Standards, databases, and modeling tools in systems biology

Reliable biological and multi-omics research through biometrology

Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.

Publishing reproducible dynamic kinetic models

Publishing repeatable and reproducible computational models is a crucial aspect of the scientific method in computational biology and one that is often forgotten in the rush to publish. The pressures of academic life and the lack of any reward system at institutions, granting agencies and journals means that publishing reproducible science is often either non-existent or, at best, presented in the form of an incomplete description. In the article, we will focus on repeatability and reproducibility in the systems biology field where a great many published models cannot be reproduced and in many cases even repeated. This review describes the current landscape of software tooling, model repositories, model standards and best practices for publishing repeatable and reproducible kinetic models. The review also discusses possible future remedies including working more closely with journals to help reviewers and editors ensure that published kinetic models are at minimum, repeatable. Contact: hsauro@uw.edu.

Finding new edges: systems approaches to MTOR signaling

Cells have evolved highly intertwined kinase networks to finely tune cellular homeostasis to the environment. The network converging on the mechanistic target of rapamycin (MTOR) kinase constitutes a central hub that integrates metabolic signals and adapts cellular metabolism and functions to nutritional changes and stress. Feedforward and feedback loops, crosstalks and a plethora of modulators finely balance MTOR-driven anabolic and catabolic processes. This complexity renders it difficult — if not impossible — to intuitively decipher signaling dynamics and network topology. Over the last two decades, systems approaches have emerged as powerful tools to simulate signaling network dynamics and responses. In this review, we discuss the contribution of systems studies to the discovery of novel edges and modulators in the MTOR network in healthy cells and in disease.

Are synthetic biology standards applicable in everyday research practice?

The issue of standardization in synthetic biology is a recurring one. As a discipline that incorporates engineering principles into biological designs, synthetic biology needs effective ways to communicate results and allow different researchers (both academic and industrial) to build upon previous results and improve on existing designs. An aspect that is left out of the discussions, especially when they happen at the level of academic and industrial consortia or policymaking, is whether or not standards are applicable or even useful in everyday research practice. In this caucus article, we examine this particular issue with the hope of including it in the standardization discussions agenda and provide insights into a topic that synthetic biology researchers experience daily.

Quantitative Systems Pharmacology: A Framework for Context

Quantitative Systems Pharmacology (QSP) is receiving increased attention. As the momentum builds and the expectations grow it is important to (re)assess and formalize the basic concepts and approaches. In this short review, I argue that QSP, in addition to enabling the rational integration of data and development of complex models, maybe more importantly, provides the foundations for developing an integrated framework for the assessment of drugs and their impact on disease within a broader context expanding the envelope to account in great detail for physiology, environment and prior history. I articulate some of the critical enablers, major obstacles and exciting opportunities manifesting themselves along the way. Charting such overarching themes will enable practitioners to identify major and defining factors as the field progressively moves towards personalized and precision health care delivery.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

Challenges and Opportunities for Exploring Patient-Level Data

The proper exploration of patient-level data will pave the way towards personalised medicine. To better assess the state of the art in this field we identify the challenges and uncover the opportunities for the exploration of patient-level data through the review of well-known initiatives and projects focusing on the exploration of patient-level data. These cover a broad array of topics, from genomics to patient registries up to rare diseases research, among others. For each, we identified basic goals, involved partners, defined strategies and key technological and scientific outcomes, establishing the foundation for our analysis framework with four pillars: control, sustainability, technology, and science. Substantial research outcomes have been produced towards the exploration of patient-level data. The potential behind these data will be essential to realise the personalised medicine premise in upcoming years. Hence, relevant stakeholders continually push forward new developments in this domain, bringing novel opportunities that are ripe for exploration. Despite last decade's translational research advances, personalised medicine is still far from being a reality. Patients' data underlying potential goes beyond daily clinical practice. There are miscellaneous challenges and opportunities open for the exploration of these data by academia and business stakeholders.

Genome scale modeling in systems biology: algorithms and resources

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.

Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine

BACKGROUND

Metabolomics is a versatile unbiased method to search for biomarkers of human disease. In particular, one approach in cancer therapy is to promote apoptosis in tumour cells; this could be improved with specific biomarkers of apoptosis for monitoring treatment. We recently observed specific metabolic patterns in apoptotic cell lines; however, in that study, apoptosis was only induced with one pro-apoptotic agent, staurosporine.

OBJECTIVE

The aim of this study was to find novel biomarkers of apoptosis by verifying our previous findings using two further pro-apoptotic agents, 5-fluorouracil and etoposide, that are commonly used in anticancer treatment.

METHODS

Metabolic parameters were assessed in HepG2 and HEK293 cells using the newborn screening assay adapted for cell culture approaches, quantifying the levels of amino acids and acylcarnitines with mass spectrometry.

RESULTS

We were able to identify apoptosis-specific changes in the metabolite profile. Moreover, the amino acids alanine and glutamate were both significantly up-regulated in apoptotic HepG2 and HEK293 cells irrespective of the apoptosis inducer.

CONCLUSION

Our observations clearly indicate the potential of metabolomics in detecting metabolic biomarkers applicable in theranostics and for monitoring drug efficacy.

Publishing 13C metabolic flux analysis studies: a review and future perspectives

(13)C-Metabolic flux analysis ((13)C-MFA) is a powerful model-based analysis technique for determining intracellular metabolic fluxes in living cells. It has become a standard tool in many labs for quantifying cell physiology, e.g., in metabolic engineering, systems biology, biotechnology, and biomedical research. With the increasing number of (13)C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing (13)C-MFA studies so that quality is not sacrificed as the number of publications increases. The main purpose of this paper is to provide an overview of good practices in (13)C-MFA, which can eventually be used as minimum data standards for publishing (13)C-MFA studies. The motivation for this work is two-fold: (1) currently, there is no general consensus among researchers and journal editors as to what minimum data standards should be required for publishing (13)C-MFA studies; as a result, there are great discrepancies in terms of quality and consistency; and (2) there is a growing number of studies that cannot be reproduced or verified independently due to incomplete information provided in these publications. This creates confusion, e.g. when trying to reconcile conflicting results, and hinders progress in the field. Here, we review current status in the (13)C-MFA field and highlight some of the shortcomings with regards to (13)C-MFA publications. We then propose a checklist that encompasses good practices in (13)C-MFA. We hope that these guidelines will be a valuable resource for the community and allow (13)C-flux studies to be more easily reproduced and accessed by others in the future.

Facilitating the use of large-scale biological data and tools in the era of translational bioinformatics

As both the amount of generated biological data and the processing compute power increase, computational experimentation is no longer the exclusivity of bioinformaticians, but it is moving across all biomedical domains. For bioinformatics to realize its translational potential, domain experts need access to user-friendly solutions to navigate, integrate and extract information out of biological databases, as well as to combine tools and data resources in bioinformatics workflows. In this review, we present services that assist biomedical scientists in incorporating bioinformatics tools into their research. We review recent applications of Cytoscape, BioGPS and DAVID for data visualization, integration and functional enrichment. Moreover, we illustrate the use of Taverna, Kepler, GenePattern, and Galaxy as open-access workbenches for bioinformatics workflows. Finally, we mention services that facilitate the integration of biomedical ontologies and bioinformatics tools in computational workflows.

Disruption of reproductive aging in female and male rats by gestational exposure to estrogenic endocrine disruptors

Polychlorinated biphenyls (PCBs) are industrial contaminants and known endocrine-disrupting chemicals. Previous work has shown that gestational exposure to PCBs cause changes in reproductive neuroendocrine processes. Here we extended work farther down the life spectrum and tested the hypothesis that early life exposure to Aroclor 1221 (A1221), a mixture of primarily estrogenic PCBs, results in sexually dimorphic aging-associated alterations to reproductive parameters in rats, and gene expression changes in hypothalamic nuclei that regulate reproductive function. Pregnant Sprague Dawley rats were injected on gestational days 16 and 18 with vehicle (dimethylsulfoxide), A1221 (1 mg/kg), or estradiol benzoate (50 μg/kg). Developmental parameters, estrous cyclicity (females), and timing of reproductive senescence were monitored in the offspring through 9 months of age. Expression of 48 genes was measured in 3 hypothalamic nuclei: the anteroventral periventricular nucleus (AVPV), arcuate nucleus (ARC), and median eminence (females only) by real-time RT-PCR. Serum LH, testosterone, and estradiol were assayed in the same animals. In males, A1221 had no effects; however, prenatal estradiol benzoate increased serum estradiol, gene expression in the AVPV (1 gene), and ARC (2 genes) compared with controls. In females, estrous cycles were longer in the A1221-exposed females throughout the life cycle. Gene expression was not affected in the AVPV, but significant changes were caused by A1221 in the ARC and median eminence as a function of cycling status. Bionetwork analysis demonstrated fundamental differences in physiology and gene expression between cycling and acyclic females independent of treatment. Thus, gestational exposure to biologically relevant levels of estrogenic endocrine-disrupting chemicals has sexually dimorphic effects, with an altered transition to reproductive aging in female rats but relatively little effect in males.

Structure-based high-throughput epitope analysis of hexon proteins in B and C species human adenoviruses (HAdVs)

Human adenoviruses (HAdVs) are the etiologic agent of many human infectious diseases. The existence of at least 54 different serotypes of HAdVs has resulted in difficulties in clinical diagnosis. Acute respiratory tract disease (ARD) caused by some serotypes from B and C species is particularly serious. Hexon, the main coat protein of HAdV, contains the major serotype-specific B cell epitopes; however, few studies have addressed epitope mapping in most HAdV serotypes. In this study, we utilized a novel and rapid method for the modeling of homologous proteins based on the phylogenetic tree of protein families and built three-dimensional (3D) models of hexon proteins in B and C species HAdVs. Based on refined hexon structures, we used reverse evolutionary trace (RET) bioinformatics analysis combined with a specially designed hexon epitope screening algorithm to achieve high-throughput epitope mapping of all 13 hexon proteins in B and C species HAdVs. This study has demonstrated that all of the epitopes from the 13 hexon proteins are located in the proteins' tower regions; however, the exact number, location, and size of the epitopes differ among the HAdV serotypes.