Integrative Data Analytic Framework to Enhance Cancer Precision Medicine.

OPINION article Front. Physiol., 06 November 2015Sec. Systems Biology Archive Volume 6 - 2015 | https://doi.org/10.3389/fphys.2015.00318

Organ reconstruction and manufacturing is a field that aims to create functional organs for transplantation or therapeutic purposes. The development of this field has been driven by rapid advances in various technologies, including three-dimensional (3D) bioprinting, organs-on-chips, organoids, stem cell reprogramming, genome editing and artificial intelligence (AI). Take, for example, the development of organoids and organs-on-chips, which has completely revolutionized the way scientists study organ development, disease progression and drug effects in vitro. 3D bioprinting, which can produce tissues and organs with customized shapes, sizes and functions, has also made it possible to create complex structures with high precision and accuracy, including livers, kidneys, hearts, ears and skin grafts. Bioprinted tissues and organs may soon be used for transplantation on demand in the clinic. Furthermore, manipulation of cell fates and genomes with the latest techniques in reprogramming, genome editing and AI-based computational modelling has allowed us to unlock the hidden potential within human cells and produce new human cell types with new capabilities. Genome editing of patient-derived cells to correct any underlying defects could also ensure that synthetic organs are created with improved functionality and compatibility with the recipient's body. Advancements like these have paved the way for new biomedical applications such as personalized medicine and regenerative therapies. In this special issue on Organ Reconstruction and Manufacturing, we have assembled a corpus of the latest studies that shed light on the cutting-edge progress and challenges in this field. Our special issue highlights the most significant new findings, technologies and therapies that are emerging in this burgeoning field, providing a platform for experts in the field to share their thoughts and predict future trends. The topics covered in this special issue include leading-edge technologies like 3D bioprinting, organs-on-chips and organoids, as well as genetically engineered cells and animals—all of which are critical pieces in the quest for regenerative medicine. If this dream is realized, scientists may well be able to use patient-derived cells to complete drug testing at warp speed, and make it possible to create synthetic organs for transplantation, potentially addressing the shortage of donor organs on a global scale. As we rapidly make technological progress towards this dream, a final piece in the puzzle is the development of ethical guidelines and manufacturing standards to ensure that the production and applications of synthetic organs remain safe, ethical and properly regulated. The creation of synthetic organs raises many ethical questions, including issues related to patient privacy, informed consent and accessibility to healthcare. Therefore it is crucial for us to establish ethical guidelines and manufacturing standards in a collaborative manner, based on multinational and global consensus, to regulate the proper development and use of synthetic organs. We believe our efforts in building such frameworks will help ensure that the benefits of technological breakthroughs in organ reconstruction and manufacturing are maximized, while minimizing potential risks and negative consequences for all of humanity. In conclusion, synthetic organ reconstruction and manufacturing is a rapidly evolving field that has the potential to revolutionize the healthcare industry. I believe recent advancements in biotechnology, computational methods, therapeutic applications, ethics and standards will pave the way for a quantum leap in innovation for the field in the near future.

Single-cell mRNA sequencing into precision medicine: Promise and challenges

Consensus on precision medicine for metastatic cancers: a report from the MAP conference

Recent advances in biotechnology have produced a wealth of genomic data, which capture a variety of complementary cellular features. While these data promise to yield key insights into molecular biology, much of the available information remains underutilized because of the lack of scalable approaches for integrating signals across large, diverse data sets. A proper framework for capturing these numerous snapshots of complementary phenomena under a variety of conditions can provide the holistic view necessary for developing precise systems-level hypotheses. Here we describe bioPIXIE, a system for combining information from diverse genomic data sets to predict biological networks. bioPIXIE utilizes a Bayesian framework for probabilistic integration of several high-throughput genomic data types including gene expression, protein-protein interactions, genetic interactions, protein localization, and sequence data to predict biological networks. The main purpose of the system is to support user-driven exploration through the inferred functional network, which is enabled by a public, web-based interface. We describe the features and supporting methods of this integration and discovery framework and present case examples where bioPIXIE has been used to generate specific, testable hypotheses for Saccharomyces cerevisiae, many of which have been confirmed experimentally.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

Biotechnology is changing healthcare by giving new solutions for disease diagnosis and treatment. Advances in gene editing, RNA-based therapeutics, and cell and gene therapies are innovating values in medical intervention. Techniques such as CRISPR-Cas9 enable precise genetic modifications and offer new avenues for treating genetic disorders and complex diseases. RNA-based therapeutics- mRNA rice and RNA interference are increasingly being applied in treating cancer, genetic disorders, and autoimmune disorders, expanding their scope beyond vaccines. Cell and gene therapies, like CAR-T cell therapy, are evolving to improve their effectiveness and safety over a broader range of diseases. This article describes methodologies in all these cutting-edge technologies and how they may provide targeted and personalized treatment. The discussion also brings into play artificial intelligence that further accelerates drug discovery and enhances the reliability of diagnostics. The recent results presented showcase the potential of these technologies with promising improvements in the treatment outcome of various diseases. The discussion discusses how biotechnology can revolutionize healthcare, leading to personalized medicine in which treatments are tailor-made to dose individually based on genetic profiles. This not only increases treatment efficiency but would also reduce its side effects; it can be said that it represents a significant shift towards precision medicine. Essential issues that still exist include ethics, regulations, and outreach. The biotechnology industry continues to evolve and is likely to take a front-row seat during transformation within the healthcare landscape, offering actionable and sustainable solutions. Overall, the present review gives a detailed overview of contemporary advances in biotechnology concerning changing the realm of healthcare and management of patient outcomes. Methodologies, observations, and future directions will be discussed, shedding light on how biotechnology will define the course of medicine in the future.

Advances in biotechnology have enabled researchers to study molecular biology from the point of view of systems, from focused efforts at functional annotation to the study of pathways, regulatory networks, protein-protein interaction networks, etc. However, direct observation of these systems has proved difficult, time-consuming, and often unreliable. Thus computational methods have been developed to infer such systems from high-throughput data, such as sequences, gene expression levels, ChIP-Seq signals, etc. For the most part, these methods have not yet proved accurate and reliable enough to be used in automated analysis pipelines. Most methods used to infer biological networks rely on data for a single organism; a few attempt to leverage existing knowledge about some related organisms. Today, however, we have data about a large variety of organisms as well as good consensus about the evolutionary relationships among these organisms, so that the latter can be used to integrate the former in a well founded manner, thereby gaining significant power in the analysis. We have coined the term Phylogenetic Transfer of Knowledge (PTK) for this approach to inference and analysis. A PTK analysis considers a family of organisms with known evolutionary relationships and "transfers" biological knowledge among the organisms in accordance with these relationships. The output of a PTK analysis thus includes both predicted (or refined) target data (such as networks) for the extant organisms and inferred details about their evolutionary history. While a few ad hoc inference methods used a PTK approach almost a dozen years ago, we first provided a global perspective on such methods just six years ago. The last few years have seen a significant increase in research in this area, as well as new applications. The time is thus right for a review of recent work that falls under this heading, a characterization of the solutions proposed, and a description of remaining challenges.

A proliferation of new computational methods and software tools for synthetic biology design has emerged in recent years but the field has not yet reached the stage where the design and construction of novel synthetic biology systems has become routine. To a large degree this is due to the inherent complexity of biological systems. However, advances in biotechnology and our scientific understanding have already enabled a number of significant achievements in this area. A key concept in engineering is the ability to assemble simpler standardised modules into systems of increasing complexity but it has yet to be adequately addressed how this approach can be applied to biological systems. In particular, the use of computer aided design tools is common in other engineering disciplines and it should eventually become centrally important to the field of synthetic biology if the challenge of dealing with the stochasticity and complexity of biological systems can be overcome.

BackgroundAs a result of recent advances in biotechnology, many findings related to intracellular systems have been published, e.g., transcription factor (TF) information. Although we can reproduce biological systems by incorporating such findings and describing their dynamics as mathematical equations, simulation results can be inconsistent with data from biological observations if there are inaccurate or unknown parts in the constructed system. For the completion of such systems, relationships among genes have been inferred through several computational approaches, which typically apply several abstractions, e.g., linearization, to handle the heavy computational cost in evaluating biological systems. However, since these approximations can generate false regulations, computational methods that can infer regulatory relationships based on less abstract models incorporating existing knowledge have been strongly required.ResultsWe propose a new data assimilation algorithm that utilizes a simple nonlinear regulatory model and a state space representation to infer gene regulatory networks (GRNs) using time-course observation data. For the estimation of the hidden state variables and the parameter values, we developed a novel method termed a higher moment ensemble particle filter (HMEnPF) that can retain first four moments of the conditional distributions through filtering steps. Starting from the original model, e.g., derived from the literature, the proposed algorithm can sequentially evaluate candidate models, which are generated by partially changing the current best model, to find the model that can best predict the data. For the performance evaluation, we generated six synthetic data based on two real biological networks and evaluated effectiveness of the proposed algorithm by improving the networks inferred by previous methods. We then applied time-course observation data of rat skeletal muscle stimulated with corticosteroid. Since a corticosteroid pharmacogenomic pathway, its kinetic/dynamics and TF candidate genes have been partially elucidated, we incorporated these findings and inferred an extended pathway of rat pharmacogenomics.ConclusionsThrough the simulation study, the proposed algorithm outperformed previous methods and successfully improved the regulatory structure inferred by the previous methods. Furthermore, the proposed algorithm could extend a corticosteroid related pathway, which has been partially elucidated, with incorporating several information sources.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0154-2) contains supplementary material, which is available to authorized users.

With the current emphasis on ‘big data' marking a new stage in the advance of biomedical sciences, improvements in computational capability, together with the impact of high throughput techniques and genome-wide methods, mean that biological and medical fields are now data-rich to a degree that was unknown a few decades ago. Increased data availability has not only highlighted the complementarity needed between biology and computer science, but has served to emphasise interdisciplinary overlap with mathematical and physical sciences in the formulation of computational models, posing of hypotheses and statistical interpretation of results. Information derived from diverse sources means that linking system behaviour to changes at cellular and molecular scales has become a viable goal, facilitated by techniques such as network theory, stochastic processes and integrative data analysis. The studies of systems of biological components, their dynamic behaviour and reliance on wide-ranging data, together with translation to disease progression and treatment options, define systems biology and medicine. The IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) is a well-established research conference, providing a leading forum for disseminating the latest research in bioinformatics and health informatics. It attracts contributions from both academic and industrial scientists, which range from biology and medicine, to chemistry, computer science, mathematics and statistics. In particular, and in addition to its over-arching remit and general proceedings, IEEE BIBM provides an important platform for showcasing computational and mathematical modelling methods, together with the data integration, analysis and visualisation, which underpin these. Selected and extended papers from several of their important workshops thus provide the focus of this Special Issue. The papers deal with basic model formulation, data analysis and the incorporation of these analyses in the decision-making process for clinical treatment. The algorithms and methods include ant colony optimisation (Sapin et al.) of the interactions between a number of single-nucleotide polymorphisms (SNPs). Discriminatory performance of the algorithm is found to agree well with SNP identification from large-scale genome-wide association studies for Type II diabetes. Medical image analysis is the objective for applying both evolutionary and swarm intelligence algorithms. In the former case, data classification and monitoring in Parkinson's disease in humans is considered, together with characterisation of genetic mutations in the fruit fly vector (Smith et al.). Deployment of swarm intelligence algorithms, such as stochastic diffusion search for CT scans and X-Rays, and learning vector quantisation for identification of abnormal tumour regions in MR segmentation (al-Rifaie et al.), also demonstrate the applicability of these methods. Wong-Lin & Cullen explore the importance of dopamine as a neurotransmitter for multiple brain functions using a computational model that spans multiple levels of function and different dynamics, and lay the foundation for an integrated approach to realistic in silico simulation of dopaminergic systems in neuropharmacology. Investigation, similarly, of the relative dynamics and structure of human intestinal crypts in malignant systems, provides the motivation for the formulation of an epigenetic model using the agent-based paradigm (Roznovat & Ruskin). Epigenetics, the additional layer of inherited genome regulation, together with epigenome-wide association studies, linking intra-individual epigenetic variation, are linked to the evolution of human diseases, such as cancer, and to autoimmune and neuropsychiatric disorders. The derived computational model enables comparative analysis on aberrant DNA methylation levels in cancer development and the investigation of the effect of potential methylation inhibitors during disease initiation. Inhibitors, both time-dependent and time-independent, merit important distinction in the characterisation of compound potency and drug-response. Reversibility properties for both are investigated by means of a simple kinetic model (Yue & You) and analysis of the outcomes and their contrast with supporting numerical studies. The complexity of the drug-receptor process in this case indicates the contribution that can be made by computational modelling, as well as the need to support formulation and parameterisation with good quality data. A further example of the flexibility and scope offered by the modelling approach is provided by studies of microtubule ordering and the way in which this is affected by collision and crossover. A 3-state model is used to determine the influence of spontaneous catastrophe, crossover and ketanin-mediated severing on plant microtubule ordering across different temperatures (Mace & Wang). It is evident, however, that, while many dynamic biological systems share similar properties and constraints, model specification, particularly in the context of sparse or poor experimental data, is often anything but straightforward. Achieving an unambiguous estimation of the full set of parameters is frequently challenging and may be impossible. The identifiability in this context, of typical S-System models for dynamic biological systems, is discussed with respect to an application on yeast fermentation pathway determination (Li et al.). The authors note also that, even where data are available, these may be noisy or incomplete, which also affects the identification process. When analysing such data, a range of statistical and computational methods are available. These can be categorised by their level of automation, the sophistication of their algorithms, their data type and size and so on. Bioinformatics, often described as the intersection of mathematics, biology and computer science, typically involves processing large amounts of data, so methods are usually machine-based. The paper (by Akutekwe et al.) thus describes a two-stage optimisation process in the modelling of protein-protein networks. This is applied to complex diseases, such as colorectal cancer, and discusses the performance of machine learning methods and selected algorithms, such as particle swarm optimisation and differential evolution for parameter optimisation for classification and automatic discovery of biomarkers, with Bayesian network analysis used to predict their temporal linkage. Ultimately, systems modelling and simulation-optimisation, with appropriate data analysis, can play a major role in decision-support systems for biomedical applications. The Heartsearcher for patient risk classification is proposed by Park & Kang, while Bansal et al. describe a cardiac monitoring system which uses ECG signal analysis and pattern recognition provided through a mobile device, a remote server, and medical practitioner point-of-care communication. Evidently, the use of computational models and methods is widespread in Systems Biology and Medicine and, as datasets grow in size and complexity, biomedical systems increase in sophistication, and computing power escalates, this trend looks set to continue.

BackgroundMulticellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.ResultsWe develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.ConclusionsOur mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.

Systems biology is a field within biology, aiming at understanding biological processes at the systems level and emerging from dynamic interactions of individual components that operate at multiple spatiotemporal scales. It is now an established and fundamental interdisciplinary research field. Systems biology studies biological systems by systematically perturbing them (biologically, genetically, chemically, or other); monitoring the gene, protein, metabolite, and informational pathway responses; integrating these data; ultimately formulating mathematical models that describe the structure of the system and predict its response to individual perturbations. Integrated “omics” (such as genome-wide measurements of transcripts, protein levels, or metabolite level) approaches have created exciting opportunities for systems biology and other biological researches thanks to the rapid advancement of high throughput biotechnologies. Computational methods such as data preprocessing, representation, modeling, measurement, interpretation, prediction, visualisation, and simulation have been well applied to understand biological processes and biological systems. We organised this special issue to provide an international forum to discuss the most recent developments in the field regarding integrated data analysis approaches in systems biology research such as pattern recognition and prediction, modeling and simulation, and data representation and visualisation. This special issue featured “integrated approach” and “complex biological system” themes. We are interested in both new theories and tools in this area as well as their applications in systems biology. The potential topics include (i) large-scale or cross-species data integration for the reconstruction of networks and pathways; (ii) genomic data analysis using systems biology approaches; (iii) quantitative understanding of the dynamics of regulatory, signaling, interaction, and metabolic networks through modeling and simulation techniques; (iv) prediction of protein/RNA structure and biological network-based interactions; (v) data integration and knowledge-driven approach in biomarker identification and drug discovery; (vi) enhancement and enablement of knowledge discovery in functional genomics of disease and other phenotypes through integrated omics approach; (vii) semantic webs and ontology-driven biological data integration methods; (viii) development of integrated systems biology visualisation and analysis tools; (ix) development of integrated systems biology visualisation and analysis tools; (x) integrating approaches in transitional bioinformatics and personalized medicine. Eight manuscripts were submitted in response to this special issue and six were finally accepted for publication, ranging from mathematical model, computational pipeline, and engineering design to computational models, with the applications at molecular, neuronal, protein, gene regulatory network, and gene ontology levels. The comparison on biology sequences is one of the most important tasks in analyzing similarities of function and properties. W. Deng and Y. Luan integrated the dual-vector curve (DV-curve) and the detailed hydrophobic-hydrophilic (HP) model of amino acids, in the representation and visualization of protein sequences. Although the information might be lost in the representation, their results showed that the proposed method is efficient and feasible when focusing on the important part of the sequences. L. Pasotti and S. Zucca reviewed the recent advances and computational tools in biological engineering design on which predictability issues in promoters, ribosome binding sites, coding sequences, transcriptional terminators, and plasmids were specifically discussed. The authors suggested that bottom-up approaches are urgently needed in order to refine and exploit the full potential of synthetic biology and a mixture of prediction tools could rapidly boost the efficiency of biological engineering by providing a smaller search space than fully random-based approaches. In “A pipeline for neuron reconstruction based on spatial sliding volume filter seeding,” D. Sui et al. proposed a pipeline with a new seeding method for the construction of neuron structures from three-dimensional microscopy images stacks, which will be beneficial to three- dimensional neuron reconstruction and detection. Gene regulatory networks consist of interactions between large number of genes and their regulators and are involved in every biological process. L. P. Tian et al. designed a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality approach, a gene repressillatory network was employed to illustrate the effectiveness of the proposed design approach. In “Effects of maximal sodium and potassium conductance on the stability of Hodgkin-Huxley model,” Y. Zhang et al. applied stability theory in the model design to investigate the importance of maximal sodium conductance and maximal potassium conductance. The study could help in researches relevant to diseases caused by maximal conductance anomaly. In “Correlating information contents of gene ontology terms to infer semantic similarity of gene products,” the author proposed a new semantic gene ontology similarity measurement. A gene product was represented as a vector that is composed of information contents of gene ontology terms annotated for the gene product, and the pairwise similarity between two gene products was viewed as the relatedness of their corresponding vectors using three measures: Pearson's correlation coefficient, cosine similarity, and Jaccard index.

A GMM-IG framework for selecting genes as expression panel biomarkers

Analyse en réseau par fouille de données textuelles systématique du concept de psychiatrie personnalisée et de précision