Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine

A grand challenge impeding optimal treatment outcomes for patients with cancer arises from the complex nature of the disease: the cellular heterogeneity, the myriad of dysfunctional molecular and genetic networks as results of genetic (somatic) and environmental perturbations. Systems biology, with its holistic approach to understanding fundamental principles in biology, and the empowering technologies in genomics, proteomics, single-cell analysis, microfluidics and computational strategies, enables a comprehensive approach to medicine, which strives to unveil the pathogenic mechanisms of diseases, identify disease biomarkers and begin thinking about new strategies for drug target discovery. The integration of multidimensional high-throughput 'omics' measurements from tumour tissues and corresponding blood specimens, together with new systems strategies for diagnostics, enables the identification of cancer biomarkers that will enable presymptomatic diagnosis, stratification of disease, assessment of disease progression, evaluation of patient response to therapy and the identification of reoccurrences. Whilst some aspects of systems medicine are being adopted in clinical oncology practice through companion molecular diagnostics for personalized therapy, the mounting influx of global quantitative data from both wellness and diseases is shaping up a transformational paradigm in medicine we termed 'predictive', 'preventive', 'personalized', and 'participatory' (P4) medicine, which requires new strategies, both scientific and organizational, to enable bringing this revolution in medicine to patients and to the healthcare system. P4 medicine will have a profound impact on society - transforming the healthcare system, turning around the ever escalating costs of healthcare, digitizing the practice of medicine and creating enormous economic opportunities for those organizations and nations that embrace this revolution.     

Global reconstruction of the human metabolic network based on genomic and bibliomic data

Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology.     

Les promesses des sciences omiques à l’ère de la médecine de précision

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.     

Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a progressive disease of increasing public health concern. In western populations the disease has an estimated prevalence of 20%-40%, rising to 70%-90% in obese and type II diabetic individuals. Simplistically, NAFLD is the macroscopic accumulation of lipid in the liver, and is viewed as the hepatic manifestation of the metabolic syndrome. However, the molecular mechanisms mediating both the initial development of steatosis and its progression through non-alcoholic steatohepatitis to debilitating and potentially fatal fibrosis and cirrhosis are only partially understood. Despite increased research in this field, the development of non-invasive clinical diagnostic tools and the discovery of novel therapeutic targets has been frustratingly slow. We note that, to date, NAFLD research has been dominated by in vivo experiments in animal models and human clinical studies. Systems biology tools and novel computational simulation techniques allow the study of large-scale metabolic networks and the impact of their dysregulation on health. Here we review current systems biology tools and discuss the benefits to their application to the study of NAFLD. We propose that a systems approach utilising novel in silico modelling and simulation techniques is key to a more comprehensive, better targeted NAFLD research strategy. Such an approach will accelerate the progress of research and vital translation into clinic.     

Systems biology approaches to heart development and congenital heart disease

Even though the foundation of systems biology approaches to cardiac function was led more than fifty years ago, there has been slow progression over the last few decades. Systems biology studies were mainly focused on lower organisms, frequently on yeast. With the boost of high-throughput technologies, systems level analyses, building one backbone of systems biology, started to complement the single-gene focus in the fields of heart development and congenital heart disease. A challenge is to bring together the many uncovered molecular components driving heart development and eventually to establish computational models describing this complex developmental process. Congenital heart diseases represent overlapping phenotypes, reflecting the modularity of heart development. The aetiology of the majority of congenital heart disease is still unknown, and it is suggestive that understanding the biological network underlying heart development will enhance our understanding for its alteration. This review provides an overview of the framework for systems biology approaches focusing on the developing heart and its pathology. Recent methodological developments building the basis for future studies are highlighted and the knowledge gained is specified.     

Clinical implications of omics and systems medicine: focus on predictive and individualized treatment

Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High‐throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole‐genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide a brief introduction to systems medicine and discuss how it may contribute to the clinical implementation of individualized treatment, using clinically relevant examples.     

The application of organoid models in research into metabolic diseases

Metabolic diseases have become a major threat to human health worldwide as a result of changing lifestyles. The exploration of the underlying molecular mechanisms of metabolic diseases and the development of improved therapeutic methods have been hindered by the lack of appropriate human experimental models. Organoids are three-dimensional in vitro models of self-renewing cells that spontaneously self-organize into structures similar to the corresponding in vivo tissues, recapitulating the original tissue function. Off-body organoid technology has been successfully applied to disease modelling, developmental biology, regenerative medicine, and tumour precision medicine. This new generation of biological models has received widespread attention. This article focuses on the construction process and research progress with regard to organoids related to metabolic diseases in recent years, and looks forward to their prospective applications.     

Using the reconstructed genome-scale human metabolic network to study physiology and pathology

Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology and pharmacology. There are currently more than 20 publications that utilize Recon 1, including studies of cancer, diabetes, host-pathogen interactions, heritable metabolic disorders and off-target drug binding effects. In this mini-review, we focus on the reconstruction of the global human metabolic network and four classes of its application. We show that computational simulations for numerous pathologies have yielded clinically relevant results, many corroborated by existing or newly generated experimental data.     

Development of kinetic models in the nonlinear world of molecular cell biology

Increasingly, successful research on metabolic systems relies on teams of specialists. Because of the enormous complexity of these systems, many experimental groups have sought collaborations with theoreticians for data analysis and modeling. Predictably, cultural differences in scientific approach, methodology, assumptions, and language have led to some persistent difficulties in communication across the experiment-theory frontier. This report attempts to diagnose some of these difficulties from the perspective of 30 years' experience in both experimental and theoretical biology, and to suggest guidelines for effective collaboration between experimentalists and theorists. As these collaborations move to the level of cellular and molecular biology, effective communication will become all the more important because the simple linear rate laws of radiotracer and stable-isotope kinetics will no longer suffice. This is because every form of regulation and control, hallmarks of metabolic systems, results in nonlinear kinetics. To advance this transition to nonlinear cellular and molecular metabolic models and to facilitate communication between experimental and theoretical collaborators, a general procedure for incorporating control mechanisms in metabolic rate laws is developed based on the familiar rapid-equilibrium assumption of classical enzyme kinetics.     

Bayesian design of synthetic biological systems

Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, whereas in the latter, we seek to construct the system that produces the data that we would like to observe, i.e., the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. To deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or in silico prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems.