Systems Biology Projects Research Articles

A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models

Constraint-based metabolic models have been used for decades to predict the phenotype of microorganisms in different environments. However, quantitative predictions are limited unless labor-intensive measurements of media uptake fluxes are performed. We show how hybrid neural-mechanistic models can serve as an architecture for machine learning providing a way to improve phenotype predictions. We illustrate our hybrid models with growth rate predictions of Escherichia coli and Pseudomonas putida grown in different media and with phenotype predictions of gene knocked-out Escherichia coli mutants. Our neural-mechanistic models systematically outperform constraint-based models and require training set sizes orders of magnitude smaller than classical machine learning methods. Our hybrid approach opens a doorway to enhancing constraint-based modeling: instead of constraining mechanistic models with additional experimental measurements, our hybrid models grasp the power of machine learning while fulfilling mechanistic constrains, thus saving time and resources in typical systems biology or biological engineering projects.

Collaborative Systems Biology Projects for the Military Medical Community.

This pilot study was conducted to examine, for the first time, the ongoing systems biology research and development projects within the laboratories and centers of the U.S. Army Medical Research and Materiel Command (USAMRMC). The analysis has provided an understanding of the breadth of systems biology activities, resources, and collaborations across all USAMRMC subordinate laboratories. The Systems Biology Collaboration Center at USAMRMC issued a survey regarding systems biology research projects to the eight U.S.-based USAMRMC laboratories and centers in August 2016. This survey included a data call worksheet to gather self-identified project and programmatic information. The general topics focused on the investigators and their projects, on the project's research areas, on omics and other large data types being collected and stored, on the analytical or computational tools being used, and on identifying intramural (i.e., USAMRMC) and extramural collaborations. Among seven of the eight laboratories, 62 unique systems biology studies were funded and active during the final quarter of fiscal year 2016. Of 29 preselected medical Research Task Areas, 20 were associated with these studies, some of which were applicable to two or more Research Task Areas. Overall, studies were categorized among six general types of objectives: biological mechanisms of disease, risk of/susceptibility to injury or disease, innate mechanisms of healing, diagnostic and prognostic biomarkers, and host/patient responses to vaccines, and therapeutic strategies including host responses to therapies. We identified eight types of omics studies and four types of study subjects. Studies were categorized on a scale of increasing complexity from single study subject/single omics technology studies (23/62) to studies integrating results across two study subject types and two or more omics technologies (13/62). Investigators at seven USAMRMC laboratories had collaborations with systems biology experts from 18 extramural organizations and three other USAMRMC laboratories. Collaborators from six USAMRMC laboratories and 58 extramural organizations were identified who provided additional research expertise to these systems biology studies. At the end of fiscal year 2016, USAMRMC laboratories self-reported 66 systems biology/computational biology studies (62 of which were unique) with 25 intramural and 81 extramural collaborators. Nearly two-thirds were led by or in collaboration with the U.S. Army Telemedicine and Advanced Technology Research Center/Department of Defense Biotechnology High-Performance Computing Software Applications Institute and U.S. Army Center for Environmental Health Research. The most common study objective addressed biological mechanisms of disease. The most common types of Research Task Areas addressed infectious diseases (viral and bacterial) and chemical agents (environmental toxicant exposures, and traditional and emerging chemical threats). More than 40% of the studies (27/62) involved collaborations between the reporting USAMRMC laboratory and one other organization. Nearly half of the studies (30/62) involved collaborations between the reporting USAMRMC laboratory and at least two other organizations. These survey results indicate that USAMRMC laboratories are compliant with data-centric policy and guidance documents whose goals are to prevent redundancy and promote collaborations by sharing data and leveraging capabilities. These results also serve as a foundation to make recommendations for future systems biology research efforts.

Selected Reaction Monitoring to Measure Proteins of Interest in Complex Samples: A Practical Guide.

Biology and especially systems biology projects increasingly require the capability to detect and quantify specific sets of proteins across multiple samples, for example the components of a biological pathway through a set of perturbation-response experiments. Targeted proteomics based on selected reaction monitoring (SRM) has emerged as an ideal tool to this purpose, and complements the discovery capabilities of shotgun proteomics methods. SRM experiments rely on the development of specific, quantitative mass spectrometric assays for each protein of interest and their application to the quantification of the protein set in various biological samples. SRM measurements are multiplexed, namely, multiple proteins can be quantified simultaneously, and are characterized by a high reproducibility and a broad dynamic range. We provide here a practical guide to the development of SRM assays targeting a set of proteins of interest and to their application to complex biological samples.

Biggest challenges in bioinformatics

The third Heidelberg Unseminars in Bioinformatics (HUB) was held on 18th October 2012, at Heidelberg University, Germany. HUB brought together around 40 bioinformaticians from academia and industry to discuss the 'Biggest Challenges in Bioinformatics' in a 'World Café' style event.

Data management strategies for multinational large-scale systems biology projects

Good accessibility of publicly funded research data is essential to secure an open scientific system and eventually becomes mandatory [Wellcome Trust will Penalise Scientists Who Don’t Embrace Open Access. The Guardian 2012]. By the use of high-throughput methods in many research areas from physics to systems biology, large data collections are increasingly important as raw material for research. Here, we present strategies worked out by international and national institutions targeting open access to publicly funded research data via incentives or obligations to share data. Funding organizations such as the British Wellcome Trust therefore have developed data sharing policies and request commitment to data management and sharing in grant applications. Increased citation rates are a profound argument for sharing publication data. Pre-publication sharing might be rewarded by a data citation credit system via digital object identifiers (DOIs) which have initially been in use for data objects. Besides policies and incentives, good practice in data management is indispensable. However, appropriate systems for data management of large-scale projects for example in systems biology are hard to find. Here, we give an overview of a selection of open-source data management systems proved to be employed successfully in large-scale projects.

OpenBIS: a flexible framework for managing and analyzing complex data in biology research.

BackgroundModern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.ResultsWe have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies.ConclusionsopenBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.

WIBL: Workbench for Integrative Biological Learning.

The construction of integrated datasets from potentially hundreds of sources with bespoke formats, and their subsequent visualization and analysis, is a recurring challenge in systems biology. We present WIBL, a visualization and model development environment initially geared towards logic-based modelling of biological systems using integrated datasets. WIBL combines data integration, visualisation and modelling in a single portal-based workbench providing a comprehensive solution for interdisciplinary systems biology projects.

GPU.proton.DOCK: Genuine Protein Ultrafast proton equilibria consistent DOCKing

GPU.proton.DOCK (Genuine Protein Ultrafast proton equilibria consistent DOCKing) is a state of the art service for in silico prediction of protein–protein interactions via rigorous and ultrafast docking code. It is unique in providing stringent account of electrostatic interactions self-consistency and proton equilibria mutual effects of docking partners. GPU.proton.DOCK is the first server offering such a crucial supplement to protein docking algorithms—a step toward more reliable and high accuracy docking results. The code (especially the Fast Fourier Transform bottleneck and electrostatic fields computation) is parallelized to run on a GPU supercomputer. The high performance will be of use for large-scale structural bioinformatics and systems biology projects, thus bridging physics of the interactions with analysis of molecular networks. We propose workflows for exploring in silico charge mutagenesis effects. Special emphasis is given to the interface-intuitive and user-friendly. The input is comprised of the atomic coordinate files in PDB format. The advanced user is provided with a special input section for addition of non-polypeptide charges, extra ionogenic groups with intrinsic pKa values or fixed ions. The output is comprised of docked complexes in PDB format as well as interactive visualization in a molecular viewer. GPU.proton.DOCK server can be accessed at http://gpudock.orgchm.bas.bg/.

WIBL: Workbench for Integrative Biological Learning

Summary The construction of integrated datasets from potentially hundreds of sources with bespoke formats, and their subsequent visualization and analysis, is a recurring challenge in systems biology. We present WIBL, a visualization and model development environment initially geared towards logic-based modelling of biological systems using integrated datasets. WIBL combines data integration, visualisation and modelling in a single portalbased workbench providing a comprehensive solution for interdisciplinary systems biology projects.

Systems Biology: Evolving into the Mainstream

Systems approaches to biology are steadily widening their reach, but the road to integration and acceptance has been fraught with skepticism and technical hurdles. Interdisciplinary research teams at systems biology centers around the globe are working to win over the critics.

Development of a new method for absolute protein quantification on 2‐D gels

With the development of systems biology projects aimed at modeling the cell, accurate and absolute measurements of cellular protein concentrations are increasingly important. However, methods for absolute quantification at the proteomic level remain rare. Using the yeast Saccharomyces cerevisiae, we propose a new method based on the radioactive labeling with an (35)S compound and 2-D PAGE. The principle is simple: cells are grown for more than four generations in the presence of a unique sulfur source labeled at a defined specific radioactivity, ensuring that more than 90% of the proteins are labeled at the same specific radioactivity as the sulfur source. After separation of (35)S-labeled proteins on 2-D gels, each protein is counted. The amount of each protein present in the gel is then calculated, from which is deduced the amount of each protein per cell. The method, limited to soluble and abundant proteins visible on 2-D gels, is simple, precise and reproducible and does not require an internal standard. We use it to compare the amounts of proteins in two growth conditions: 100 microM sulfate or 500 microM methionine. Up to now, we only had transcriptional data on the expression of these proteins in both conditions.

Systems Biology and Physiome Projects

AbstractThis article is categorized under: Physiology > Mammalian Physiology in Health and Disease

SysBioMed report: Advancing systems biology for medical applications

The following report selects and summarises some of the conclusions and recommendations generated throughout a series of workshops and discussions that have lead to the publication of the Science Policy Briefing (SPB) Nr. 35, published by the European Science Foundation. (Large parts of the present text are directly based on the ESF SPB. Detailed recommendations with regard to specific application areas are not given here but can be found in the SPB. Issues related to mathematical modelling, including training and the need for an infrastructure supporting modelling are discussed in greater detail in the present text.)The numerous reports and publications about the advances within the rapidly growing field of systems biology have led to a plethora of alternative definitions for key concepts. Here, with 'mathematical modelling' the authors refer to the modelling and simulation of subcellular, cellular and macro-scale phenomena, using primarily methods from dynamical systems theory. The aim of such models is encoding and testing hypotheses about mechanisms underlying the functioning of cells. Typical examples are models for molecular networks, where the behaviour of cells is expressed in terms of quantitative changes in the levels of transcripts and gene products. Bioinformatics provides essential complementary tools, including procedures for pattern recognition, machine learning, statistical modelling (testing for differences, searching for associations and correlations) and secondary data extracted from databases.Dynamical systems theory is the natural language to investigate complex biological systems demonstrating nonlinear spatio-temporal behaviour. However, the generation of experimental data suitable to parameterise, calibrate and validate such models is often time consuming and expensive or not even possible with the technology available today. In our report, we use the term 'computational model' when mathematical models are complemented with information generated from bioinformatics resources. Hence, 'the model' is, in reality, an integrated collection of data and models from various (possibly heterogeneous) sources. The present report focuses on a selection of topics, which were identified as appropriate case studies for medical systems biology, and adopts a particular perspective which the authors consider important. We strongly believe that mathematical modelling represents a natural language with which to integrate data at various levels and, in doing so, to provide insight into complex diseases: 1. Modelling necessitates the statement of explicit hypotheses, a process which often enhances comprehension of the biological system and can uncover critical points where understanding is lacking. 2. Simulations can reveal hidden patterns and/or counter-intuitive mechanisms in complex systems. 3. Theoretical thinking and mathematical modelling constitute powerful tools to integrate and make sense of the biological and clinical information being generated and, more importantly, to generate new hypotheses that can then be tested in the laboratory.Medical Systems Biology projects carried out recently across Europe have revealed a need for action: 4. While the need for mathematical modelling and interdisciplinary collaborations is becoming widely recognised in the biological sciences, with substantial implications for the training and research funding mechanisms within this area, the medical sciences have yet to follow this lead. 5. To achieve major breakthroughs in Medical Systems Biology, existing academic funding schemes for large-scale projects need to be reconsidered. 6. The hesitant stance of the pharmaceutical industry towards major investment in systems biology research has to be addressed. 7. Leading medical journals should be encouraged to promote mathematical modelling.

SulfoSYS (Sulfolobus Systems Biology): towards a silicon cell model for the central carbohydrate metabolism of the archaeon Sulfolobus solfataricus under temperature variation

SulfoSYS (Sulfolobus Systems Biology) focuses on the study of the CCM (central carbohydrate metabolism) of Sulfolobus solfataricus and its regulation under temperature variation at the systems level. In Archaea, carbohydrates are metabolized by modifications of the classical pathways known from Bacteria or Eukarya, e.g. the unusual branched ED (Entner-Doudoroff) pathway, which is utilized for glucose degradation in S. solfataricus. This archaeal model organism of choice is a thermoacidophilic crenarchaeon that optimally grows at 80 degrees C (60-92 degrees C) and pH 2-4. In general, life at high temperature requires very efficient adaptation to temperature changes, which is most difficult to deal with for organisms, and it is unclear how biological networks can withstand and respond to such changes. This integrative project combines genomic, transcriptomic, proteomic and metabolomic, as well as kinetic and biochemical information. The final goal of SulfoSYS is the construction of a silicon cell model for this part of the living cell that will enable computation of the CCM network. In the present paper, we report on one of the first archaeal systems biology projects.

The Alpha Project: a model system for systems biology research

One goal of systems biology is to understand how genome-encoded parts interact to produce quantitative phenotypes. The Alpha Project is a medium-scale, interdisciplinary systems biology effort that aims to achieve this goal by understanding fundamental quantitative behaviours of a prototypic signal transduction pathway, the yeast pheromone response system from Saccharomyces cerevisiae. The Alpha Project distinguishes itself from many other systems biology projects by studying a tightly bounded and well-characterised system that is easily modified by genetic means, and by focusing on deep understanding of a discrete number of important and accessible quantitative behaviours. During the project, the authors have developed tools to measure the appropriate data and develop models at appropriate levels of detail to study a number of these quantitative behaviours. The authors have also developed transportable experimental tools and conceptual frameworks for understanding other signalling systems. In particular, the authors have begun to interpret system behaviours and their underlying molecular mechanisms through the lens of information transmission, a principal function of signalling systems. The Alpha Project demonstrates that interdisciplinary studies that identify key quantitative behaviours and measure important quantities, in the context of well-articulated abstractions of system function and appropriate analytical frameworks, can lead to deeper biological understanding. The authors' experience may provide a productive template for systems biology investigations of other cellular systems.

Validation and discovery from computational biology models

Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analysis software is reliable and that techniques exist for automating the analysis of the vast amounts of data which such simulation environments generate. A rigorous approach to the development of complex modelling software is needed. Such a framework is presented here together with techniques for the automated analysis of such models and a process for the automatic discovery of biological phenomena from large simulation data sets. Illustrations are taken from a major systems biology research project involving the in vitro investigation, modelling and simulation of epithelial tissue.

Systems biology for industrial strains and fermentation processes—Example: Amino acids

Systems biology is attracting significant interest finding applications not only in pharmaceutical development but also for basic studies on microbial systems. The latter often concentrate on the quantitative understanding of global regulation phenomena. So far, these activities are dominated by academic groups basically mirroring the necessity to prepare the sound scientific understanding first, before industrial applications can be derived later. However, this short-term view may not be sufficient because systems biology already offers numerous benefits for industrial applications, provided that special constraints are considered. This contribution indicates some of the constraints worth noticing when industrial systems biology projects are carried out. Consequently, differences in project structure and goals between purely academic and industrial systems biology projects are outlined.

BioMoby extensions to the Taverna workflow management and enactment software.

BackgroundAs biology becomes an increasingly computational science, it is critical that we develop software tools that support not only bioinformaticians, but also bench biologists in their exploration of the vast and complex data-sets that continue to build from international genomic, proteomic, and systems-biology projects. The BioMoby interoperability system was created with the goal of facilitating the movement of data from one Web-based resource to another to fulfill the requirements of non-expert bioinformaticians. In parallel with the development of BioMoby, the European myGrid project was designing Taverna, a bioinformatics workflow design and enactment tool. Here we describe the marriage of these two projects in the form of a Taverna plug-in that provides access to many of BioMoby's features through the Taverna interface.ResultsThe exposed BioMoby functionality aids in the design of "sensible" BioMoby workflows, aids in pipelining BioMoby and non-BioMoby-based resources, and ensures that end-users need only a minimal understanding of both BioMoby, and the Taverna interface itself. Users are guided through the construction of syntactically and semantically correct workflows through plug-in calls to the Moby Central registry. Moby Central provides a menu of only those BioMoby services capable of operating on the data-type(s) that exist at any given position in the workflow. Moreover, the plug-in automatically and correctly connects a selected service into the workflow such that users are not required to understand the nature of the inputs or outputs for any service, leaving them to focus on the biological meaning of the workflow they are constructing, rather than the technical details of how the services will interoperate.ConclusionWith the availability of the BioMoby plug-in to Taverna, we believe that BioMoby-based Web Services are now significantly more useful and accessible to bench scientists than are more traditional Web Services.