Systems Medicine

A dynamic systems model was used to generate parameters describing a phenotype of Hypothalamic-Pituitary-Adrenal (HPA) behavior in a sample of 36 patients with chronic fatigue syndrome (CFS) and/ or fibromyalgia (FM) and 36 case-matched healthy controls. Altered neuroendocrine function, particularly in relation to somatic symptoms and poor sleep quality, may contribute to the pathophysiology of these disorders. Blood plasma was assayed for cortisol and ACTH every 10 min for 24 h. The dynamic model was specified with an ordinary differential equation using three parameters: (1) ACTH-adrenal signaling, (2) inhibitory feedback, and (3) non-ACTH influences. The model was ''personalized'' by estimating an individualized set of parameters from each participant's data. Day and nighttime parameters were assessed separately. Two nocturnal parameters (ACTH-adrenal signaling and inhibitory feedback) significantly differentiated the two patient subgroups ("fatigue-predominant" patients with CFS only versus ''pain-predominant'' patients with FM and comorbid chronic fatigue) from controls (allp's < .05), whereas daytime parameters and diurnal/nocturnal slopes did not. The same nocturnal parameters were significantly associated with somatic symptoms among patients (p's < .05). There was a significantly different pattern of association between nocturnal non-ACTH influences and sleep quality among patients versus controls (p < .05). Although speculative, the finding that patient somatic symptoms decreased when more cortisol was produced per unit ACTH, is consistent with cortisol's anti-inflammatory and sleep-modulatory effects. Patients' HPA systems may compensate by promoting more rapid or sustained cortisol production. Mapping "behavioral phenotypes" of stress-arousal systems onto symptom clusters may help disentangle the pathophysiology of complex disorders with frequent comorbidity.

Traditional pharmacokinetic-pharmacodynamic (PK/PD) models rely on static parameters derived from population averages, limiting their ability to predict individual patient responses and adverse drug reactions. This paper introduces the Hepatic Response Unified Model (HRUM), a novel dynamical systems approach that incorporates real-time metabolic capacity fluctuations into PK/PD modeling. HRUM's core innovation lies in its Dynamic Metabolic Feedback Loop, where hepatic function is modeled as a state variable that responds dynamically to drug concentration and toxicity. The model consists of three coupled ordinary differential equations describing drug concentration dynamics, pharmacodynamic effects, and instantaneous metabolic capacity. Mathematical analysis reveals critical stability conditions that determine whether a patient will maintain therapeutic drug levels or experience metabolic collapse leading to drug-induced liver injury (DILI). The model introduces two key clinical metrics: the Metabolic Resilience Index (MRI) for assessing dosing sensitivity and the Efficacy-Toxicity Gradient (ETG) for optimizing therapeutic strategies. HRUM demonstrates particular utility for drugs with narrow therapeutic windows, including chemotherapeutics, anticoagulants, and certain antibiotics. By enabling patientspecific parameter estimation through biomarkers, HRUM represents a significant advancement toward personalized medicine in clinical pharmacology. The model's predictive capabilities address the critical question of why standard dosing regimens work for most patients while causing severe adverse reactions in others, providing a mathematical framework for understanding and preventing drug-induced hepatotoxicity.

Systems biology is characterized by the application of computational models and methods to a biological question, focusing on entire biological systems and the complex interactions therein. In systems biology, an iterative cycle of model building and validation based on experimental data generation and analysis is pursued. The key purpose of computational models is the integration of The liver is a highly complex organ. It is characterized by (a) its multi-scale architecture, (b) its special perfusion system with two parallel inflows (hepatic artery and portal vein) and one outflow (hepatic vein), (c) its multitude of functions including metabolic homeostasis, synthesis of essential compounds, detoxification, and excretion of toxic substances, and (d) its high regenerative capacity after injury. Despite the seemingly regular microstructure of the liver, perfusion, functional, and regenerative capacity are distributed heterogeneously in the

Chronic diseases are disorders of long duration and generally slow progression . They include four major non-communicable diseases (NCDs) listed by the World Health Organization (WHO) [2] -cardiovascular diseases, cancer, chronic respiratory diseases and diabetes -as well as other NCDs, such as neuropsychiatric disorders [3] and arthritis. As survival rates have improved for infectious and genetic diseases, chronic diseases have come to include communicable diseases (such as HIV/AIDS) and genetic disorders (such as cystic fibrosis). NCDs represent the major global health problem of the 21st century ; they affect all age groups [6] and their burden is greater than that of infectious diseases. NCDs are the world leading cause of disease burden and mortality [2] and are increasing in prevalence and burden , even in low-and middleincome countries . Costs incurred by uncontrolled NCDs are substantial, especially in underserved populations [9] and low-and middle-income countries . NCDs are an under-appreciated cause of poverty and hinder economic development . Importantly, management of NCDs has recently been prioritized globally (Box 1). Chronic diseases are caused by complex geneenvironment interactions acting across the lifespan from the fetus to old age (Figure ). In this context, 'environment' includes risk and protective factors associated with environment and lifestyle, such as

Recent studies show that the dysregulation of the transcription factor SOX10 is essential for the development and progression of melanoma. MicroRNAs (miRNAs) can regulate the expression of transcription factors at the post-transcriptional level. The interactions between SOX10 and its targeting miRNAs form network motifs such as feedforward and feedback loops. Such motifs can result in nonlinear dynamics in gene expression levels, therefore playing a crucial role in regulating tumor proliferation and metastasis as well as the tumor's responses to therapies. Here, we reviewed and discussed the intricate interplay between SOX10 and miRNAs in melanoma biology including melanogenesis, phenotype switch, and therapy resistance. Additionally, we investigated the gene regulatory interactions in melanoma, identifying crucial network motifs that involve SOX10, MITF, and miRNAs. We also analyzed the expression levels of the components within these motifs. From a control theory perspective, we explained how these dynamics are linked to the phenotypic plasticity of melanoma cells. In summary, we underscored the importance of employing a datadriven network biology approach to elucidate the complex regulatory mechanisms and identify driver network motifs within the melanoma network. This methodology facilitates a deeper understanding of the regulation of SOX10 and MITF by miRNAs in melanoma. The insight gained could potentially contribute to the development of miRNA-based treatments, thereby enhancing the clinical management of this malignancy.

The Covid-19 pandemic has challenged both medicine and governments as they have strived to confront the pandemic and its consequences. One major challenge is that evidence-based medicine has struggled to provide timely and necessary evidence to guide medical practice and public policy formulation. We propose an extension of evidence-based corona medicine to an organismic systems corona medicine as a multilevel conceptual framework to develop a robust concept-oriented medical system. The proposed organismic systems corona medicine could help to prevent or mitigate future pandemics by transitioning to a bifocal medicine that extends an empirical evidence-based medicine to a theory-oriented organismic systems medicine.

In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard (2010) contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, all mechanisms are more or less general (or abstract), and on the other, many (if not all) design principles are causal systems.

Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overvi...

The emergence of systems biology as a result of the molecular revolution in biology and progress in genomic and proteomics is now beginning to affect medicine. The systems approaches are poised to revolutionize medicine as they have been revolutionizing biology, through their application to drug discovery and therapeutic applications including personalized medicine, introduction of novel diagnostic and surgical procedures, design and use of medical devices, and education and training. While the terms "systems biology," "systems physiology," and "systems medicine" are frequently used but not always defined, we will attempt to provide working definitions of these terms, and give examples of ongoing physiome projects.

The emergence of systems biology as a result of the molecular revolution in biology and progress in genomic and proteomics is now beginning to affect medicine. The systems approaches are poised to revolutionize medicine as they have been revolutionizing biology, through their application to drug discovery and therapeutic applications including personalized medicine, introduction of novel diagnostic and surgical procedures, design and use of medical devices, and education and training. While the terms "systems biology," "systems physiology," and "systems medicine" are frequently used but not always defined, we will attempt to provide working definitions of these terms, and give examples of ongoing physiome projects.

This paper presents a brief history of Systems Theory, progresses to Systems Biology, and its relation to the more traditional investigative method of reductionism. The emergence of Systems Medicine represents the application of Systems Biology to disease and clinical issues. The challenges faced by this transition from Systems Biology to Systems Medicine are explained; the requirements of physicians at the bedside, caring for patients, as well as the place of human-human interaction and the needs of the patients are addressed. An organ-focused transition to Systems Medicine, rather than a genomic-, molecular-, or cell-based effort is emphasized. Organ focus represents a middle-out approach to ease this transition and to maximize the benefits of scientific discovery and clinical application. This method manages the perceptions of time and space, the massive amounts of human-and patient-related data, and the ensuing complexity of information.

Molecular medicine is undergoing a revolution, creating a data fog that may obscure understanding. The functioning human is analogous to a biological factory controlled by an incredibly complex Information and Communication (IC) network. It is proposed that 7 billion computational replicas be made of those 7 billion human IC networks to enable interrogation and manipulation, for understanding and personalized healthcare. This requires a revolutionary ICT that follows the organization of the biological information and communication flows, with implications for hardware, software and connectivity.

Neurodegenerative diseases, particularly Alzheimer's disease (AD), pose an unprecedented threat to global healthcare systems and represent a mounting financial burden on modern society. Current projections indicate costs exceeding $2 trillion globally by 2030, with devastating implications for healthcare infrastructure and societal welfare. Leading economists and healthcare experts warn that, if left unchecked, Alzheimer's will eventually bankrupt our society-a stark reality driven by the exponential growth in cases and the astronomical costs of late-stage care. Despite significant advances in therapeutic research, contemporary approaches remain largely reactive, focusing on late-stage interventions when neurological damage is already extensive and largely irreversible. There is, therefore, a critical and urgent need to revolutionise our approach by shifting towards proactive disease prevention. This paper presents a comprehensive framework that leverages three key innovations: established and emerging blood-based biomarker technologies, the implementation of AD Prevention Bonds to overcome current reimbursement challenges, aligning economic incentives with preventative care, and groundbreaking insights into molecular pathways-particularly the role of GLP-1 in neuroprotection. Through Zentara's holistic approach, we demonstrate how early intervention can fundamentally alter disease trajectories whilst simultaneously addressing the economic challenges that currently impede widespread adoption of preventative measures.

Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis, however, has also demonstrated the need for systemic thinking and transdisciplinarity and the limits of MSM: the neglect of the bio-psycho-social systemic nature of humans and their context as the object of individual therapeutic and population-oriented interventions. COVID-19 illustrates how a medical problem requires a transdisciplinary approach in epidemiology, pathology, internal medicine, public health, environmental medicine, and socio-economic modeling. Regarding the ne...

Currently, medicine is transforming towards molecular systems medicine (MSM), based on molecular systems biology (MSB). These approaches should be related to Ludwig von Bertalanffy&#39;s vision of “organismic” systems biology/medicine (OSB/OSM) and of general system theory (GST), which he created already in the 1930s. In this paper, on the basis of current diversity of knowledge in medicine, major differences between MSB/MSM and OSB/OSM are highlighted: MSB is based on biochemical high‐throughput technologies, sophisticated mathematical data analytical tools, and supercomputers for computation, whereas OSB is based on developmental biology and is concept and theory oriented. Metatheoretical considerations show that holistic but still reductive MSM cannot bridge the categorical molecule–cell difference, the mind–body difference, and the environment–organism gap by a consistent molecular and mechanistic theory of the organism. In contrast, the options of theoretical interlevel modelli...

Ce rapport présente les travaux du groupe d'experts réunis par l'Inserm dans le cadre de la procédure d'expertise collective (annexe 1), pour répondre à la demande de la Caisse nationale d'assurance maladie des travailleurs salariés (Cnamts) concernant les aspects scientifiques, médicaux et sociétaux des tests génétiques. Ce travail s'appuie sur les données scientifiques disponibles en date du dernier trimestre 2006. Près de 600 articles ont constitué la base documentaire de cette expertise. Le centre d'expertise collective de l'Inserm a assuré la coordination de cette expertise collective.

Melanoma is a complex disease with various clinical and histopathological features that impact prognosis and treatment decisions. Traditional methods of melanoma prognosis involve manual examination and interpretation of clinical and histopathological data by dermatologists and pathologists. However, the subjective nature of these assessments can lead to inter-observer variability and suboptimal prognostic accuracy. AI, with its ability to analyze vast amounts of data and identify patterns, has emerged as a promising tool for improving melanoma prognosis. Methods: A comprehensive literature search was conducted to identify studies that employed AI techniques for melanoma prognosis. The search included databases such as PubMed and Google Scholar, using keywords such as "artificial intelligence," "melanoma," and "prognosis." Studies published between 2010 and 2022 were considered. The selected articles were critically reviewed, and relevant information was extracted. Results: The review identified various AI methodologies utilized in melanoma prognosis, including machine learning algorithms, deep learning techniques, and computer vision. These techniques have been applied to diverse data sources, such as clinical images, dermoscopy images, histopathological slides, and genetic data. Studies have demonstrated the potential of AI in accurately predicting melanoma prognosis, including survival outcomes, recurrence risk, and response to therapy. AI-based prognostic models have shown comparable or even superior performance compared to traditional methods.

Inflammatory bowel diseases (IBD) are chronic and recurrent inflammatory disorders of the gastrointestinal tract. The elucidation of their etiopathology requires complex and multiple approaches. Systems biology has come to fulfill this need in approaching the pathogenetic mechanisms of IBD and its etiopathology, in a comprehensive way, by combining data from different scientific sources. In combination with bioinformatics and network medicine, it uses principles from computer science, mathematics, physics, chemistry, biology, medicine and computational tools to achieve its purposes. Systems biology utilizes scientific sources that provide data from omics studies (e.g., genomics, transcriptomics, etc.) and clinical observations, whose combined analysis leads to network formation and ultimately to a more integrative image of disease etiopathogenesis. In this review, we analyze the current literature on the methods and the tools utilized by systems biology in order to cover an innovative and exciting field: IBD-omics.

Influenza represents a serious threat to public health with thousands of deaths each year. A deeper understanding of the host-pathogen interactions is urgently needed to evaluate individual and population risks for severe influenza disease and to identify new therapeutic targets. Here, we review recent progress in large scale omics technologies, systems genetics as well as new mathematical and computational developments that are now in place to apply a systems biology approach for a comprehensive description of the multidimensional host response to influenza infection. In addition, we describe how results from experimental animal models can be translated to humans, and we discuss some of the future challenges ahead.

Endocrine diseases convey far-reaching consequences for individual health, ranging from a reduced quality of life over an increased risk of serious secondary diseases to acutely lifethreatening conditions. In recent decades, endocrinology has faced considerable progress, for example in molecular biological research, assay technology and therapeutic options. However, serious gaps remain open that prevent the derivation of a unified endocrine theory and the development of a predictive, preventive, personalised and participatory medicine (P4 medicine). As a possible brick for a solution, we have developed the universal non-linear "MiMe-NoCoDI" platform for formulating cybernetic models of endocrine networks. It allows the development of mathematical "high fidelity" descriptions as well as the implementation of simulations on digital and analog computers. In addition, it provides the basis for new diagnostic procedures in the form of calculated biomarkers (structure parameter inference approach-SPINA) and approaches to therapy planning (set point optimisation and targeting-SPOT), which are superior to previous methods of clinical endocrinology. This progress paves the way for a future precision endocrinology with applications ranging from vertically integrated physiology over individually planned patient treatment to macro-strategies in the public healthcare system.

L’objectif général de ce travail de recherche est de trouver des critères constitutifs d’une définition qui répondraient au problème du pluralisme de la définition de l’individu biologique, ainsi qu’aux enjeux pratiques reliés à ce concept en biologie. Les problèmes associés au pluralisme et à la compétition entre les définitions émergent des chevauchements de définitions.
Pour ce faire, je propose l’idée que l’inclusion de critères environnementaux peut contribuer à résoudre le pluralisme de définition. L’environnement serait représenté par les critères de l’échelle et de l’observateur. Les difficultés associées au pluralisme disparaissent lorsqu’on accorde une considération au cadre théorique qui impose une résolution en fonction du sujet étudié. En conséquence, le pluralisme est compris comme l’application de critères généraux correspondant à des entités biologiques situées à différentes échelles.
Ainsi, le pluralisme provient de la sensibilité d’échelle dans laquelle les individus biologiques se situent. Ce qui entraine l’émerge de différents outils pour les identifier.
En d’autres termes, ce travail de recherche propose de concevoir l’individualité biologique comme une redondance structurelle qui se reproduit à chaque échelle du vivant sous différentes modalités en raison d’une forte dépendance d’échelle. Les différentes définitions ciblent une individualité située à une échelle précise et non l’individualité en général. C’est pourquoi nous proposons un diagnostic de l’individu biologique et non une définition d’un individu. Ce diagnostic se compose de trois critères spécifiques, distinction unité et communication, et de deux critères contextuels, l’échelle et l’observateur (le cadre théorique choisi).
Ce diagnostic de l’individu biologique se veut minimaliste et universel. Minimaliste, car chaque critère correspond à une caractéristique essentielle d’une entité pour être identifié comme un individu biologique. Et universel, car ce diagnostic s’applique à l’ensemble des niveaux d’organisation du vivant. Il permet également aux perspectives de prendre en compte les spécificités de leur objet d’étude ainsi que les contraintes liées à son environnement.

Precision medicine (PM) is an emerging data-driven health care approach that integrates phenotypic, genomic, epigenetic, and environmental factors unique to an individual. The goal of PM is to facilitate diagnosis, predict effective therapy, and avoid adverse reactions specific for each patient. The forefront of PM is in oncology; nonetheless, it is developing in other fields of medicine, including rheumatology. Recent studies on elucidating the genetic architecture of polygenic and monogenic rheumatological diseases have made PM possible by enabling physicians to customize medical treatment through the incorporation of clinical features and genetic data. For complex inflammatory disorders, the prevailing paradigm is that disease susceptibility is due to additive effects of common reduced-penetrance gene variants and environmental factors. Efforts have been made to calculate cumulative genetic risk score (GRS) and to relate specific susceptibility alleles for use of target therapies. The discovery of rare patients with single-gene high-penetrance mutations informed our understanding of pathways driving systemic inflammation. Here, we review the advances in practicing PM in patients with primary systemic vasculitides (PSVs). We summarize recent genetic studies and discuss current knowledge on the contribution of epigenetic factors and extracellular vesicles (EVs) in disease progression and treatment response. Implementation of PM in PSVs is a developing field that will require analysis of a large cohort of patients to validate data from genomics, transcriptomics, metabolomics, proteomics, and epigenomics studies for accurate disease profiling. This multi-omics approach to study disease pathogeneses should ultimately provide a powerful tool for stratification of patients to receive tailored optimal therapies and for monitoring their disease activity.

After intense scientific exploration and more than a decade of failed trials, Alzheimer's disease (AD) remains a fatal global epidemic. A traditional research and drug development paradigm continues to target heterogeneous late-stage clinically phenotyped patients with single "magic bullet" drugs. Here, we propose that it is time for a paradigm shift towards the implementation of precision medicine (PM) for enhanced risk screening, detection, treatment, and prevention of AD. The overarching structure of how PM for AD can be achieved will be provided through the convergence of breakthrough technological advances, including big data science, systems biology, genomic sequencing, blood-based biomarkers, integrated disease modeling and P4 medicine. It is hypothesized that deconstructing AD into multiple genetic and biological subsets existing within this heterogeneous target population will provide an effective PM strategy for treating individual patients with the specific agent(s) that are likely to work best based on the specific individual biological makeup. The Alzheimer's Precision Medicine Initiative (APMI) is an international collaboration of leading interdisciplinary clinicians and scientists devoted towards the implementation of PM in Neurology, Psychiatry and Neuroscience. It is hypothesized that successful realization of PM in AD and other neurodegenerative diseases will result in breakthrough therapies, such as in oncology, with optimized safety profiles, better responder rates and treatment responses, particularly through biomarker guided early preclinical disease stage clinical trials.

Despite the promises made that genomic sequencing would transform therapy by introducing a new era of personalized medicine, relatively few tangible breakthroughs have been made. This has led to the recognition that complex interactions at multiple spatial, temporal, and organizational levels may often combine to produce disease. Understanding this complexity requires that existing and future models are used and interpreted within a framework that incorporates knowledge derived from investigations at multiple levels of biological function. It also requires a computational infrastructure capable of dealing with the vast quantities of data generated by genomic approaches. In this review, we discuss the use of molecular modeling to generate quantitative and qualitative insights at the smallest scales of the systems biology hierarchy, how it can play an important role in the development of a systems understanding of disease and in the application of such knowledge to help discover new therapies and target existing ones on a personal level.

This paper defines a revolution as an orthogonal change in direction, a 90-degree perpendicular turn from the status quo ways of thinking, being and doing, so as to create a complete break, an abolitionist rupture with current and past ways of producing knowledge.

David Bowie was a relatable example of a revolutionary and orthogonal innovator who completely and courageously broke with the past and the present and opened up new vistas in music and performing arts.

The late anthropologist and public intellectual David Graeber also argued that a revolution fundamentally changes the assumptions in a given field of inquiry. Changing the entrenched assumptions that are long ossified, outdated or uncritically internalized by a knowledge community and profession can have multiplying revolutionary effects on downstream knowledge production.

Grounding systems medicine in political determinants of planetary health, to link two fields of inquiry that have remained isolated and orthogonal since the 17th century, is nothing short of a revolution and orthogonal innovation in the making. For systems medicine to be a truly revolutionary field, it ought to acknowledge that there is no single-issue health nor single-issue politics.

https://www.liebertpub.com/doi/10.1089/omi.2024.0173

Proponents of the emerging field of P4 medicine (defined as personalized, predictive, preventive and participatory) argue that computational integration and analysis of patient-specific “big data” will revolutionize our health care systems, in particular primary care-based disease prevention. While many ambitions remain visionary, steps to personalize medicine are already taken via personalized genomics, mobile health technologies and pilot projects. An important aim of P4 medicine is to enable disease prevention among healthy persons through detection of risk factors. In this paper, we examine the current status of P4 medicine in light of historical and current challenges to predictive and preventive medicine, including overdiagnosis and overtreatment. Moreover, we ask whether it is likely that in silico integration of patient-specific data will be able to better deal such challenges and to turn risk predictions into disease-preventive actions in a wider social context. Given the l...

A billion-dollar question is whether precision medicine (aka personalized, "P4" or systems medicine) can substantially increase the utility of individualized disease prevention and population health . In this respect, the first results from the "Pioneer 100 Wellness Project (P100)" featured in last August's Nature Biotechnology issue, is a landmark . The study sheds light on an approach that has primarily existed as a vision and precedes the US National Institutes of Health's (NIH; Bethesda, MD) "All of Us Study", which will include a million participants in a similar scheme (). The researchers behind the study claim to demonstrate how measurement of personal data clouds over time can improve our understanding of health and disease and to identify "actionable possibilities", by which individuals can enhance health through preventive strategies. P100 is an exploratory study of associations in networks of biomarkers and risk factors established from large and dynamic data clouds of 108 participants. Over the course of a 9month period, participants underwent whole genome sequencing (yielding 127 polygenic scores for disease risks plus three copy number variations), three-times testing of metabolome (643 metabolites), proteome (262 proteins) and microbiome (4616 taxonomic units), 218 other clinical tests and measurements, as well as daily activity tracking via "quantified self" technologies. Associations between these biomarkers yielded a total of 3470 connections in a correlation network. The P100 study is a project designed to display the potentials for novel technologies and gather support for precision or P4 medicine (predictive, preventive, personalized and participatory), and it has gained prominent publicity in Nature 5 . The P100 will also be scaled up to include 100,000 participants in the 100K Wellness Project 3 . Against this background, we here discuss whether the P100 study actually supports the prospect of substantial benefits from datadriven disease prevention, and argue that it exposes severe challenges. The graphics of the correlational networks presented by Price et al. do offer an interesting research potential for exploring connections in molecular networks and for identifying candidate biomarkers 3 . However, as yet there are only a few examples where such This is a preprint version of a correspondence article published in Nature Biotechnology. Please see the following link for the final version: .

Feedback loops and other types of information processing structures play a pivotal role in maintaining the internal milieu of living organisms. Although methods of biomedical cybernetics and systems biology help to translate between the structure and function of processing structures, computer simulations are necessary for studying nonlinear systems and the full range of dynamic responses of feedback control systems. Currently, available approaches for modelling and simulation comprise basically domain-specific environments, toolkits for computer algebra systems and custom software written in universal programming languages for a specific purpose, respectively. All of these approaches are faced with certain weaknesses. We therefore developed a cross-platform class library that provides versatile building bricks for writing computer simulations in a universal programming language (CyberUnits Bricks). It supports the definition of models, the simulative analysis of linear and nonlinear systems in the time and frequency domain and the plotting of block diagrams. We compared several programming languages that are commonly used in biomedical research (S in the R implementation and Python) or that are optimized for speed (Swift, C++ and Object Pascal). In benchmarking experiments with two prototypical feedback loops, we found the implementations in Object Pascal to deliver the fastest results. CyberUnits Bricks is available as open-source software that has been optimised for Embarcadero Delphi and the Lazarus IDE for Free Pascal.

Chronic diseases are disorders of long duration and generally slow progression . They include four major non-communicable diseases (NCDs) listed by the World Health Organization (WHO) [2] -cardiovascular diseases, cancer, chronic respiratory diseases and diabetes -as well as other NCDs, such as neuropsychiatric disorders [3] and arthritis. As survival rates have improved for infectious and genetic diseases, chronic diseases have come to include communicable diseases (such as HIV/AIDS) and genetic disorders (such as cystic fibrosis). NCDs represent the major global health problem of the 21st century ; they affect all age groups [6] and their burden is greater than that of infectious diseases. NCDs are the world leading cause of disease burden and mortality [2] and are increasing in prevalence and burden , even in low-and middleincome countries . Costs incurred by uncontrolled NCDs are substantial, especially in underserved populations [9] and low-and middle-income countries . NCDs are an under-appreciated cause of poverty and hinder economic development . Importantly, management of NCDs has recently been prioritized globally (Box 1). Chronic diseases are caused by complex geneenvironment interactions acting across the lifespan from the fetus to old age (Figure ). In this context, 'environment' includes risk and protective factors associated with environment and lifestyle, such as

Introduction: This forward-thinking review will introduce microphysiological systems (MPS), also known as organs-on-chips or tissue chips and their potential utility in personalized medicine. The authors will explain how this technology could push precision medicine efforts forward, using scenarios of current and projected use of MPS in different disease and treatment scenarios. Areas covered: The research discussed will include highlights of the last half decade of MPS development, with background information on microphysiological systems including within the context of precision medicine. This review will cite examples on cancer, neurological disorders and rare disease scenarios where MPS could be integrated into the decision-making process for physicians and healthcare providers. Expert commentary: This review will discuss how microphysiological systems could be used in a variety of situations for medical diagnostics, understanding disease mechanisms, and development of therapeutic treatment plans. A useful place in precision medicine efforts exists for MPS to help eliminate some of the 'guesswork' in standard therapeutic recommendations, and to inform future treatment regimens for subgroups of individuals based on the systems biology and-omics-based readouts these platforms can produce.

Intentional bacterial infections can produce efficacious anti-tumor responses in mice, rats, dogs and humans. However, low overall success rates and intense side-effects prevent such approaches from being employed clinically. In this work, we titered bacteria and/or the pro-inflammatory cytokine TNF-α in a set of established murine models of cancer. To interpret the experiments conducted, we considered and calibrated a tumor-effector cell recruitment model under the influence of functional tumor-associated vasculature. In this model, bacterial infections and TNF-α enhanced immune activity and altered vascularization in the tumor bed. Information to predict bacterial therapy outcomes was provided by pre-treatment tumor size and the underlying immune recruitment dynamics. Notably, increasing bacterial loads did not necessarily produce better long-term tumor control, suggesting that tumor sizes affected optimal bacterial loads. Short-term treatment responses were favored by high concen...

Chronic diseases (i.e., Noncommunicable Diseases), mainly cardiovascular disease, cancer, respiratory diseases and type-2-diabetes, are now the leading cause of death, disability and diminished quality of life on the planet. Moreover, these diseases are also a major financial burden worldwide, significantly impacting the economy of many countries. Healthcare systems and medicine have progressively improved upon the ability to address infectious diseases and react to adverse health events through both surgical interventions and pharmacology; we have become efficient in delivering reactive care (i.e., initiating interventions once an individual is on the verge of or has actually suffered a negative health event). However, with slowly progressing and often &#39;silent&#39; chronic diseases now being the main cause of illness, healthcare and medicine must evolve into a proactive system, moving away from a merely reactive approach to care. Minimal interactions among the specialists and l...

BackgroundChronic obstructive pulmonary disease (COPD) patients often show skeletal muscle dysfunction that has a prominent negative impact on prognosis. The study aims to further explore underlying mechanisms of skeletal muscle dysfunction as a characteristic systemic effect of COPD, potentially modifiable with preventive interventions (i.e. muscle training). The research analyzes network module associated pathways and evaluates the findings using independent measurements.MethodsWe characterized the transcriptionally active network modules of interacting proteins in the vastus lateralis of COPD patients (n = 15, FEV146 ± 12% pred, age 68 ± 7 years) and healthy sedentary controls (n = 12, age 65 ± 9  years), at rest and after an 8-week endurance training program. Network modules were functionally evaluated using experimental data derived from the same study groups.ResultsAt baseline, we identified four COPD specific network modules indicating abnormalities in creatinine metabolism, ...

Epithelial abnormality during the transformation of oral submucous fibrosis (OSF) into oral squamous cell carcinoma has been well studied and documented. However, the differential contribution of atrophy and hyperplasia for malignant potentiality of OSF is yet to be resolved. Existing diagnostic conjectures lack precise diagnostic attributes which may be effectively resolved by substantiation of specific molecular pathology signatures. Present study elucidates existence of cellular competitiveness in OSF conditions using computer-assisted neighbourhood analysis in quantitative immunohistochemistry (IHC) framework. The concept of field cancerization was contributory in finding correspondence among neighbouring cells of epithelial layers with reference to differential expression of cardinal cancer-related genes [c-Myc (oncogene), p53 (tumour suppressor), and HIF-1α (hypoxia regulator)] which are known to be important sensors in recognizing cellular competitive interface. Our analyses ...

Medical images form an essential source of information for various important tasks such as diagnosis of diseases, surgical planning, medical reference, research and training. Oral submucous fibrosis [OSMF] is a chronic debilitating disease of the oral cavity characterized by inflammation and progressive fibrosis of the submucosal tissues. Support Vector Machine [SVM] is a statistic machine learning technique that has been successfully applied in the pattern recognition and is based on the principle of structural risk minimization. In this paper a histogram based feature extraction technique has been proposed to classify normal images and OSMF affected images using SVM. An attempt is made to provide an enhanced knowledge about computer aided diagnosis of this potentially malignant disorder, to health care providers in order to help in differentiating the OSMF affected tissue from normal. Experiments showed significantly satisfactory results with an accuracy of 94%.

Neuronal nicotinic acetylcholine receptors containing the α9 or the α9 and α10 subunits are expressed in various extra-neuronal tissues. Moreover, most cancer cells and tissues highly express α9-containing receptors, and a number of studies have shown that they are powerful regulators of responses that stimulate cancer processes such as proliferation, inhibition of apoptosis, and metastasis. It has also emerged that their modulation is a promising target for drug development. The aim of this review is to summarize recent data showing the involvement of these receptors in controlling the downstream signaling cascades involved in the promotion of cancer.

Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-&#39;omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-&#39;omics signatures of disease states. The framework is divided into four major steps: dataset subsetting, feature filtering, &#39;omics-based clustering and biomarker identification. We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-&#39;omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. This framework will help health researchers plan and perform mu...

The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative dr...

In this essay I suggest that we view design principles in systems biology as minimal models, for a design principle usually exhibits universal behaviors that are common to a whole range of heterogeneous (living and nonliving) systems with different underlying mechanisms. A well-known design principle in systems biology, integral feedback control, is discussed, showing that it satisfies all the conditions for a model to be a minimal model. This approach has significant philosophical implications: it not only accounts for how design principles explain, but also helps clarify one dispute over design principles, e.g., whether design principles provide mechanistic explanations or a distinct kind of explanations called design explanations.