A decade of systems biology.

Systems biology provides a framework for assembling models of biological systems from systematic measurements. Since the field was first introduced a decade ago, considerable progress has been made in technologies for global cell measurement and in computational analyses of these data to map and model cell function. It has also greatly expanded into the translational sciences, with approaches pioneered in yeast now being applied to elucidate human development and disease. Here, we review the state of the field with a focus on four emerging applications of systems biology that are likely to be of particular importance during the decade to follow: (a) pathway-based biomarkers, (b) global genetic interaction maps, (c) systems approaches to identify disease genes, and (d) stem cell systems biology. We also cover recent advances in software tools that allow biologists to explore system-wide models and to formulate new hypotheses. The applications and methods covered in this review provide a set of prime exemplars useful to cell and developmental biologists wishing to apply systems approaches to areas of interest.

Tellurium notebooks-An environment for reproducible dynamical modeling in systems biology.

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python-based Jupyter-like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in-line, human-readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards-compliant models and simulations, run the simulations in-line, and re-export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.

In situ detection of histone variants and modifications in mouse brain using imaging mass spectrometry.

Histone posttranslational modifications and histone variants control the epigenetic regulation of gene expression and affect a wide variety of biological processes. A complex pattern of such modifications and variants defines the identity of cells within complex organ systems and can therefore be used to characterize cells at a molecular level. However, their detection and identification in situ has been limited so far due to lack of specificity, selectivity, and availability of antihistone antibodies. Here, we describe a novel MALDI imaging MS based workflow, which enables us to detect and characterize histones by their intact mass and their correlation with cytological properties of the tissue using novel statistical and image analysis tools. The workflow allows us to characterize the in situ distribution of the major histone variants and their modification in the mouse brain. This new analysis tool is particularly useful for the investigation of expression patterns of the linker histone H1 variants for which suitable antibodies are so far not available.

Optogenetic characterization methods overcome key challenges in synthetic and systems biology.

Systems biologists aim to understand how organism-level processes, such as differentiation and multicellular development, are encoded in DNA. Conversely, synthetic biologists aim to program systems-level biological processes, such as engineered tissue growth, by writing artificial DNA sequences. To achieve their goals, these groups have adapted a hierarchical electrical engineering framework that can be applied in the forward direction to design complex biological systems or in the reverse direction to analyze evolved networks. Despite much progress, this framework has been limited by an inability to directly and dynamically characterize biological components in the varied contexts of living cells. Recently, two optogenetic methods for programming custom gene expression and protein localization signals have been developed and used to reveal fundamentally new information about biological components that respond to those signals. This basic dynamic characterization approach will be a major enabling technology in synthetic and systems biology.

Emerging proteomic technologies for elucidating context-dependent cellular signaling events: A big challenge of tiny proportions.

Aberrant cell signaling events either drive or compensate for nearly all pathologies. A thorough description and quantification of maladaptive signaling flux in disease is a critical step in drug development, and complex proteomic approaches can provide valuable mechanistic insights. Traditional proteomics-based signaling analyses rely heavily on in vitro cellular monoculture. The characterization of these simplified systems generates a rich understanding of the basic components and complex interactions of many signaling networks, but they cannot capture the full complexity of the microenvironments in which pathologies are ultimately made manifest. Unfortunately, techniques that can directly interrogate signaling in situ often yield mass-limited starting materials that are incompatible with traditional proteomics workflows. This review provides an overview of established and emerging techniques that are applicable to context-dependent proteomics. Analytical approaches are illustrated through recent proteomics-based studies in which selective sample acquisition strategies preserve context-dependent information, and where the challenge of minimal starting material is met by optimized sensitivity and coverage. This review is organized into three major technological themes: (i) LC methods in line with MS; (ii) antibody-based approaches; (iii) MS imaging with a discussion of data integration and systems modeling. Finally, we conclude with future perspectives and implications of context-dependent proteomics.

Single-cell systems biology by super-resolution imaging and combinatorial labeling.

Fluorescence microscopy is a powerful quantitative tool for exploring regulatory networks in single cells. However, the number of molecular species that can be measured simultaneously is limited by the spectral overlap between fluorophores. Here we demonstrate a simple but general strategy to drastically increase the capacity for multiplex detection of molecules in single cells by using optical super-resolution microscopy (SRM) and combinatorial labeling. As a proof of principle, we labeled mRNAs with unique combinations of fluorophores using fluorescence in situ hybridization (FISH), and resolved the sequences and combinations of fluorophores with SRM. We measured mRNA levels of 32 genes simultaneously in single Saccharomyces cerevisiae cells. These experiments demonstrate that combinatorial labeling and super-resolution imaging of single cells is a natural approach to bring systems biology into single cells.

Overview.

Although broadly defined in the literature, for the purpose of this section, we define systems biology as the description of the dynamic genomic, proteomic, and metabolomic processes integrated into a functional model of the cell, organelle, or tissue that is capable of accurately tracking the biological system's response to environmental perturbations. The goal of this section is to complete the tripartite description of asthma systems biology, initiated by the previous section (Section II: Genetics and Genomics of Asthma), by reviewing the recent literature-the types and methods of sample collection, processing, analysis, and instrumentation-of metabolomic and proteomic investigations, including functional proteomic studies of the asthma innate immune response and glucocorticoid (GC) receptor signaling with reference to GC resistance in severe asthma.

Enabling systems biology approaches through microfabricated systems.

With the experimental tools and knowledge that have accrued from a long history of reductionist biology, we can now start to put the pieces together and begin to understand how biological systems function as an integrated whole. Here, we describe how microfabricated tools have demonstrated promise in addressing experimental challenges in throughput, resolution, and sensitivity to support systems-based approaches to biological understanding.

Quantitative microfluidic biomolecular analysis for systems biology and medicine.

In the postgenome era, biology and medicine are rapidly evolving towards quantitative and systems studies of complex biological systems. Emerging breakthroughs in microfluidic technologies and innovative applications are transforming systems biology by offering new capabilities to address the challenges in many areas, such as single-cell genomics, gene regulation networks, and pathology. In this review, we focus on recent progress in microfluidic technology from the perspective of its applications to promoting quantitative and systems biomolecular analysis in biology and medicine.

Systems biology of innate immunity.

Systems biology is the comprehensive and quantitative analysis of the interactions between all of the components of biological systems over time. Systems biology involves an iterative cycle, in which emerging biological problems drive the development of new technologies and computational tools. These technologies and tools then open new frontiers that revolutionize biology. Innate immunity is well suited for systems analysis, because the relevant cells can be isolated in various functional states and their interactions can be reconstituted in a biologically meaningful manner. Application of the tools of systems biology to the innate immune system will enable comprehensive analysis of the complex interactions that maintain the difficult balance between host defense and inflammatory disease. In this review, we discuss innate immunity in the context of the systems biology concepts, emergence, robustness, and modularity, and we describe emerging technologies we are applying in our systems-level analyses. These technologies include genomics, proteomics, computational analysis, forward genetics screens, and analyses that link human genetic polymorphisms to disease resistance.

RuleBender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry.

BACKGROUND: Rule-based modeling (RBM) is a powerful and increasingly popular approach to modeling cell signaling networks. However, novel visual tools are needed in order to make RBM accessible to a broad range of users, to make specification of models less error prone, and to improve workflows. RESULTS: We introduce RuleBender, a novel visualization system for the integrated visualization, modeling and simulation of rule-based intracellular biochemistry. We present the user requirements, visual paradigms, algorithms and design decisions behind RuleBender, with emphasis on visual global/local model exploration and integrated execution of simulations. The support of RBM creation, debugging, and interactive visualization expedites the RBM learning process and reduces model construction time; while built-in model simulation and results with multiple linked views streamline the execution and analysis of newly created models and generated networks. CONCLUSION: RuleBender has been adopted as both an educational and a research tool and is available as a free open source tool at http://www.rulebender.org. A development cycle that includes close interaction with expert users allows RuleBender to better serve the needs of the systems biology community.

GPU computing for systems biology.

The development of detailed, coherent, models of complex biological systems is recognized as a key requirement for integrating the increasing amount of experimental data. In addition, in-silico simulation of bio-chemical models provides an easy way to test different experimental conditions, helping in the discovery of the dynamics that regulate biological systems. However, the computational power required by these simulations often exceeds that available on common desktop computers and thus expensive high performance computing solutions are required. An emerging alternative is represented by general-purpose scientific computing on graphics processing units (GPGPU), which offers the power of a small computer cluster at a cost of approximately $400. Computing with a GPU requires the development of specific algorithms, since the programming paradigm substantially differs from traditional CPU-based computing. In this paper, we review some recent efforts in exploiting the processing power of GPUs for the simulation of biological systems.

Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega.

Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.

Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies.

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive "global" metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography-MS (LC-MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS-based metabolomics with a particular focus on LC-MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC-MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures.

Optimal control of gene mutation in DNA replication.

We propose a molecular-level control system view of the gene mutations in DNA replication from the finite field concept. By treating DNA sequences as state variables, chemical mutagens and radiation as control inputs, one cell cycle as a step increment, and the measurements of the resulting DNA sequence as outputs, we derive system equations for both deterministic and stochastic discrete-time, finite-state systems of different scales. Defining the cost function as a summation of the costs of applying mutagens and the off-trajectory penalty, we solve the deterministic and stochastic optimal control problems by dynamic programming algorithm. In addition, given that the system is completely controllable, we find that the global optimum of both base-to-base and codon-to-codon deterministic mutations can always be achieved within a finite number of steps.

Setting Up an Automated Biomanufacturing Laboratory.

Laboratory automation is a key enabling technology for genetic engineering that can lead to higher throughput, more efficient and accurate experiments, better data management and analysis, decrease in the DBT (Design, Build, and Test) cycle turnaround, increase of reproducibility, and savings in lab resources. Choosing the correct framework among so many options available in terms of software, hardware, and skills needed to operate them is crucial for the success of any automation project. This chapter explores the multiple aspects to be considered for the solid development of a biofoundry project including available software and hardware tools, resources, strategies, partnerships, and collaborations in the field needed to speed up the translation of research results to solve important society problems.

The analysis of living systems can generate both knowledge and illusions.

Life relies on phenomena that range from changes in molecules that occur within nanoseconds to changes in populations that occur over millions of years. Researchers have developed a vast range of experimental techniques to analyze living systems, but a given technique usually only works over a limited range of length or time scales. Therefore, gaining a full understanding of a living system usually requires the integration of information obtained at multiple different scales by two or more techniques. This approach has undoubtedly led to a much better understanding of living systems but, equally, the staggering complexity of these systems, the sophistication and limitations of the techniques available in modern biology, and the need to use two or more techniques, can lead to persistent illusions of knowledge. Here, in an effort to make better use of the experimental techniques we have at our disposal, I propose a broad classification of techniques into six complementary approaches: perturbation, visualization, substitution, characterization, reconstitution, and simulation. Such a taxonomy might also help increase the reproducibility of inferences and improve peer review.

QuantStudio™ 12K Flex OpenArray® System as a Tool for High-Throughput Genotyping and Gene Expression Analysis.

Real time technology provides great advancements over PCR-based methods for a broad range of applications. With the increased availability of sequencing information, there is a need for the development and application of high-throughput real time PCR genotyping and gene expression methods that significantly broaden the current screening capabilities. Thermo Fisher Scientific (USA) has released a platform (QuantStudio™ 12K Flex system coupled with OpenArray® technology) with key elements required for high-throughput SNP genotyping and gene expression analysis. This allows for a rapid screening of large numbers of TaqMan® assays (up to 256) in many samples (up to 480) per run. This advanced real-time method involves the use of an array composed of 3,000 through-holes running on the QuantStudio™ 12K with OpenArray® block. The aim of this chapter is to outline the OpenArray® approach while providing a comprehensive in-depth review of the scientific literature on this topic. In agreement with a large number of independent studies, we conclude that the use of OpenArray® technology is a rapid and accurate method for high-throughput and large-scale systems biology studies with high specificity and sensitivity.

Unsupervised GRN Ensemble.

Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this chapter, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because they are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.