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During the last decade, organoid and organs-on-chip technologies have significantly enhanced the ability to model human biology in vitro. For the pharmaceutical industry, this represents an opportunity to augment, or possibly replace, traditional preclinical animal studies with more clinically predictive tools. In the last few years, the marketplace for new human model systems has expanded rapidly. While pharma companies welcome the breadth of new options, ample choice can be paralyzing. Even for experts from the model developer community who are now filling the ranks in the industry, the pairing of the right model for a specific, fit-for-purpose biological question can be daunting. As a community, the adoption of these models can be hastened in the industry by publishing high dimensional datasets (e.g., multiomic, imaging, functional, etc.) on existing model systems, termed model-omics, and storing them in publicly accessible databases. This action will allow for quick cross-model comparisons and provide a sought-after rationale for either routine or fit-for-purpose use of organoids or organs-on-chip during drug development., (© 2023 Genentech, Inc. Advanced Biology published by Wiley-VCH GmbH.)

Natural medicine has been widely used for clinical treatment and health care in many countries and regions. Additionally, extracting active ingredients from traditional Chinese medicine and other natural plants, defining their chemical structure and pharmacological effects, and screening potential druggable candidates are also uprising directions in new drug research and development. Physiologically based pharmacokinetic (PBPK) modeling is a mathematical modeling technique that simulates the absorption, distribution, metabolism, and elimination of drugs in various tissues and organs in vivo based on physiological and anatomical characteristics and physicochemical properties. PBPK modeling in drug research and development has gradually been recognized by regulatory authorities in recent years, including the U.S. Food and Drug Administration. This review summarizes the general situation and shortcomings of the current research on the pharmacokinetics of natural medicine and introduces the concept and the advantages of the PBPK model in the study of pharmacokinetics of natural medicine. Finally, the pharmacokinetic studies of natural medicine using the PBPK models are summed up, followed by discussions on the applications of PBPK modeling to the enzyme-mediated pharmacokinetic changes, special populations, new drug research and development, and new indication adding for natural medicine. This paper aims to provide a novel strategy for the preclinical research and clinical use of natural medicine.

Contemporary medicine is characterized by an increasing subspecialization and the acquisition of a greater knowledge about the interaction among the different body structures (biosemiotics), both in health and disease. This article proposes a new conceptualization of the body based on considering it as a biological space (cells, tissues, and organs) and a biosemiotic space (exchange of signs among them). Its development would lead to a new subspecialty focused on the study and interference of disease biosemiotics (biosemiotic medicine), which would trigger a processbased medicine centered on early diagnosis and management of disease., Competing Interests: None, (Sociedad Argentina de Pediatría.)

The fusion of artificial neural networks and fuzzy logic systems allows researchers to model real-world problems through the development of intelligent and adaptive systems. Artificial neural networks are able to adapt and learn by adjusting the interconnections between layers, while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as "neuro-fuzzy" systems. In this paper, the basic elements of both approaches are analyzed, noticing that this blending could be applied for pattern recognition in medical applications.

From September-December 2017, the Mathematical Biosciences Institute at Ohio State University hosted a series of workshops on control theory in biology and medicine, including workshops on control and modulation of neuronal and motor systems, control of cellular and molecular systems, control of disease / personalized medicine across heterogeneous populations, and sensorimotor control of animals and robots. This special issue presents tutorials and research articles by several of the participants in the MBI workshops.

A historical assessment of the origin of the dose-response in modern toxicology and its integration as a central concept in biology and medicine is presented. This article provides an overview of how the threshold, linear and biphasic (i.e., hormetic) dose-response models emerged in the late 19th and early 20th centuries and competed for acceptance and dominance. Particular attention is directed to the hormetic model for which a general description and evaluation is provided, including its historical basis, and how it was marginalized by the medical and pharmacology communities in the early decades of the 20th century., Competing Interests: The author declares no conflict of interest.

The "first digit law" or "Benford's law" is a mathematical distribution discovered by Simon Newcomb and Frank Benford. It states, that the probability of the leading number d (d∈{1,…,9}) in many natural datasets follows: P (d)=log 10 (d+1)-log 10 (d)=log 10 (1+1/d). It was successfully used through tax authorities and "forensic accounting" in order to detect fraud and other irregularities. Benfords law was almost neglected for its use outside financial accounting. The planning for health care systems in developing countries is extremely dependant on good, valid data. Whether you plan the catchment area for the future district hospitals, the number of health posts, the staff establishment for the central hospital or the drug budget in the Ministry. The "first digit law" can be used in medicine, public health, physiology and development aid to unmask questionable data, to discover unexpected challenges, difficulties in the data collection process, loss through corruption and criminal fraud. Our hypothesis suggests, that the "first digit law" is a cost effective tool, which is easy to use for most people in the medical profession, which does not really needs complicated statistical software and can be used on the spot, even in the resource restricted conditions of developing countries. Several preconditions (like the size of the data set and its reach over more than two dimensions) have to be fulfilled, but then Benfords law can be used by any clinician, physiologist, public health specialist or aid consultant without difficulties and without deeper statistical knowledge in the four steps, we suggest in this article. The consequences will be different depending on the level (local regional, national, continental, international) on which you will use the law. All levels will be enabled to get insight into the validity of the data-challenges for the other levels without the help of trained statisticians or accountants. We believe that the "first digit law" is a vastly underestimated and neglected, but extremely useful tool for the identification of unexpected challenges, supervision and control in various parts of medicine and public health for almost all aspects of development aid., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.

Systems medicine is the application of systems biology concepts, methods, and tools to medical research and practice. It aims to integrate data and knowledge from different disciplines into biomedical models and simulations for the understanding, prevention, cure, and management of complex diseases. Complex diseases arise from the interactions among disease-influencing factors across multiple levels of biological organization from the environment to molecules. To tackle the enormous challenges posed by complex diseases, we need a modeling and simulation framework capable of capturing and integrating information originating from multiple spatiotemporal and organizational scales. Multiscale modeling and simulation in systems medicine is an emerging methodology and discipline that has already demonstrated its potential in becoming this framework. The aim of this chapter is to present some of the main concepts, requirements, and challenges of multiscale modeling and simulation in systems medicine.

The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

The development of new drug therapies requires substantial and ever increasing investments from the pharmaceutical company. Ten years ago, the average time from early target identification and optimization until initial market authorization of a new drug compound took more than 10 years and involved costs in the order of one billion US dollars. Recent studies indicate even a significant growth of costs in the meanwhile, mainly driven by the increasing complexity of diseases addressed by pharmaceutical research.Modeling and simulation are proven approaches to handle highly complex systems; hence, systems medicine is expected to control the spiral of complexity of diseases and increasing costs. Today, the main focus of systems medicine applications in industry is on mechanistic modeling. Biological mechanisms are represented by explicit equations enabling insight into the cooperation of all relevant mechanisms. Mechanistic modeling is widely accepted in pharmacokinetics, but prediction from cell behavior to patients is rarely possible due to lacks in our understanding of the controlling mechanisms. Data-driven modeling aims to compensate these lacks by the use of advanced statistical and machine learning methods. Future progress in pharmaceutical research and development will require integrated hybrid modeling technologies allowing realization of the benefits of both mechanistic and data-driven modeling. In this chapter, we sketch typical industrial application areas for both modeling techniques and derive the requirements for future technology development.

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.

Modeling is an integral component of modern biology. In this chapter we look into the role of the model, as it pertains to Systems Medicine, and the software that is required to instantiate and run it. We do this by comparing the development, implementation, and characteristics of tools that have been developed to work with two divergent methodologies: Systems Biology and Pharmacometrics. From the Systems Biology perspective we consider the concept of "Software as a Medical Device" and what this may imply for the migration of research-oriented, simulation software into the domain of human health.In our second perspective, we see how in practice hundreds of computational tools already accompany drug discovery and development at every stage of the process. Standardized exchange formats are required to streamline the model exchange between tools, which would minimize translation errors and reduce the required time. With the emergence, almost 15 years ago, of the SBML standard, a large part of the domain of interest is already covered and models can be shared and passed from software to software without recoding them. Until recently the last stage of the process, the pharmacometric analysis used in clinical studies carried out on subject populations, lacked such an exchange medium. We describe a new emerging exchange format in Pharmacometrics which covers the non-linear mixed effects models, the standard statistical model type used in this area. By interfacing these two formats the entire domain can be covered by complementary standards and subsequently the according tools.

Systems biology is currently making a bid to show that it is able to make an important contribution to personalised or precision medicine. In order to do so, systems biologists need to find a way of tackling the pervasive variability of biological systems that is manifested in the medical domain as inter-subject variability. This need is simultaneously social and epistemic: social as systems biologists attempt to engage with the interests and concerns of clinicians and others in applied medical research; epistemic as they attempt to develop new strategies to cope with variability in the validation of the computational models typical of systems biology. This paper describes one attempt to develop such a strategy: a trial with a population-of-models approach in the context of cardiac electrophysiology. I discuss the development of this approach against the background of ongoing tensions between mathematically and experimentally inclined modellers on the one hand, and attempts to forge new collaborations with medical scientists on the other. Apart from the scientific interest of the population-of-models approach for tackling variability, the trial also offers a good illustration of the epistemology of experiment-facing modelling. I claim that it shows the extent to which experiment-facing modelling and validation require the establishment of criteria for comparing models and experiments that enable them to be linked together. These 'grounds of comparability' are the broad framework in which validation experiments are interpreted and evaluated by all the disciplines in the collaboration, or being persuaded to participate in it. I claim that following the process of construction of the grounds of comparability allows us to see the establishment of epistemic norms for judging validation results, through a process of 'normative intra-action' (Rouse, 2002) that shape the social and epistemic evolution of systems approaches to biomedicine., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Medicine's cardinal diagnostic and therapeutic resource is the clinical encounter. Over the last two centuries and particularly over the last five decades the function of the clinical encounter has been eroded to the point of near irrelevance because of the atomized and atomizing influence of technology and microspecialization. Meanwhile, over the past five decades the exceptionalist view of Homo sapiens inherent in the social and religious traditions of the West has similarly undergone radical changes. H. sapiens is now best understood as a microecosystem integrated into a much broader ecosystem: the biosphere. That human microecosystem is composed of constituents derived from the archaeal, bacterial, and eukaryan domains via endosymbiotic, commensalistic and mutualistic interactions. This amalgamation of 100 trillion cells and viral elements is regulated by a composite genome aggregated over the 3.8 billion years of evolutionary history of organic life. No component of H. sapiens or its genome can be identified as irreducibly and exclusively human. H. sapiens' humanity is an emergent property of the microecosystem. Ironically as H. sapiens is viewed by evolutionary science in a highly integrated manner medicine approaches it as a balkanized, deaggregated entity through the eye of 150 different specialties. To effectively address the needs of H sapiens in its role as patient by the same species in its role as physician the disparate views must be harmonized. Here I review some conceptual elements that would assist a physician in addressing the needs of the patient in integrum, as a microecosystem, by the former address the latter as a historical gestalt being. The optimal way to recover the harmony between patient and physician is through a revitalization of the clinical encounter via an ecological and Darwinian epistemology.

Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.

Chemoprevention is effective in inhibiting the onset of cancer in experimental animal models, but the transferability of similar results to humans is questionable. Therefore, reliable intermediate molecular biomarkers are needed to evaluate the efficacy of chemopreventive agents before the onset of cancer. The use of genomic biomarkers is limited by their poor predictive value. Although post-genomic biomarkers (i.e., gene-expression analyses) are useful for evaluating the safety, efficacy, and mechanistic basis of chemopreventive agents, the biomarkers are often poorly related to the phenotype, due to posttranscriptional regulation. Proteome analyses can evaluate preclinical phenotype alterations, but only at low protein counts. MicroRNA alterations, which are essential for the development of cancer, may be modulated by chemopreventive agents. Furthermore, microRNA delivery may be used to counteract carcinogenesis. Exposure to cigarette smoke induces microRNA let-7 downregulation and cell proliferation that can be converted to cell growth arrest and apoptosis upon let-7a transfection. Therefore, microRNAs are reliable biomarkers for evaluating chemoprevention efficacy and may be used to counteract carcinogenesis., (© 2012 New York Academy of Sciences.)

During the last 10 years, a series of exciting observations has led to a new theory of pathophysiology using insights from evolutionary biology and neuroendocrine immunology to understand the sequelae of chronic inflammatory disease. According to this theory, disease sequelae can be explained based on redirection of energy-rich fuels from storage organs to the activated immune system. These disease sequelae are highly diverse and include the following: sickness behavior, anorexia, malnutrition, muscle wasting-cachexia, cachectic obesity, insulin resistance with hyperinsulinemia, dyslipidemia, increase of adipose tissue near inflamed tissue, alterations of steroid hormone axes, elevated sympathetic tone and local sympathetic nerve fiber loss, decreased parasympathetic tone, hypertension, inflammation-related anemia, and osteopenia. Since these disease sequelae can be found in many animal models of chronic inflammatory diseases with mammals (e.g., monkeys, mice, rats, rabbits, etc.), the evolutionary time line goes back at least 70 million years. While the initial version of this theory could explain prominent sequelae of chronic inflammatory disease, it did not however address two features important in the pathogenesis of immune-mediated diseases: the time point when an acute inflammatory disease becomes chronic, and the appearance of hypertension in chronic inflammation. To address these aspects more specifically, a new version of the theory has been developed. This version defines more precisely the moment of transition from acute inflammatory disease to chronic inflammatory disease as a time in which energy stores become empty (complete energy consumption). Depending on the amount of stored energy, this time point can be calculated to be 19-43 days. Second, the revised theory addresses the mechanisms of essential hypertension since, on the basis of water loss, acute inflammatory diseases can stimulate water retention using a positively selected water retention system (identical to the energy provision system). In chronic smoldering inflammation, however, there is no increased water loss. In contrast, there is increased water generation in inflamed tissue and inflammatory cells, and the activation of the water retention system persists. This combination leads to a net increase of the systemic fluid volume, which is hypothesized to be the basis of essential hypertension (prevalence in adults 22-32%).

During the last few decades, conventional practices in medicine including oncology focus their interests towards the reductionism of molecular detailing and at the analytical level population-based assessment with stochastic principles, technically called 'evidence-based medicine', is generally practiced. Due to fluctuations in physiological parameters, the analysis and prediction of a therapeutic outcome in cancer on an individual level is uncertain. In recent times the well accepted opinion is that cancer should be looked upon as a systems disorder. This makes a paradigm shift - from a fragmented to a systems approach, linear to nonlinear methodology and from genome to physiome based analysis to understand the cancer. In the arena of systems biology, different groups have different views, namely, bottom-up (mechanistic), top-down (operational) and middle-out (rational). With respect to cancer each has a special relevance to serve the specific objectivity. In this article we have reviewed the views of the different schools, recent developments and controversies associated with the uncertainties in prediction of the therapeutic outcome of cancer. Recent advances in dynamical science and control theory may provide suitable analytical tools for capturing the uncertainties associated with cancer therapy through the development of middle-out rationalist (MORA) views.

Until fairly recently, medical publications have been handicapped by being restricted to non-electronic formats, effectively preventing the dissemination of complex audiovisual and three-dimensional data. However, authors and readers could significantly profit from advances in electronic publishing that permit the inclusion of multimedia content directly into an article. For the first time, the de facto gold standard for scientific publishing, the portable document format (PDF), is used here as a platform to embed a video and an audio sequence of patient data into a publication. Fully interactive three-dimensional models of a face and a schematic representation of a human brain are also part of this publication. We discuss the potential of this approach and its impact on the communication of scientific medical data, particularly with regard to electronic and open access publications. Finally, we emphasise how medical teaching can benefit from this new tool and comment on the future of medical publishing.

Background: Transfusion-related acute lung injury (TRALI) is hypothesized to be a "two-hit" entity, in which an inflammatory condition (e.g., sepsis) predisposes to TRALI. TRALI is a clinical diagnosis. Disciplines involved in managing TRALI may differ in decision-making on the reporting of TRALI., Study Design and Methods: A survey was conducted among critical care physicians, hematologists, hemovigilance workers, and transfusion medicine physicians, using case vignettes and a questionnaire. The vignettes varied in patient- and blood product-related factors that may influence the decision to report a TRALI case. Multiple linear regression analysis was performed. A positive beta-coefficient is in favor of reporting., Results: Ninety-two questionnaires were returned (response rate, 68%). For all disciplines, preferences in favor of reporting TRALI were onset of symptoms within 1 hour (beta = 0.4), after transfusion of a single unit of FFP (beta = 0.5), and in the absence of acute lung injury before transfusion (beta = 1.3). An admission diagnosis of sepsis was a negative preference (beta = -0.3). Massive transfusion (6 RBC plus 4 FFP units) was a negative preference for transfusion medicine physicians (beta = -0.3), but a positive preference for the other disciplines. The questionnaire revealed that massive transfusion and the age of blood products were considered relatively more important reasons to report TRALI by critical care physicians compared to the other disciplines (p < 0.05)., Conclusion: A pretransfusion inflammatory condition is a reason to withhold from reporting of a suspected TRALI case. Disciplines involved in managing TRALI differ in decision-making of reporting TRALI, which may contribute to variance in incidence.

The following report selects and summarises some of the conclusions and recommendations generated throughout a series of workshops and discussions that have lead to the publication of the Science Policy Briefing (SPB) Nr. 35, published by the European Science Foundation. (Large parts of the present text are directly based on the ESF SPB. Detailed recommendations with regard to specific application areas are not given here but can be found in the SPB. Issues related to mathematical modelling, including training and the need for an infrastructure supporting modelling are discussed in greater detail in the present text.)The numerous reports and publications about the advances within the rapidly growing field of systems biology have led to a plethora of alternative definitions for key concepts. Here, with 'mathematical modelling' the authors refer to the modelling and simulation of subcellular, cellular and macro-scale phenomena, using primarily methods from dynamical systems theory. The aim of such models is encoding and testing hypotheses about mechanisms underlying the functioning of cells. Typical examples are models for molecular networks, where the behaviour of cells is expressed in terms of quantitative changes in the levels of transcripts and gene products. Bioinformatics provides essential complementary tools, including procedures for pattern recognition, machine learning, statistical modelling (testing for differences, searching for associations and correlations) and secondary data extracted from databases.Dynamical systems theory is the natural language to investigate complex biological systems demonstrating nonlinear spatio-temporal behaviour. However, the generation of experimental data suitable to parameterise, calibrate and validate such models is often time consuming and expensive or not even possible with the technology available today. In our report, we use the term 'computational model' when mathematical models are complemented with information generated from bioinformatics resources. Hence, 'the model' is, in reality, an integrated collection of data and models from various (possibly heterogeneous) sources. The present report focuses on a selection of topics, which were identified as appropriate case studies for medical systems biology, and adopts a particular perspective which the authors consider important. We strongly believe that mathematical modelling represents a natural language with which to integrate data at various levels and, in doing so, to provide insight into complex diseases: 1. Modelling necessitates the statement of explicit hypotheses, a process which often enhances comprehension of the biological system and can uncover critical points where understanding is lacking. 2. Simulations can reveal hidden patterns and/or counter-intuitive mechanisms in complex systems. 3. Theoretical thinking and mathematical modelling constitute powerful tools to integrate and make sense of the biological and clinical information being generated and, more importantly, to generate new hypotheses that can then be tested in the laboratory.Medical Systems Biology projects carried out recently across Europe have revealed a need for action: 4. While the need for mathematical modelling and interdisciplinary collaborations is becoming widely recognised in the biological sciences, with substantial implications for the training and research funding mechanisms within this area, the medical sciences have yet to follow this lead. 5. To achieve major breakthroughs in Medical Systems Biology, existing academic funding schemes for large-scale projects need to be reconsidered. 6. The hesitant stance of the pharmaceutical industry towards major investment in systems biology research has to be addressed. 7. Leading medical journals should be encouraged to promote mathematical modelling.

Developments in radiotherapy treatment planning and optimization by medical physicists and the American Association of Physicists in Medicine are reviewed, with emphasis on recent work in optimization. It is shown that medical physicists have played a vital role in the creation of innovative treatment planning techniques throughout the past century, most significantly since the advent of computerized tomography for three-dimensional (3D) imaging and high-powered computers capable of 3D planning and optimization. Some early advances in 3D planning made by physicists include development of novel planning algorithms, beam's-eye-view, virtual simulation, dose-volume histogram analysis tools, and bioeffect modeling. Most of the recent developments have been driven by the need to develop treatment planning for conformal radiotherapy, especially intensity modulated radiation therapy. These advances include inverse planning, handling the effects of motion and uncertainty, biological planning, and multicriteria optimization.

Making a prognosis is one of the primary functions of the medical profession. At the end of the nineteenth century prognostication took up approximately ten percent of medical textbooks, by 1970 this had fallen to nearly zero. Given medical technology's awesome ability to prolong the process and suffering of dying today's patients need to know their prognosis in order to make choices about their treatment options. Whilst precise predictions of the future are obviously not possible, relatively simple mathematical modelling techniques can make reasonable estimates of likely outcomes for individual patients. The life expectancy of a patient of any age with any illness can be estimated provided the disease-specific mortality of the illness is known. Decision analysis or logistic regression models can then be used to determine the risks and benefits of various treatment options. A patient's prognosis does not just depend on their age and primary diagnosis, but also on the severity of their illness, their functional capacity both prior to and during the illness and the number of co-morbidities also suffered from. Several predictive instruments have been developed to help simplify the prediction of the outcome of individual patients. There are conflicting reports on how these models compare with doctors' intuition--whatever their strengths and weaknesses it is unlikely that they worsen clinical judgement. Therefore, all doctors should become familiar with them and use them appropriately.

There exists a sense of urgency to begin to generate a cohesive assembly of biomedical knowledge as the pace of knowledge accumulation accelerates. The urgency is in part driven by the emergence of systems molecular medicine that emphasizes the combination of systems analysis and molecular dissection in the future of medical practice and research. A potentially powerful approach is to build integrative pathway knowledge bases that link organ systems function with molecules.

Mathematical models are finding an increasing use in bio-medical scientific investigations as effective means of putting the interpretation of biological phenomena on a more quantitative basis. Besides the well established mathematical paradigm based on differential equations, another approach that takes full advantage of the steadily increasing computing power, is gaining increasing consensus: micro-simulation. Micro-simulation is based on the idea of mimicking the behavior of the system under investigation through the specification of the rules of interaction among its individual constituents. This rule-driven (sometimes called equation-free) approach allows a smoother upgrade of models sophistication and reduces the gap between the abstract level of description typical of mathematical models and the complexity of the biological world. In this article we aim at illustrating, through specific examples, some of the potential advantages that micro-simulation has to offer in order to gain a better grasp and understanding of complex phenomena in biology and medicine.

Background: During the past few decades, research has reported gender bias in various areas of clinical and academic medicine. To prevent such bias, a gender perspective in medicine has been requested, but difficulties and resistance have been reported from implementation attempts. Our study aimed at analysing this resistance in relation to what is considered good medical research., Method: We used a theoretical model, based on scientific competition, to understand the structures of scientific medicine and how they might influence the resistance to a gender perspective in medicine. The model was originally introduced to discuss how pluralism improves rationality in the social sciences., Results: The model provided a way to conceptualise different fields of research in medicine: basic research, applied research, medical philosophy, and 'empowering' research. It clarified how various research approaches within medicine relate to each other, and how they differ and compete. It also indicated why there might be conflicts between them: basic and applied research performed within the biomedical framework have higher status than gender research and other research approaches that are performed within divergent research paradigms., Conclusion: This hierarchy within medical research contributes to the resistance to a gender perspective, causing gender bias and making medical scientific rationality suboptimal. We recommend that the theoretical model can be applied in a wider medical context when different and hierarchically arranged research traditions are in conflict. In this way, the model might contribute to shape a medical community where scientific pluralism is acknowledged to enlarge, not to disturb, the scientific rationality of medicine.

In order to place pharmaco-EEG within the clinical context, the distinction between biomarkers, surrogate endpoints, clinical endpoints and clinical outcomes is introduced. State-of-the-art applications of pharmaco-EEG, together with pharmacokinetic-pharmacodynamic modeling in everyday clinical practice in anesthesiology (semilinear canonical correlation), psychiatry (discrimination between responders and nonresponders to pharmacological treatment using the test dose), neurology (antiepileptic field) and neurophysiology (first-order Markov model of sleep stage transitions) are discussed. The combination of both procedures, although successfully used during some drug development programs (opioids or benzodiazepines), is not widely applied in the clinical scenario where the central nervous system (CNS) is concerned. Much work is still need to develop fully the potentials that pharmaco-EEG together with pharmacokinetic-pharmacodynamic modeling could bring to therapeutics in neuroscience.

The theory of Evolution is being invoked frequently in medicine to account for counter-intuitive findings such as programmed cell death in congestive heart failure and the fact that fever can prove both blessing or curse, depending on the circumstances. These and other examples of what might be called maladaptive adaptations are discussed, and it is suggested that human development may have reached a stage where the roles of mutation and selection are drastically changed. By creating an environment both mutagenic and protective, we have altered the balance between the two great driving forces of evolution, increasing the frequency of mutations and reducing the need for adaptation. As a result, new diseases have arisen and the whole evolutionary process seems to have lost some of its benevolence, no longer insuring the survival of the fittest. We must entertain the possibility that Darwin's theory cannot explain the last few millennia of human evolution and is now useful chiefly as an approximation applicable to very long periods of time.

Medical or biochemical data are often expressed as percentages or proportions. Since the sampling error of percentage values does not usually follow a normal (Laplace-Gauss) model, statistical significance tests may give unsatisfactory results, when applied to the original data; however, the fitting to a normal distribution can be improved by choosing suitable metameters. The Author proposes the transformation of percentages to a hyperbolic function zp, based on Fisher's z, which is almost normally distributed. The performances of this transformation are checked, with special reference to medical laboratory problems.

Genes and environment interact continuously throughout life, from the moment of conception, to regulate and affect all aspects of human structure and function. The normal rules of mendelian inheritance, ie, the transmission of genes over the generations, are usually best studied by observation of abnormal mutations, especially in plants and animals under precise breeding conditions. The study of such didactic models as the guinea pig crosses discussed here is a prerequisite to an understanding of mendelian genetics in man and the terms used in other articles to appear in this series.