemical and in silico studies the NBDHEX analogues named MC2752 and MC2753.

Materials and methods: Synthesis of MC2752 and MC2753, biochemical assays and in silico docking and normal-mode analyses.

Results: The presence of a hydrophobic moiety in the side chain of MC2753 confers unique features to this molecule. Unlike its parent drug NBDHEX, MC2753 does not require GSH to trigger the dissociation of the complex between GSTP1-1 and TRAF2, and displays high stability towards the nucleophilic attack of the tripeptide under physiological conditions.

Discussion and conclusion: MC2753 may represent a lead compound for the development of novel GSTP1-1 inhibitors not affected in their anticancer action by fluctuations of cellular GSH levels, and characterized by an increased half-life in vivo.