Big Biology Research Articles

Magneto-Biological Physics

Since the appearance of magneto-biology as an intriguing field of Big Biology, the most significant event, which was happened in it, was the discovery of huge isotope effects, when natural magnesium, calcium and zinc ions in the catalytic sites of enzymes were replaced with nuclear spin magnetic ions 25Mg2+, 43Ca2+, and 67Zn2+ of their stable isotopes. This discovery elucidated physical sense and the origin of numerous magneto-biological effects; it has illuminated all magneto-biology and introduced it as a respectable science. Now the spin concept and radical pair mechanism are generally accepted and overwhelmingly used to interpret experimentally detected magneto-biological effects. In terms of the radical pair mechanism the DNA repair, Hayflick limit and biological longevity under magnetic control are discussed. It is emphasized that magneto-biology is a means to better understand Big Biology itself.

Protein structures unravel the signatures and patterns of deep time evolution.

The formulation and testing of hypotheses using 'big biology data' often lie at the interface of computational biology and structural biology. The Protein Data Bank (PDB), which was established about 50years ago, catalogs three-dimensional (3D) shapes of organic macromolecules and showcases a structural view of biology. The comparative analysis of the structures of homologs, particularly of proteins, from different species has significantly improved the in-depth analyses of molecular and cell biological questions. In addition, computational tools that were developed to analyze the 'protein universe' are providing the means for efficient resolution of longstanding debates in cell and molecular evolution. In celebrating the golden jubilee of the PDB, much has been written about the transformative impact of PDB on a broad range of fields of scientific inquiry and how structural biology transformed the study of the fundamental processes of life. Yet, the transforming influence of PDB on one field of inquiry of fundamental interest-the reconstruction of the distant biological past-has gone almost unnoticed. Here, I discuss the recent advances to highlight how insights and tools of structural biology are bearing on the data required for the empirical resolution of vigorously debated and apparently contradicting hypotheses in evolutionary biology. Specifically, I show that evolutionary characters defined by protein structure are superior compared to conventional sequence characters for reliable, data-driven resolution of competing hypotheses about the origins of the major clades of life and evolutionary relationship among those clades. Since the better quality data unequivocally support two primary domains of life, it is imperative that the primary classification of life be revised accordingly.

Computation Methods in the Era of Big Biology Data: Opportunities and Obstacles

Computation Methods in the Era of Big Biology Data: Opportunities and Obstacles

Small molecule. Big biology. Dual phosphatase inhibitor enters the immunotherapy fray.

The advent and clinical success of immune checkpoint inhibitors Ipilimumab, Nivolumab and Pembrolizumab has had a seismic impact on our drug discovery focus and rationale. Novel extrinsic targets that enhance immune responses to cancer are actively being pursued, while tumor intrinsic targets that render cancer cells more sensitive to the immune system have joined traditional intrinsic targets (e.g. directly cytotoxic) in the drug discovery pipeline. The phosphatase PTPN2 (TC-PTP) and its paralog PTPN1 (PTP-1B) are negative regulators of several cytokine signaling pathways and Tcell receptor (TCR) signaling. In a recent publication, Baumgartner etal. demonstrate the pre-clinical efficacy of a first-in-class dual PTPN1/N2 active site inhibitor (ABBV-CLS-484/AC484) in cancer models.

KibioR & Kibio: a new architecture for next-generation data querying and sharing in big biology.

The growing production of massive heterogeneous biological data offers opportunities for new discoveries. However, performing multi-omics data analysis is challenging, and researchers are forced to handle the ever-increasing complexity of both data management and evolution of our biological understanding. Substantial efforts have been made to unify biological datasets into integrated systems. Unfortunately, they are not easily scalable, deployable and searchable, locally or globally. This publication presents two tools with a simple structure that can help any data provider, organization or researcher, requiring a reliable data search and analysis base. The first tool isKibio, a scalable and adaptable data storage based on Elasticsearch search engine. The second tool isKibioR, a R package to pull, push and searchKibiodatasets or any accessible Elasticsearch-based databases. These tools apply a uniform data exchange model and minimize the burden of data management by organizing data into a decentralized, versatile, searchable and shareable structure. Several case studies are presented using multiple databases, from drug characterization to miRNAs and pathways identification, emphasizing the ease of use and versatility of the Kibio/KibioR framework. Both KibioR and Elasticsearch are open source. KibioR package source is available at https://github.com/regisoc/kibior and the library on CRAN at https://cran.r-project.org/package=kibior. Supplementary data are available at Bioinformatics online.

When more data means better results: Abundance and scarcity in research collaborations in epigenetics.

Drawing upon ethnographic findings from an epigenetics research laboratory in the United Kingdom, this paper explores practices of research collaborations in the field of epigenetics, and epigenomics research consortia in particular. I demonstrate that research consortia are key scientific infrastructures that enable the aggregation of masses of data deemed necessary for the production of results and the fostering of epistemic value. Building on STS scholarship on value production, and the concept of asset, I show that the production of valuable research within epigenomics research consortia rests on the active organisation and management of abundance and scarcity. It involves shaping and standardising the masses of data gathered in consortia, while it also entails research teams enclosing their data within their laboratories' walls. As they do so, research teams construct data into scarce and monopolised assets, which they can put to productive use in collaborative endeavours against a revenue. In addition to contributing empirical and critical insights into the ways epigenetics knowledge is formed and negotiated in specific research contexts, this paper offers conceptual tools to examine and problematise knowledge production practices in data-intensive research more broadly. In particular, it points out that while contemporary big biology is marked by the generalised imperative to 'share' data and 'open' science, collaborative endeavours within research consortia are built around forms of exclusions.

Abstract 672: Establishment of a drug-tumor interaction database using Lantern Pharma’s Response Algorithm for Drug Positioning and Rescue (RADRTM)

Abstract The Response Algorithm for Drug positioning and Rescue (RADRTM) technology is Lantern Pharma's proprietary Artificial Intelligence (Al)-based machine learning approach for biomarker panel identification and patient stratification. RADRTM is a combination of three automated modules working sequentially to generate drug- and tumor-specific gene signatures predictive of response. RADRTM integrates biological knowledge, data-driven feature selection, and robust Al algorithms to facilitate hypothesis-free, drug- and cancer-specific biomarker development. RADRTM uses transcriptomic, drug sensitivity datasets and systems biology inputs and generates gene expression-based responder/non-responder profiles for specific tumor indications with high accuracy. RADRTM uses a unique process flow and a combination of machine learning algorithms to extract drug-specific biomarkers from whole transcriptome level input (~18000 genes). RADRTM comprises three main modules: data pre-processing, feature selection, and response prediction. Data pre-processing includes data cleaning, transformation and normalization. For dimensionality reduction and feature selection, RADRTM performs gene filtering via biological and statistical feature selection methods. In the final component, an AI-driven program reduces the intermediate number (approximately 500) of genes to a more manageable number (10 - 50) of candidate biomarkers capable of predicting drug sensitivity or insensitivity. Lantern’s RADRTM AI application incorporates automated supervised machine learning strategies along with big data analytics, statistics and systems biology to enable identification of new correlations of genetic biomarkers with drug activity. Using RADRTM we have created a database of drug response prediction models for more than 120 drug-tumor type combinations in a preclinical setting that is expected to keep growing. These drug- and cancer-specific RADRTMmodels have further enabled the classification of clinical records into distinct response groups, as well as generated gene expression signatures as features predictive of response. The average response prediction accuracy lies above 80%. We also demonstrate the utility of such a database in clinical translation through various performance metrics including but not limited to true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC). This database links the majority of FDA approved and selected investigational drugs with appropriate cancer indications and the associated RADRTM-derived responder/non-responder profiles in terms of gene expression signatures. This database could directly inform the drug-companion diagnostic co-developmental pathways for new drugs and cancer indications. Citation Format: Umesh Kathad, Yuvanesh Vedaraju, Aditya Kulkarni, Barry Henderson, Gregory Tobin, Panna Sharma, Arun Asaithambi. Establishment of a drug-tumor interaction database using Lantern Pharma’s Response Algorithm for Drug Positioning and Rescue (RADRTM) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 672.

\u201cA good collaboration is based on unique contributions from each side\u201d: assessing the dynamics of collaboration in stem cell science

The rise of ‘big biology’ is bringing academic and industrial scientists together in large consortia to address translational challenges in the life sciences. In order to assess the impact of this change, this paper examines the existing norms and styles of collaboration in one high profile translational domain; stem cell research. Data is drawn from qualitative interviews with academic and industry scientists working in a large European stem cell research project. Respondents discussed what they perceived as the main benefits and risks of collaborative research, what styles of collaboration they were familiar with, and what collaborative work in stem cell science normally involves. A wide range of materials, data, and expertise can be exchanged during collaborative work. Informal collaborations are governed by an ethos of reciprocity and mediated by trust while formal project agreements can provide a safe space for sharing between unfamiliar partners. These characteristics make stem cell research well suited to pre-competitive public-private ventures but translation of new products to market may be more challenging.

Big biology projects warm up to preprints

Consortium backed by US National Institutes of Health is first major biology programme to mandate online publication of results ahead of peer review.

The rise of developmental genetics - a historical account of the fusion of embryology and cell biology with human genetics and the emergence of the Stem Cell Initiative.

Genetics and cell biology are very prominent areas of biological research with rapid advances being driven by a flood of theoretical, technological and informational knowledge. Big biology and small biology continue to feed off each other. In this paper, we provide a brief overview of the productive interactions that have taken place between human geneticists and cell biologists at UCT, and credit is given to the enabling environment created led by Prof. Peter Beighton. The growth of new disciplines and disciplinary mergers that have swept away division of the past to make new exciting syntheses are discussed. We show how our joint research has benefitted from worldwide advances in developmental genetics, cloning and stem cell technologies, genomics, bioinformatics and imaging. We conclude by describing the role of the UCT Stem Cell Initiative and show how we are using induced pluripotent cells to carry out disease-in-the- dish studies on retinal degeneration and fibrosis.

High-throughput «Omics» technologies: New tools for the study of triple-negative breast cancer

Triple negative breast cancer (TNBC) represents about 15% to 20% of all breast cancers and is typically associated with poorer outcome than other breast cancer subtypes. The heterogeneity of this breast cancer subtype and present lack of clinically established targeted therapies further complicates treatment of patients. The treatment of TNBC emphasizes enhancing health care and developing personalized medicine. To respond to this need, the researchers have turned their attention to a different approach to scientific enquiry: the era of “big biology” and the integrative study of biological systems, also called “Omics” technologies. The term omics comprises different fields of molecular studies and characterizes a global view on biological molecules such as DNA, RNA, proteins, and metabolites. Combined “omics” approach offers a major tool for the understanding of a challenging cancer model, TNBC. This review discusses the different discoveries made using omics technologies concerning the molecular mechanisms underlying TNBC phenotypic heterogeneity, and their potential transfer to clinical applications.

Abstract A75: Big data and computational biology method for personalized prognosis of high-grade serous ovarian carcinoma.

Abstract The field of personalized disease prognosis, risk prediction and disease management has always attracted the attention of scientists and clinicians worldwide. Translating current clinical research and emerging genome-scale molecular biomarkers of ovarian and other cancers is yielding promising new insights into predictive signatures and potential clinical targets, moving treatment in favor of more personalized therapeutic approaches. Integrative genomics, big data analysis and the disease prediction modelling have become the major driving factors in understanding how to reproducibly stop a disease for individual patients - a goal of precision medicine. In clinical practice, prognostic and predictive factors could be measured as continuous or discrete variables. Here, we proposed a novel methodology to predict disease/treatment outcome via analysis of the similarities between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). High-grade serous ovarian carcinoma (HGSC) is the most prevalent ovarian cancer, and is one of the most lethal gynecological diseases in the world today. Many molecular signatures for prognostic and disease prediction of HGSC have been proposed, but no been implemented in clinical practice yet. In our previous study, the highly-confidence prognostic classifier, comprising 36 mRNAs that can stratify the HGSC patients into low, intermediate or high-risk subgroups with significantly distinct overall survival outcomes and sensitivity to post-surgery chemotherapy, has been proposed [1]. In this work, we used this classifier as the discriminative model of prognosis of HGSC patients. We implemented our methods to personalized prognosis of HGSC patients of TCGA database based on our molecular predictor [1]. Our results revealed that patient's age, when converted into discrete binary values, was positively correlated with the overall risk of succumbing to the disease and can be used as independent prognostic factor. The inclusion of age into the molecular survival predictor provided more robust personalized prognosis of overall survival when applied to an independent testing metadata composing 359 HGSC patients. In summary, we propose that our personalized prognosis methods and results after including age as prognostic factor could provide clinical benefits in the context of personalized prognosis and therapeutic assignment of HGSC patients. Our method can be generalized and implemented in many other diseases to accurately predict personalized patients' outcomes. [1]. Tang Z, Ow GS, Thiery JP, Ivshina AV and Kuznetsov VA. Int J Cancer. 2013; 134(2):306-318. Citation Format: Ghim Siong Ow, Zhiqun Tang, Anna Vladimirovna Ivshina, Vladimir Andreevich Kuznetsov. Big data and computational biology method for personalized prognosis of high-grade serous ovarian carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr A75.

Constructing failure in big biology: The socio-technical anatomy of Japan's Protein 3000 Project.

This study focuses on the 5-year Protein 3000 Project launched in 2002, the largest biological project in Japan. The project aimed to overcome Japan's alleged failure to contribute fully to the Human Genome Project, by determining 3000 protein structures, 30 percent of the global target. Despite its achievement of this goal, the project was fiercely criticized in various sectors of society and was often branded an awkward failure. This article tries to solve the mystery of why such failure discourse was prevalent. Three explanatory factors are offered: first, because some goals were excluded during project development, there was a dynamic of failed expectations; second, structural genomics, while promoting collaboration with the international community, became an 'anti-boundary object', only the absence of which bound heterogeneous domestic actors; third, there developed an urgent sense of international competition in order to obtain patents on such structural information.

Human Genome Project: Twenty-five years of big biology.

The Human Genome Project, which launched a quarter of a century ago this week, still holds lessons for the consortium-based science it ushered in, say Eric D. Green, James D. Watson and Francis S. Collins.

Helping Protists to Find Their Place in a Big Data World

The 'big new biology' is a vision of a discipline transformed by a commitment to sharing data and with investigative practices that call on very large open pools of freely accessible data. As this datacentric world matures, biologists will be better able to manage the deluge of data arising from digitization programs, governmental mandates for data sharing, and increasing instrumentation of science. The big new biology will create new opportunities for research and will enable scientists to answer questions that require access to data on a scale not previously possible. Informatics will become the new genomics, and those not participating will become marginalized. If a traditional discipline like protistology is to benefit from this big data world, it must define, build, and populate an appropriate infrastructure. The infra- structure is likely to be modular, with modules focusing on needs within defined subject and makes it available in standard formats by an array of pathways. It is the responsibility of protistologists to build such nodes for their own discipline.

Local Alignment Tool Based on Hadoop Framework and GPU Architecture

With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analyze such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented on GPU architectures, for biologists to compare protein sequences. To deal with the big biology data, it is hard to rely on single GPU. Therefore, we implement a distributed BLASTP by combining Hadoop and multi-GPUs. The experimental results present that the proposed method can improve the performance of BLASTP on single GPU, and also it can achieve high availability and fault tolerance.

The escalating politics of ‘Big Biology’

As the history of twentieth-century science suggests, controversies about ‘Big Science’ are vehicles for rethinking the relationship between science and society. Behind arguments about size, scale or enhanced coordination, with all their managerial undertones, lie deepseated commitments to alternative visions of the political organization of knowledge production. The experiments in large-scale biology discussed in this collection are thus best seen as ‘constitutional moments’ (Jasanoff, 2011), occasions when the challenge of creating new and extended research collectives forces an explication of the links between technical practices, organizational architectures and ethical imaginaries. As these articles make clear, however, constitutional moments in the contemporary life sciences come in many shapes and forms. A healthy degree of scepticism towards claims of scale, speed or novelty runs through this special section, and helps qualify many of the most hyperbolic arguments about the nature and consequences of large biological research projects. The very category of ‘big biology’, the editors point out, “is in question from the start”. And yet, all the contributors describe processes of escalation (if not always the ‘scaling up’ of things). They are concerned, that is, with an amplification of the political reflexivity of the scientific enterprise, brought about by the expansion, intensification or acceleration of research efforts. What can we glean from these case studies, then, about the evolving constitution of the life sciences in an era of large data infrastructures and a seemingly relentless push towards greater and faster circulation of informational and biological materials? The following are some open-ended reflections prompted by the many insights that crisscross this collection.

Constituting large-scale biology: Building a regime of governance in the early years of the Human Genome Project

This article examines how a regime for governing the US Human Genome Project (HGP) emerged during the early years of the project, paying special attention to the construction of what might be called its ‘governing frame’. This governing frame provided an interpretive scheme that constituted a set of entities (agents, spaces, things and actions) and promoted an official view of which agents would be endowed with what rights, duties, and privileges, powers and liabilities, and immunities and disabilities as they pertained to other agents and to control over spaces and things. The governing frame of the HGP regime was not codified formally in any single ‘constitutional’ document, but emerged through a historical process. The key elements of this regime took shape through a process of coproduction that constituted a new category of science – ‘large-scale biology’ – and the sociotechnical machinery for governing it. Simultaneously, extant molecular biology was redefined as ‘ordinary biology’, a form of science to be protected from and enhanced by Big Biology. The article is based on ethnographic research in the genome mapping and sequencing community during the HGP.

Questions of critique for big biology: Conjuncture, agency and the global postcolonial

Questions of critique for big biology: Conjuncture, agency and the global postcolonial

Arguably big biology: Sociology, spatiality and the knockout mouse project

Following the completion of the Human Genome Project (HGP), a critical challenge has been how to make biological sense of the amassed sequence data and translate this into clinical applications. A range of large biological research projects, as well as more distributed experimental collaborations, are seeking to realise this through translational research initiatives and postgenomic approaches. Drawing on interviews with key participants, this article explores the biological assumptions, sociological challenges and spatial imaginaries at play in arguments around one of these developments, which is using genetically altered mice to understand gene function. The knockout mouse project (KOMP) is a large-scale initiative in functional genomics, seeking to produce a ‘knockout mouse’ for each gene in the mouse’s genome, which can then be used to answer questions about gene function in mammals. KOMP is frequently framed as one successor to the HGP, emblematic of the ambitions of internationally coordinated biological research. However, the development of new technologies for generating and managing genetically altered mice, alongside the challenge of asking biologically meaningful questions of vast numbers of animals, is creating new frictions in this extension and intensification of biological research practices. This article introduces two separate approaches to the future of international research using mutant mice as stakeholders to negotiate the biological, sociological and spatial challenges of collaboration. The first centres on the directed research practices and sociological assumptions of KOMP, as individual researchers are reorganised around shared animals, databases and infrastructures. The second highlights an alternative vision of the future of biomedical research, using distributed management to enhance the sensitivities and efficiencies of existing experimental practices over space. These exemplify two different tactics in the organisation of an ‘arguably’ big biology. They also critically embody different sociological and spatial imaginaries for the collaborative practices of international translational research.