Systems Biology Era Research Articles

Dynamic In Silico Model of Type 2 Diabetes Treated With Metformin Combined With Exercise: A Sobol Sensitivity Analysis

Background: The widespread adoption of a sedentary lifestyle characterized by the consumption of a high-energy diet combined with a lack of physical activity has resulted in a rise in the prevalence of metabolic disorders like type 2 diabetes (T2D) globally. The data published by the International Diabetes Federation (IDF) every year catches the attention of the world. Insulin resistance results in the dysregulation of the control mechanism that leads to T2D. In addition to metformin as a globally prescribed drug, physical exercise is recommended for the treatment of T2D as an alternative therapy. In today’s systems biology era, sensitivity analysis plays a major role in providing deeper insights into the metabolic insulin signalling pathways (ISPs). Methods: Here, the complex in silico dynamic model of the effect of alternative therapies on T2D was examined. In order to obtain more quantitative information about the signal transduction network of the ISPs and their interaction, a global sensitivity analysis of the in silico model was carried out using the MATLAB tool. Results: This study sheds light on the effect of parameter perturbation on the ISP via GLUT4 translocation in T2D patients performing physical exercise and using metformin. It also allows for the selection of a variety of parameters for in vivo or in vitro studies in the future on the basis of the impact of parameter variations on each component of the pathway. Conclusion: The Sobol index for each condition was noted after introducing perturbations in the most sensitive parameters. These results can provide experimental guidance on how the variations in model inputs have affected the model outputs.

Glycoinformatics in the Artificial Intelligence Era.

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

Systems Biology and Multi‐Omics

Systems Biology and Multi‐Omics

Insights on Engineered Microbes in Sustainable Agriculture: Biotechnological Developments and Future Prospects.

Background Enhanced agricultural production is essential for increasing demand of the growing world population. At the same time, to combat the adverse effects caused by conventional agriculture practices to the environment along with the impact on human health and food security, a sustainable and healthy agricultural production needs to be practiced using beneficial microorganisms for enhanced yield. It is quite challenging because these microorganisms have rich biosynthetic repositories to produce biomolecules of interest; however, the intensive research in allied sectors and emerging genetic tools for improved microbial consortia are accepting new approaches that are helpful to farmers and agriculturists to meet the ever-increasing demand of sustainable food production. An important advancement is improved strain development via genetically engineered microbial systems (GEMS) as well as genetically modified microorganisms (GMOs) possessing known and upgraded functional characteristics to promote sustainable agriculture and food security. With the development of novel technologies such as DNA automated synthesis, sequencing and influential computational tools, molecular biology has entered the systems biology and synthetic biology era. More recently, CRISPR/Cas has been engineered to be an important tool in genetic engineering for various applications in the agri sector. The research in sustainable agriculture is progressing tremendously through GMOs/GEMS for their potential use in biofertilizers and as biopesticides.Conclusion In this review, we discuss the beneficial effects of engineered microorganisms through integrated sustainable agriculture production practices to improve the soil microbial health in order to increase crop productivity.

Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information.

Structural details of protein interactions are invaluable to the understanding of cellular processes. However, the identification of interactions at atomic resolution is a continuing challenge in the systems biology era. Although the number of structurally resolved complexes in the Protein Databank increases exponentially, the complexes only cover a small portion of the known structural interactome. In this chapter, we review the PRISM system that is a protein-protein interaction (PPI) prediction tool-its rationale, principles, and applications. We further discuss its extensions to discover the effect of single residue mutations, to model large protein assemblies, to improve its performance by exploiting conformational protein ensembles, and to reconstruct large PPI networks or pathway maps.

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare.

In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare.

Recent Progress on Systems and Synthetic Biology Approaches to Engineer Fungi As Microbial Cell Factories.

Filamentous fungi are remarkable organisms naturally specialized in deconstructing plant biomass and this feature has a tremendous potential for biofuel production from renewable sources. The past decades have been marked by a remarkable progress in the genetic engineering of fungi to generate industry-compatible strains needed for some biotech applications. In this sense, progress in this field has been marked by the utilization of high-throughput techniques to gain deep understanding of the molecular machinery controlling the physiology of these organisms, starting thus the Systems Biology era of fungi. Additionally, genetic engineering has been extensively applied to modify wellcharacterized promoters in order to construct new expression systems with enhanced performance under the conditions of interest. In this review, we discuss some aspects related to significant progress in the understating and engineering of fungi for biotechnological applications, with special focus on the construction of synthetic promoters and circuits in organisms relevant for industry. Different engineering approaches are shown, and their potential and limitations for the construction of complex synthetic circuits in these organisms are examined. Finally, we discuss the impact of engineered promoter architecture in the single-cell behavior of the system, an often-neglected relationship with a tremendous impact in the final performance of the process of interest. We expect to provide here some new directions to drive future research directed to the construction of high-performance, engineered fungal strains working as microbial cell factories.

CytoGEDEVO-global alignment of biological networks with Cytoscape.

In the systems biology era, high-throughput omics technologies have enabled the unraveling of the interplay of some biological entities on a large scale (e.g. genes, proteins, metabolites or RNAs). Huge biological networks have emerged, where nodes correspond to these entities and edges between them model their relations. Protein-protein interaction networks, for instance, show the physical interactions of proteins in an organism. The comparison of such networks promises additional insights into protein and cell function as well as knowledge-transfer across species. Several computational approaches have been developed previously to solve the network alignment (NA) problem, but only a few concentrate on the usability of the implemented tools for the evaluation of protein-protein interactions by the end users (biologists and medical researchers). We have created CytoGEDEVO, a Cytoscape app for visual and user-assisted NA. It extends the previous GEDEVO methodology for global pairwise NAs with new graphical and functional features. Our main focus was on the usability, even by non-programmers and the interpretability of the NA results with Cytoscape. CytoGEDEVO is publicly available from the Cytoscape app store at http://apps.cytoscape.org/apps/cytogedevo In addition, we provide stand-alone command line executables, source code, documentation and step-by-step user instructions at http://cytogedevo.compbio.sdu.dk malek@tugraz.at Supplementary data are available at Bioinformatics online.

CRISPR/Cas9‐mediated genome engineering of CHO cell factories: Application and perspectives

Chinese hamster ovary (CHO) cells are the most widely used production host for therapeutic proteins. With the recent emergence of CHO genome sequences, CHO cell line engineering has taken on a new aspect through targeted genome editing. The bacterial clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system enables rapid, easy and efficient engineering of mammalian genomes. It has a wide range of applications from modification of individual genes to genome-wide screening or regulation of genes. Facile genome editing using CRISPR/Cas9 empowers researchers in the CHO community to elucidate the mechanistic basis behind high level production of proteins and product quality attributes of interest. In this review, we describe the basis of CRISPR/Cas9-mediated genome editing and its application for development of next generation CHO cell factories while highlighting both future perspectives and challenges. As one of the main drivers for the CHO systems biology era, genome engineering with CRISPR/Cas9 will pave the way for rational design of CHO cell factories.

Uncovering distinct protein-network topologies in heterogeneous cell populations.

BackgroundCell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation.ResultsOur approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins.ConclusionsCoupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0170-2) contains supplementary material, which is available to authorized users.

Systems biology of polycystic kidney disease: a critical review.

The proliferation and diminishing costs of 'omics' approaches have finally opened the doors for small and medium laboratories to enter the 'systems biology era'. This is a welcome evolution that requires a new framework to design, interpret, and validate studies. Here, we highlight some of the challenges, contributions, and prospects of the 'cyst-ems biology' of polycystic kidney disease.

Statistical methods for mining Chinese hamster ovary cell ‘omics data: from differential expression to integrated multilevel analysis of the biological system

Publication of Chinese hamster ovary (CHO) cell line and Chinese hamster genomes is accelerating efforts to increase the efficiency of biopharmaceutical manufacturing through greater understanding of CHO cell biology. It is hoped that this knowledge will lead to more predictable bioprocesses through the identification of biomarkers for culture monitoring and engineering of the CHO cell itself. If we are to translate the potential of the CHO systems biology era to industrial practice, the extraction of knowledge from complex genomic, proteomic, transcriptomic and metabolomic datasets will be critical. In this manuscript, we review the methods utilized to analyze expression profiling data and highlight the role of advanced statistics as we generate larger scale datasets and move toward integrated multi-omic analyses of the biological system.

A detailed view on sulphur metabolism at the cellular and whole-plant level illustrates challenges in metabolite flux analyses.

Understanding the dynamics of physiological process in the systems biology era requires approaches at the genome, transcriptome, proteome, and metabolome levels. In this context, metabolite flux experiments have been used in mapping metabolite pathways and analysing metabolic control. In the present review, sulphur metabolism was taken to illustrate current challenges of metabolic flux analyses. At the cellular level, restrictions in metabolite flux analyses originate from incomplete knowledge of the compartmentation network of metabolic pathways. Transport of metabolites through membranes is usually not considered in flux experiments but may be involved in controlling the whole pathway. Hence, steady-state and snapshot readings need to be expanded to time-course studies in combination with compartment-specific metabolite analyses. Because of species-specific differences, differences between tissues, and stress-related responses, the quantitative significance of different sulphur sinks has to be elucidated; this requires the development of methods for whole-sulphur metabolome approaches. Different cell types can contribute to metabolite fluxes to different extents at the tissue and organ level. Cell type-specific analyses are needed to characterize these contributions. Based on such approaches, metabolite flux analyses can be expanded to the whole-plant level by considering long-distance transport and, thus, the interaction of roots and the shoot in metabolite fluxes. However, whole-plant studies need detailed empirical and mathematical modelling that have to be validated by experimental analyses.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.

Systems genetics: challenges and developing strategies

Systems genetics is a new discipline based on the transcription mapping, which is also called “genetical genomics”. In recent years, systems genetics has become more practical because of advances in science and technology. Analysis of expression quantitative trait loci (eQTLs) is an emerging technique in which individuals are genotyped across a panel of genetic markers and, simultaneously, phenotyped using DNA microarrays. Depending on eQTL mapping, one can infer the underlying regulatory network responsible for complex diseases or quantitative trait phenotypes. Systems genetics approaches integrate DNA sequence variation, variation in transcript abundance and other molecular phenotypes and variation in organismal phenotypes in a linkage or association mapping population, and allow us to interpret quantitative genetic variation in terms of biologically meaningful causal networks of correlated transcripts. These approaches have been made possible due to the development of massively parallel technologies for quantifying genome-wide levels of transcript abundance. The predictive power of the networks could be enhanced by more systematically integrating protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites. Systems genetics research will change the traditional approaches based on reductionism, and allows us to reconsider the living phenomenon and complex disease mechanism. Systems genetics benefits from varied “omics” researches (such as transcriptomics, metabolomics, and phenomics) and the development of bioinformatics tools and mathematical modeling, and will become mature in the near future like many other branches of genetics. Systems genetics is leading researchers to understand genetics systems from holism’s viewpoint, and will open a wide field of vision for genetics researchers in systems biology era.

Dynamics of lymphocyte membrane in Chernobyl clean-up workers with type 2 diabetes mellitus

Objective: Diabetes mellitus is one of the most crippling diseases that man has seen and its prevalence has risen dramatically over the past two decades. Currently there are over the 150 million diabetics worldwide and this number is likely to increase to 300 million or more by the year 2025. Diabetes mellitus increases the risk of many disorders including cardiovascular diseases. Understanding the molecular properties of diabetic progression is a big challenge in systems-biology era. Methods: The 3-aminobenzanthrone derivative ABM, developed at the Daugavpils University, Latvia, has been previously shown as a potential biomarker for determination of the immune state of patients with different pathologies. The aim of this study was to determine the several aspects of membrane alterations in the group of Chernobyl clean-up workers with diabetes mellitus in relation with its common group without diabetes mellitus and humans having no professional contact with radioactivity. The following parameters were examined: (1) the spectral characteristics of ABM in cell suspension (e.g. anisotropy index); (2) quantitative parameters of protein/lipid interaction in liposomes prepared from phosphatidylcholine and its mixtures with cardiolipin and cholesterol. Results: Screening of the individuals with diabetes mellitus 25-26 years after the work in Chernobyl revealed two groups of patients differing in structural and functional membrane properties, first of all on the lipid/protein interrelations and conformations of membrane proteins. The revealed structural modifications of membranes are dependent on radiation-induced factors. Concomitant diseases (diabetes mellitus, cardiovascular diseases) reinforce radiation induced effects. Conclusion: ABM is a sensitive probe of membrane architecture alterations, and can be used to elucidate the changes in membrane systems. Significant differences in membrane dynamics exist between control (donors), and diabetics and non-diabetics groups of Chernobyl clean-up workers

Boolean Modeling of Biochemical Networks

The use of modeling to observe and analyze the mechanisms of complex biochemical network function is be- coming an important methodological tool in the systems biology era. Number of different approaches to model these net- works have been utilized-- they range from analysis of static connection graphs to dynamical models based on kinetic in- teraction data. Dynamical models have a distinct appeal in that they make it possible to observe these networks in action, but they also pose a distinct challenge in that they require detailed information describing how the individual components of these networks interact in living cells. Because this level of detail is generally not known, dynamic modeling requires simplifying assumptions in order to make it practical. In this review Boolean modeling will be discussed, a modeling method that depends on the simplifying assumption that all elements of a network exist only in one of two states.

Classical Twin Design in Modern Pharmacogenomics Studies

Response to medication is highly variable, unpredictable and, at times, may be fatal. All drugs are more effective in certain groups of the population while showing no or minimal benefit in other groups. Although the current data on the subject are piecemeal, anecdotal evidence suggests that, in line with other common multifactorial traits, a myriad of genomic as well as environmental factors underpin population variability in drug response. Pharmacogenomics is the study of how variations in the human genome affect the variability in response to medication. Efforts to personalize treatment based on results from pharmacogenomics studies have the potential to increase efficacy, lower the overall cost of treatment, and decrease the incidence of adverse drug reactions, and are one of the major challenges of the modern era. The classical twin design has traditionally been used to assess the relative contribution of genetic and environmental factors to population variation in common, complex phenotypes, including drug response. Twins are not commonly regarded as providing the optimal design in genomic studies. However, we argue that, through their precise 'matching' for confounding variables (age, sex, cohort and common environmental effects), their amenability to numerous nonclassical study designs (genome-wide association studies or the role of epigenetic factors), and the availability of large, established registries worldwide, the twin model represents a flexible study design for systems-biology studies of drug response in humans. In this review, we describe the 'classical twin model' and its application in traditional pharmacogenetics studies, discuss the value of the twin design in the modern systems biology era, and highlight the potential of existing twin registries in formulating future strategies in pharmacogenomics research. We argue that the usefulness of this design goes beyond its traditional applications. Moreover, the flexibility of the model in concert with the amenability of large, established registries of twins worldwide to the collecting of new phenotypes will mean that the study of identical and nonidentical twins will play a considerable role in shaping our understanding of the important factors that underpin population variability in common, complex phenotypes, including response to medication.

Ultra-Structure database design methodology for managing systems biology data and analyses

BackgroundModern, high-throughput biological experiments generate copious, heterogeneous, interconnected data sets. Research is dynamic, with frequently changing protocols, techniques, instruments, and file formats. Because of these factors, systems designed to manage and integrate modern biological data sets often end up as large, unwieldy databases that become difficult to maintain or evolve. The novel rule-based approach of the Ultra-Structure design methodology presents a potential solution to this problem. By representing both data and processes as formal rules within a database, an Ultra-Structure system constitutes a flexible framework that enables users to explicitly store domain knowledge in both a machine- and human-readable form. End users themselves can change the system's capabilities without programmer intervention, simply by altering database contents; no computer code or schemas need be modified. This provides flexibility in adapting to change, and allows integration of disparate, heterogenous data sets within a small core set of database tables, facilitating joint analysis and visualization without becoming unwieldy. Here, we examine the application of Ultra-Structure to our ongoing research program for the integration of large proteomic and genomic data sets (proteogenomic mapping).ResultsWe transitioned our proteogenomic mapping information system from a traditional entity-relationship design to one based on Ultra-Structure. Our system integrates tandem mass spectrum data, genomic annotation sets, and spectrum/peptide mappings, all within a small, general framework implemented within a standard relational database system. General software procedures driven by user-modifiable rules can perform tasks such as logical deduction and location-based computations. The system is not tied specifically to proteogenomic research, but is rather designed to accommodate virtually any kind of biological research.ConclusionWe find Ultra-Structure offers substantial benefits for biological information systems, the largest being the integration of diverse information sources into a common framework. This facilitates systems biology research by integrating data from disparate high-throughput techniques. It also enables us to readily incorporate new data types, sources, and domain knowledge with no change to the database structure or associated computer code. Ultra-Structure may be a significant step towards solving the hard problem of data management and integration in the systems biology era.

ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists

BackgroundNew mathematical models of complex biological structures and computer simulation software allow modelers to simulate and analyze biochemical systems in silico and form mathematical predictions. Due to this potential predictive ability, the use of these models and software has the possibility to compliment laboratory investigations and help refine, or even develop, new hypotheses. However, the existing mathematical modeling techniques and simulation tools are often difficult to use by laboratory biologists without training in high-level mathematics, limiting their use to trained modelers.ResultsWe have developed a Boolean network-based simulation and analysis software tool, ChemChains, which combines the advantages of the parameter-free nature of logical models while providing the ability for users to interact with their models in a continuous manner, similar to the way laboratory biologists interact with laboratory data. ChemChains allows users to simulate models in an automatic fashion under tens of thousands of different external environments, as well as perform various mutational studies.ConclusionChemChains combines the advantages of logical and continuous modeling and provides a way for laboratory biologists to perform in silico experiments on mathematical models easily, a necessary component of laboratory research in the systems biology era.