Wiley Interdisciplinary Reviews-Systems Biology and Medicine最新文献

A cell's ability to change shape is one of the most fundamental biological processes and is essential for maintaining healthy organisms. When the ability to control shape goes awry, it often results in a diseased system. As such, it is important to understand the mechanisms that allow a cell to sense and respond to its environment so as to maintain cellular shape homeostasis. Because of the inherent complexity of the system, computational models that are based on sound theoretical understanding of the biochemistry and biomechanics and that use experimentally measured parameters are an essential tool. These models involve an inherent feedback, whereby shape is determined by the action of regulatory signals whose spatial distribution depends on the shape. To carry out computational simulations of these moving boundary problems requires special computational techniques. A variety of alternative approaches, depending on the type and scale of question being asked, have been used to simulate various biological processes, including cell motility, division, mechanosensation, and cell engulfment. In general, these models consider the forces that act on the system (both internally generated, or externally imposed) and the mechanical properties of the cell that resist these forces. Moving forward, making these techniques more accessible to the non-expert will help improve interdisciplinary research thereby providing new insight into important biological processes that affect human health. This article is categorized under: Cancer > Cancer>Computational Models Cancer > Cancer>Molecular and Cellular Physiology.

Generating needed cell types using cellular reprogramming is a promising strategy for restoring tissue function in injury or disease. A common method for reprogramming is addition of one or more transcription factors that confer a new function or identity. Advancements in transcription factor selection and delivery have culminated in successful grafting of autologous reprogrammed cells, an early demonstration of their clinical utility. Though cellular reprogramming has been successful in a number of settings, identification of appropriate transcription factors for a particular transformation has been challenging. Computational methods enable more sophisticated prediction of relevant transcription factors for reprogramming by leveraging gene expression data of initial and target cell types, and are built on mathematical frameworks ranging from information theory to control theory. This review highlights the utility and impact of these mathematical frameworks in the field of cellular reprogramming. This article is categorized under: Reproductive System Diseases > Reproductive System Diseases>Genetics/Genomics/Epigenetics Reproductive System Diseases > Reproductive System Diseases>Stem Cells and Development Reproductive System Diseases > Reproductive System Diseases>Computational Models.

Thermoregulation plays a vital role in homeostasis. Many species of animals as well as humans have evolved various physiological mechanisms for body temperature control, which are characteristically flexible and enable a fine-tuned spatial and temporal regulation of body temperature in different environmental conditions and circumstances. Human beings normally maintain a core body temperature at around 37°C, and maintenance of this relatively high temperature is critical for survival. Therefore, principles of thermoregulatory control have also important clinical implications. Infections can cause the body temperature to rise internally and several diseases can cause a dysfunction of thermoregulatory mechanisms. Moreover, the utilization of thermotherapies in treating various diseases has been known for thousands of years with a recent resurgence of interest. An increasing amount of research suggests that targeted temperature management is of paramount importance to patient outcomes in certain clinical scenarios. We provide a concise summary of the basic concepts of thermoregulation. Emphasis is given to the principles of thermoregulation in humans in basic pathological states and to targeted temperature management strategies in the clinical environment, with special attention on therapeutic hypothermia in postcardiac arrest patients. Finally, the discussion is focused on the potential offered by computational thermophysiological models for predicting thermal responses of patients in various clinical circumstances, for proposing new perspectives in the design of novel thermal therapies, and to optimize targeted temperature management strategies. This article is categorized under: Cardiovascular Diseases > Cardiovascular Diseases>Computational Models Cardiovascular Diseases > Cardiovascular Diseases>Environmental Factors Cardiovascular Diseases > Cardiovascular Diseases>Biomedical Engineering.

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Epithelial to mesenchymal transition (EMT) is a complex biological process that plays a key role in cancer progression and metastasis formation. Its activation results in epithelial cells losing adhesion and polarity and becoming capable of migrating from their site of origin. At this step the disease is generally considered incurable. As EMT execution involves several individual molecular components, connected by nontrivial relations, in vitro techniques are often inadequate to capture its complexity. Computational models can be used to complement experiments and provide additional knowledge difficult to build up in a wetlab. Indeed in silico analysis gives the user total control on the system, allowing to identify the contribution of each independent element. In the following, two kinds of approaches to the computational study of EMT will be presented. The first relies on signal transduction networks description and details how changes in gene expression could influence this process, both focusing on specific aspects of the EMT and providing a general frame for this phenomenon easily comparable with experimental data. The second integrates single cell and population level descriptions in a multiscale model that can be considered a more accurate representation of the EMT. The advantages and disadvantages of each approach will be highlighted, together with the importance of coupling computational and experimental results. Finally, the main challenges that need to be addressed to improve our knowledge of the role of EMT in the neoplastic disease and the scientific and translational value of computational models in this respect will be presented. This article is categorized under: Analytical and Computational Methods > Computational Methods.

Direct imaging of metabolism in cells or multicellular organisms is important for understanding many biological processes. Raman scattering (RS) microscopy, particularly, coherent Raman scattering (CRS) such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS), has emerged as a powerful platform for cellular imaging due to its high chemical selectivity, sensitivity, and imaging speed. RS microscopy has been extensively used for the identification of subcellular structures, metabolic observation, and phenotypic characterization. Conjugating RS modalities with other techniques such as fluorescence or infrared (IR) spectroscopy, flow cytometry, and RNA-sequencing can further extend the applications of RS imaging in microbiology, system biology, neurology, tumor biology and more. Here we overview RS modalities and techniques for mammalian cell and tissue imaging, with a focus on the advances and applications of CARS and SRS microscopy, for a better understanding of the metabolism and dynamics of lipids, protein, glucose, and nucleic acids in mammalian cells and tissues. This article is categorized under: Laboratory Methods and Technologies > Imaging Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods.

Pro-inflammatory signals induce metabolic reprogramming in innate and adaptive immune cells of both myeloid and lymphoid lineage, characterized by a shift to aerobic glycolysis akin to the Warburg effect first described in cancer. Blocking the switch to aerobic glycolysis impairs the survival, differentiation, and effector functions of pro-inflammatory cell types while favoring anti-inflammatory and regulatory phenotypes. Glycolytic reprogramming may therefore represent a selective vulnerability of inflammatory immune cells, providing an opportunity to modulate immune responses in autoimmune disease without broad toxicity in other tissues of the body. The mechanisms by which aerobic glycolysis and the balance between glycolysis and oxidative phosphorylation regulate immune responses have only begun to be understood, with many additional insights expected in the years to come. Immunometabolic therapies targeting aerobic glycolysis include both pharmacologic inhibitors of key enzymes and glucose-restricted diets, such as the ketogenic diet. Animal studies support a role for these pharmacologic and dietary therapies for the treatment of autoimmune diseases, and in a few cases proof of concept has been demonstrated in human disease. Nonetheless, much more work is needed to establish the clinical safety and efficacy of these treatments. This article is categorized under: Biological Mechanisms > Metabolism Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling.

Epidemiological data indicate that long-term low dose aspirin administration has a protective effect against the occurrence of colorectal cancer, both in sporadic and in hereditary forms of the disease. The mechanisms underlying this protective effect, however, are incompletely understood. The molecular events that lead to protection have been partly defined, but remain to be fully characterized. So far, however, approaches based on evolutionary dynamics have not been discussed much, but can potentially offer important insights. The aim of this review is to highlight this line of investigation and the results that have been obtained. A core observation in this respect is that aspirin has a direct negative impact on the growth dynamics of the cells, by influencing the kinetics of tumor cell division and death. We discuss the application of mathematical models to experimental data to quantify these parameter changes. We then describe further mathematical models that have been used to explore how these aspirin-mediated changes in kinetic parameters influence the probability of successful colony growth versus extinction, and how they affect the evolution of the tumor during aspirin administration. Finally, we discuss mathematical models that have been used to investigate the selective forces that can lead to the rise of mismatch-repair deficient cells in an inflammatory environment, and how this selection can be potentially altered through aspirin-mediated interventions. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

A number of hormones and growth factors stimulate target cells via the second messenger pathways, which in turn regulate cellular phenotypes. Cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger that facilitates numerous signal transduction pathways; its production in cells is tightly balanced by ligand-stimulated receptors that activate adenylate cyclases (ACs), that is, "source" and by phosphodiesterases (PDEs) that hydrolyze it, that is, "sinks." Because it regulates various cellular functions, including cell growth and differentiation, gene transcription and protein expression, the cAMP signaling pathway has been exploited for the treatment of numerous human diseases. Reduction in cAMP is achieved by blocking "sources"; however, elevation in cAMP is achieved by either stimulating "source" or blocking "sinks." Here we discuss an alternative paradigm for the regulation of cellular cAMP via GIV/Girdin, the prototypical member of a family of modulators of trimeric GTPases, Guanine nucleotide Exchange Modulators (GEMs). Cells upregulate or downregulate cellular levels of GIV-GEM, which modulates cellular cAMP via spatiotemporal mechanisms distinct from the two most often targeted classes of cAMP modulators, "sources" and "sinks." A network-based compartmental model for the paradigm of GEM-facilitated cAMP signaling has recently revealed that GEMs such as GIV serve much like a "tunable valve" that cells may employ to finetune cellular levels of cAMP. Because dysregulated signaling via GIV and other GEMs has been implicated in multiple disease states, GEMs constitute a hitherto untapped class of targets that could be exploited for modulating aberrant cAMP signaling in disease states. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Signaling.

Knowledge about metabolism of immune cells increased almost exponentially during the last two decades and thereby created the new area immunometabolism. Increased glucose uptake and glycolysis were identified as one of the major drivers in immune cells for rapid adaptation to changes in the microenvironment or external stimuli. These metabolic switches are crucial to generate macromolecules for immune cell proliferation and activation. Glucose transporter 1 (GLUT1), a ubiquitously expressed glucose transporter, is strongly upregulated after innate and adaptive immune cell activation. Deletion or inhibition of GLUT1 blocked T cell proliferation and effector function, antibody production from B cells and reduced inflammatory responses in macrophages. Increased glucose uptake and GLUT1 expression are not only observed in proinflammatory conditions, but also in murine models of autoimmunity as well as in human patients. Rheumatoid arthritis (RA), the most common autoimmune disease, is characterized by infiltration of immune cells, hyperproliferation of fibroblast-like synoviocytes, and destruction of cartilage and bone. These processes create a hypoxic microenvironment in the synovium. Moreover, synovial samples including fibroblast-like synoviocytes from RA patients showed increased lactate level and upregulate GLUT1. Similar upregulation of GLUT1 is observed in systemic lupus erythematosus and psoriasis patients as well as in murine autoimmune models. Inhibition of GLUT1 using either T cell specific knockouts or small molecule GLUT1/glycolysis inhibitors improved phenotypes of different murine autoimmune disease models like arthritis, lupus, and psoriasis. Thereby the therapeutic potential of immunometabolism and especially interference with glycolysis was proven. This article is categorized under: Biological Mechanisms > Metabolism Translational, Genomic, and Systems Medicine > Translational Medicine Physiology > Mammalian Physiology in Health and Disease.