Systems biology applied to the study of papaya fruit ripening: the influence of ethylene / Biologia de sistemas aplicada ao estudo do amadurecimento de mamões: a influência do etileno

Fruit ripening is a biochemical process that results in flavor, odor, texture, and color suitable for human consumption, in addition to providing access to important nutrients. Although ripening promotes sensory and nutritional increases in fruits, there is also an increased susceptibility to physical damage, as is the case with papaya. These transformations occur due to changes in gene expression patterns at different stages of maturity, whose control and coordination result from the combined action of plant hormones, especially ethylene. As the action of this hormone in the regulation of gene expression is still elusive, this dissertation sought to address the global analysis of the transcriptome in an overview study of molecular processes involved in the ripening of ethylene-treated and non-treated papaya. Transcription factors related to ethylene synthesis and signaling had increased activity towards exogenous-ethylene treatment. Consequently, ethylene-induced enzymes had their coding genes differentially expressed, like genes related to the synthesis of carotenoids, linalool, and vitamins, which increase color, aroma, and antioxidant activity, respectively. Metabolic pathways related to the synthesis of sugars were suppressed while genes encoding the enzyme responsible for sucrose synthesis maintained a basal expression, showing that the accumulation of sugars occurs before the ripening process. The firmness of the peel and pulp of the fruits were strongly influenced by the treatment with ethylene and by the time of the experiment, suffering the action of numerous enzymes related to the degradation of the cell wall. The main enzyme responsible for softening the pulp was polygalacturonase, together with the activity of other pectinases and cellulases. In contrast to the need for the pre-climacteric action of pectate lyase and pectinesterase reported in other fleshy fruits, such as tomatoes and strawberries, papaya did not show a significant difference in their expression. The meta-analysis of several papaya ripening transcriptomes confirmed the expression profile observed in the previous RNA-seq, besides providing statistical enrichment to the biological narratives. Finally, the present study gathered a range of robust information on the gene regulation of the papaya ripening process, which opens possibilities for future approaches to transcriptomic analysis and validates the use of papaya as a model for such studies

O amadurecimento de frutos é um processo bioquímico que resulta em sabor, odor, textura e cor adequados para o consumo humano, além de propiciar o acesso a nutrientes importantes. Apesar do amadurecimento promover incrementos sensoriais e nutricionais nos frutos, ocorre também um aumento da suscetibilidade a danos físicos, como é o caso do mamão. Essas transformações ocorrem devido às alterações nos padrões de expressão gênica nos diferentes estádios de amadurecimento, cujo controle e coordenação decorrem da ação combinada de hormônios vegetais, principalmente do etileno. Como a ação deste hormônio na regulação da expressão gênica ainda é elusiva, a presente dissertação buscou abordar a análise global do transcriptoma em um amplo estudo dos processos moleculares envolvidos no amadurecimento de mamões tratados e não tratados com etileno. Os fatores de transcrição relacionados com a síntese e a sinalização do etileno tiveram sua atividade aumentada perante o tratamento exógeno com etileno. Consequentemente, as enzimas reguladas por esse hormônio tiveram seus genes de codificação expressos diferencialmente, como foi o caso de genes relacionados à síntese de carotenoides, linalool e vitaminas, que atuam no aumento da cor, aroma e atividade antioxidante, respectivamente. Vias metabólicas relacionadas com à síntese de açúcares foram reprimidas enquanto genes codificantes da enzima responsável pela síntese de sacarose mantiveram uma expressão basal, evidenciando que o acúmulo de açúcares ocorre antes do processo de amadurecimento. A firmeza da casca e da polpa dos frutos foram fortemente influenciadas pelo tratamento com etileno e pelo tempo de experimento, sofrendo ação de inúmeras enzimas relacionadas com a degradação da parede celular. A principal enzima responsável pelo amolecimento da polpa foi a poligalacturonase, em conjunto com a atividade de outras pectinases e celulases. Em contraste com a necessidade da ação pré-climatérica da pectato liase e da pectinesterase relatada em outras frutas carnosas, como tomates e morangos, o mamão não apresentou uma diferença significativa na expressão das mesmas. A meta-análise de diversos transcriptomas do amadurecimento do mamão reafirmaram o perfil de expressão observado no RNA-seq, além de prover enriquecimento estatístico às narrativas biológicas. Por fim, o presente estudo reuniu uma gama de informações robustas sobre a regulação gênica do processo de amadurecimento do mamão papaia, o que abrange a possibilidade para futuras abordagens de análise transcriptomica e valida o uso do mamão como modelo para tais estudos

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.

P4 Medicine or O4 Medicine? Hippocrates Provides the Answer.

BACKGROUND: The term P4 medicine (predictive, preventative, personalized, participatory) was coined by Dr. Leroy Hood of the Institute for Systems Biology to demonstrate his framework to detect and prevent disease through extensive biomarker testing, close monitoring, deep statistical analysis, and patient health coaching. METHODS: In 2017, this group published the results of their "100 Person Wellness Project." They performed whole genome sequencing and 218 clinical laboratory tests, measured 643 metabolites and 262 proteins, quantified 4616 operational taxonomic units in the microbiome, and monitored exercise in 108 participants for 9 months. The study was also interventional, as members were paired with a coach who gave lifestyle and supplement counseling to improve biomarker levels between each sampling period. RESULTS: Using this study as a basis, we here analyze the Hippocratic roots and the advantages and disadvantages of P4 medicine. We introduce O4 medicine (overtesting, overdiagnosis, overtreatment, overcharging) as a counterpoint to P4 medicine to highlight the drawbacks, including possible harms and cost. CONCLUSIONS: We hope this analysis will contribute to the discussion about the best use of limited health-care resources to produce maximum benefit for all patients.

Towards embedding Caco-2 model of gut interface in a microfluidic device to enable multi-organ models for systems biology.

BACKGROUND: A cancer cell line originating from human epithelial colorectal adenocarcinoma (Caco-2 cells) serves as a high capacity model for a preclinical screening of drugs. Recent need for incorporating barrier tissue into multi-organ chips calls for inclusion of Caco-2 cells into microperfused environment. RESULTS: This article describes a series of systems biology insights obtained from comparing Caco-2 models cells grown as conventional 2D layer and in a microfluidic chip. When basic electrical parameters of Caco-2 monolayers were evaluated using impedance spectrometry and MTT assays, no differences were noted. On the other hand, the microarray profiling of mRNAs and miRNAs revealed that grows on a microfluidic chip leads to the change in the production of specific miRNA, which regulate a set of genes for cell adhesion molecules (CAMs), and provide for more complete differentiation of Caco-2 monolayer. Moreover, the sets of miRNAs secreted at the apical surface of Caco-2 monolayers grown in conventional 2D culture and in microfluidic device differ. CONCLUSIONS: When integrated into a multi-tissue platform, Caco-2 cells may aid in generating insights into complex pathophysiological processes, not possible to dissect in conventional cultures.

Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data.

A challenging problem in systems biology is the reconstruction of gene regulatory networks from postgenomic data. A variety of reverse engineering methods from machine learning and computational statistics have been proposed in the literature. However, deciding on the best method to adopt for a particular application or data set might be a confusing task. The present chapter provides a broad overview of state-of-the-art methods with an emphasis on conceptual understanding rather than a deluge of mathematical details, and the pros and cons of the various approaches are discussed. Guidance on practical applications with pointers to publicly available software implementations are included. The chapter concludes with a comprehensive comparative benchmark study on simulated data and a real-work application taken from the current plant systems biology.

Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks.

Biological networks are a very convenient modeling and visualization tool to discover knowledge from modern high-throughput genomics and post-genomics data sets. Indeed, biological entities are not isolated but are components of complex multilevel systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.

Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.

Inference of gene regulatory networks (GRNs) from time series data is a well-established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly complementary: model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability. Since they are not based on any parametric model, they are more flexible than model-based methods, but also less interpretable.In this chapter, we describe Jump3, a hybrid approach that bridges the gap between model-free and model-based methods. Jump3 uses a formal stochastic differential equation to model each gene expression but reconstructs the GRN topology with a nonparametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step-by-step tutorial of the associated software usage.

Network Inference from Single-Cell Transcriptomic Data.

Recent technological breakthroughs in single-cell RNA sequencing are revolutionizing modern experimental design in biology. The increasing size of the single-cell expression data from which networks can be inferred allows identifying more complex, non-linear dependencies between genes. Moreover, the inter-cellular variability that is observed in single-cell expression data can be used to infer not only one global network representing all the cells, but also numerous regulatory networks that are more specific to certain conditions. By experimentally perturbing certain genes, the deconvolution of the true contribution of these genes can also be greatly facilitated. In this chapter, we will therefore tackle the advantages of single-cell transcriptomic data and show how new methods exploit this novel data type to enhance the inference of gene regulatory networks.

Inferring Gene Regulatory Networks from Multiple Datasets.

Gaussian process dynamical systems (GPDS) represent Bayesian nonparametric approaches to inference of nonlinear dynamical systems, and provide a principled framework for the learning of biological networks from multiple perturbed time series measurements of gene or protein expression. Such approaches are able to capture the full richness of complex ODE models, and can be scaled for inference in moderately large systems containing hundreds of genes. Related hierarchical approaches allow for inference from multiple datasets in which the underlying generative networks are assumed to have been rewired, either by context-dependent changes in network structure, evolutionary processes, or synthetic manipulation. These approaches can also be used to leverage experimentally determined network structures from one species into another where the network structure is unknown. Collectively, these methods provide a comprehensive and flexible platform for inference from a diverse range of data, with applications in systems and synthetic biology, as well as spatiotemporal modelling of embryo development. In this chapter we provide an overview of GPDS approaches and highlight their applications in the biological sciences, with accompanying tutorials available as a Jupyter notebook from https://github.com/cap76/GPDS .

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.

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.

Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes.

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.

Analysis of the transcriptional regulatory mechanisms mediated by microRNAs in Metabolic Syndrome / Análise dos mecanismos regulatórios transcricionais mediados por microRNAs na Síndrome Metabólica

Metabolic Syndrome (MetS) is a combination of diseases interrelated and associated with increased mortality and risk of cardiovascular events. Among the elucidated molecular mechanisms of MetS, there are several genes regulated by miRNAs - small non-coding RNAs. A large number of transcriptomic studies in public databases integrated with new analysis methods can generate new insights. Therefore, this study aimed to identify circulating miRNAs and their target genes in MetS using a Systems Biology approach. For this, we used GEO-NCBI to download and analyse 26 microarray transcriptome studies of MetS and obesity. After preprocessing, the data underwent differential expression (LIMMA method), gene co-expression (CEMiTool), and enrichment (GSEA, Reactome) analyses. We retrieved a gene expression signature for subcutaneous adipose tissue (SAT) for obese individuals that included 291 consistent differentially expressed genes (DEG). This signature had a positive normalized enrichment score (NES) for adaptive immune system activation responses, and negative NES for metabolic pathways. The consensus co-expression network of SAT revealed 3 communities (CM) of densely interconnected genes. These CMs had a high number of up regulated genes and a consistent positive NES among the studies. The co-expressed genes of these 3 CMs were related to neutrophil degranulation, infiltration of immune system cells, and inflammatory processes. Also, a small brazillian cohort (6 individuals with MetS and 6 controls) underwent a seric miRNA profiling using PCR array. From the 222 miRNAs detected in serum, the differential expression analysis identified 4 upregulated miRNAs (miR-30c-5p, miR-421, miR-542-5p and miR-574) in MetS patients (p<0.01). The integrative miRNAs-mRNAs analysis revealed that the circulating upregulated miRNAs had 12 targets in the SAT, 3 targets in the liver; and no targets in the muscle and blood. Many of these target genes are known modulators of proinflammatory pathways. In conclusion, the use of Systems Biology in the analysis of gene networks and circulating miRNAs identified some potential molecular and pathophysiological mechanisms of the Metabolic Syndrome. The circulating miRNAs identified in this study are potential biomarkers and/or therapeutic targets. However, further studies are needed to validate these miRNAs and their target mRNA

A Síndrome Metabólica (MetS) é um conjunto de doenças inter-relacionadas e associadas ao aumento de mortalidade e risco de eventos cardiovasculares. Entre os mecanismos moleculares elucidados da MetS, existem muitos genes regulados por miRNAs - RNAs pequenos não codificadores. O grande número de estudos transcriptômicos em banco dados públicos integrado a novos métodos de análise podem gerar novas descobertas. Deste modo, o objetivo deste estudo foi identificar miRNAs circulantes e genes alvos na MetS usando a abordagem de Biologia de Sistemas. Para isso, GEO-NCBI foi usado para obter e analisar 26 estudos de transcriptoma por microarray de MetS e obesidade. Após o pré-processamento, realizamos análises de expressão diferencial (método LIMMA), co-expressão gênica (CEMiTool), e enriquecimento (GSEA, Reactome). Identificamos uma assinatura de expressão gênica do tecido adiposo subcutâneo (SAT) de indivíduos obesos, composta por 291 genes consistentemente diferencialmente expressos (DEG). Essa assinatura teve um escore de enriquecimento normalizado (NES) positivo para ativação de respostas do sistema imune adaptativo, e NES negativo para vias de metabolismo. A rede consenso de co-expressão do SAT revelou 3 comunidades (CM) de genes densamente interconectadas. Essas CMs continham muitos genes regulados positivamente e com consistência de NES positivo entre os estudos. Os genes co-expressos dessas 3 comunidades pertenciam a vias de a degranulação de neutrófilos, infiltração de células do sistema imune e processos inflamatórios. Além disso, uma pequena coorte brasileira (6 indivíduos com MetS e 6 controles) foi submetida à dosagem sérica de miRNAs por PCR array. Dos 222 miRNAs detectados no soro, a análise de expressão diferencial identificou 4 miRNAs regulados positivamente (miR-30c-5p, miR-421, miR-542-5p e miR-574) nos pacientes com MetS (p<0.01). A análise integrativa miRNAs-mRNAs revelou que osmiRNAs circulantes superexpressos tinham 12 alvos no SAT, 3 alvos no fígado; e nenhum alvo no músculo e no sangue. Muitos desses alvos são moduladores de vias ró-inflamatórias. Em conclusão, a utilização da Biologia de Sistemas na análise de redes gênicas e miRNAs circulantes identificou alguns potenciais mecanismos moleculares e fisiopatológicos da Síndrome Metabólica. Os miRNAs circulantes identificados neste trabalho são potenciais biomarcadores e/ou alvos terapêuticos. Entretanto, mais estudos são necessários para validar esses miRNAs e seus mRNAs alvos

Computational Modeling of Mitochondrial Function from a Systems Biology Perspective.

The advent of "big data" in biology (e.g., genomics, proteomics, metabolomics), holding the promise to reveal the nature of the formidable complexity in cellular and organ makeup and function, has highlighted the compelling need for analytical and integrative computational methods to interpret and make sense of the patterns and changes in those complex networks. Computational models need to be built on sound physicochemical mechanistic principles in order to integrate, interpret, and simulate high-throughput experimental data. Energy transduction processes have been traditionally studied with thermodynamic, kinetic, or thermo-kinetic models, with the latter proving superior to understand the control and regulation of mitochondrial energy metabolism and its interactions with cytoplasmic and other cellular compartments. In this work, we survey the methods to be followed to build a computational model of mitochondrial energetics in isolation or integrated into a network of cellular processes. We describe the use of analytical tools such as elementary flux modes, linear optimization of metabolic models, and control analysis, to help refine our grasp of biologically meaningful behaviors and model reliability. The use of these tools should improve the design, building, and interpretation of steady-state behaviors of computational models while assessing validation criteria and paving the way to prediction.

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.

Fast and Precise Emulation of Stochastic Biochemical Reaction Networks With Amplified Thermal Noise in Silicon Chips.

The analysis and simulation of complex interacting biochemical reaction pathways in cells is important in all of systems biology and medicine. Yet, the dynamics of even a modest number of noisy or stochastic coupled biochemical reactions is extremely time consuming to simulate. In large part, this is because of the expensive cost of random number and Poisson process generation and the presence of stiff, coupled, nonlinear differential equations. Here, we demonstrate that we can amplify inherent thermal noise in chips to emulate randomness physically, thus alleviating these costs significantly. Concurrently, molecular flux in thermodynamic biochemical reactions maps to thermodynamic electronic current in a transistor such that stiff nonlinear biochemical differential equations are emulated exactly in compact, digitally programmable, highly parallel analog "cytomorphic" transistor circuits. For even small-scale systems involving just 80 stochastic reactions, our 0.35-µm BiCMOS chips yield a 311× speedup in the simulation time of Gillespie's stochastic algorithm over COPASI, a fast biochemical-reaction software simulator that is widely used in computational biology; they yield a 15 500× speedup over equivalent MATLAB stochastic simulations. The chip emulation results are consistent with these software simulations over a large range of signal-to-noise ratios. Most importantly, our physical emulation of Poisson chemical dynamics does not involve any inherently sequential processes and updates such that, unlike prior exact simulation approaches, they are parallelizable, asynchronous, and enable even more speedup for larger-size networks.

Systems morphodynamics: understanding the development of tissue hardware.

Systems morphodynamics describes a multi-level analysis of mechanical morphogenesis that draws on new microscopy and computational technologies and embraces a systems biology-informed scope. We present a selection of articles that illustrate and explain this rapidly progressing field.This article is part of the themed issue 'Systems morphodynamics: understanding the development of tissue hardware'.

Big Data access and infrastructure for modern biology: case studies in data repository utility.

Big Data is no longer solely the purview of big organizations with big resources. Today's routine tools and experimental methods can generate large slices of data. For example, high-throughput sequencing can quickly interrogate biological systems for the expression levels of thousands of different RNAs, examine epigenetic marks throughout the genome, and detect differences in the genomes of individuals. Multichannel electrophysiology platforms produce gigabytes of data in just a few minutes of recording. Imaging systems generate videos capturing biological behaviors over the course of days. Thus, any researcher now has access to a veritable wealth of data. However, the ability of any given researcher to utilize that data is limited by her/his own resources and skills for downloading, storing, and analyzing the data. In this paper, we examine the necessary resources required to engage Big Data, survey the state of modern data analysis pipelines, present a few data repository case studies, and touch on current institutions and programs supporting the work that relies on Big Data.