Wiley Interdisciplinary Reviews-Systems Biology and Medicine最新文献

Cellular cytoskeletal mechanics plays a major role in many aspects of human health from organ development to wound healing, tissue homeostasis and cancer metastasis. We summarize the state-of-the-art techniques for mathematically modeling cellular stiffness and mechanics and the cytoskeletal components and factors that regulate them. We highlight key experiments that have assisted model parameterization and compare the advantages of different models that have been used to recapitulate these experiments. An overview of feed-forward mechanisms from signaling to cytoskeleton remodeling is provided, followed by a discussion of the rapidly growing niche of encapsulating feedback mechanisms from cytoskeletal and cell mechanics to signaling. We discuss broad areas of advancement that could accelerate research and understanding of cellular mechanobiology. A precise understanding of the molecular mechanisms that affect cell and tissue mechanics and function will underpin innovations in medical device technologies of the future. WIREs Syst Biol Med 2018, 10:e1407. doi: 10.1002/wsbm.1407 This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.

Enhancers serve as critical regulatory elements in higher eukaryotic cells. The characterization of enhancer function has evolved primarily from genome-wide methodologies, including chromatin immunoprecipitation (ChIP-seq), DNase-I hypersensitivity (DNase-seq), digital genomic footprinting (DGF), and the chromosome conformation capture techniques (3C, 4C, and Hi-C). These population-based assays average signals across millions of cells and lead to enhancer models characterized by static and sequential binding. More recently, fluorescent microscopy techniques, including fluorescence recovery after photobleaching, fluorescence correlation spectroscopy, and single molecule tracking (SMT), reveal a highly dynamic binding behavior for these factors in live cells. Furthermore, a refined analysis of genomic footprinting suggests that many transcription factors leave minimal or no footprints in chromatin, even when present and active in a given cell type. In this study, we review the implications of these new approaches for an accurate understanding of enhancer function in real time. In vivo SMT, in particular, has recently evolved as a promising methodology to probe enhancer function in live cells. Integration of findings from the many approaches now employed in the study of enhancer function suggest a highly dynamic view for the action of enhancer activating factors, viewed on a time scale of milliseconds to seconds, rather than minutes to hours. WIREs Syst Biol Med 2018, 10:e1390. doi: 10.1002/wsbm.1390 This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Genetic/Genomic Methods Laboratory Methods and Technologies > Imaging.

The epidermal growth factor receptor (EGFR) is a transmembrane receptor tyrosine kinase (RTK) that is critical for normal development and function. EGFR is also amplified or mutated in a variety of cancers including in nearly 60% of cases of the highly lethal brain cancer glioblastoma (GBM). EGFR amplification and mutation reprogram cellular metabolism and broadly alter gene transcription to drive tumor formation and progression, rendering EGFR as a compelling drug target. To date, brain tumor patients have yet to benefit from anti-EGFR therapy due in part to an inability to achieve sufficient intratumoral drug levels in the brain, cultivating adaptive mechanisms of resistance. Here, we review an alternative set of strategies for targeting EGFR-amplified GBMs, based on identifying and targeting tumor co-dependencies shaped both by aberrant EGFR signaling and the brain's unique biochemical environment. These approaches may include highly brain-penetrant drugs from non-cancer pipelines, expanding the pharmacopeia and providing promising new treatments. We review the molecular underpinnings of EGFR-activated co-dependencies in the brain and the promising new treatments based on this strategy. WIREs Syst Biol Med 2018, 10:e1398. doi: 10.1002/wsbm.1398 This article is categorized under: Biological Mechanisms > Cell Signaling Laboratory Methods and Technologies > Genetic/Genomic Methods Translational, Genomic, and Systems Medicine > Translational Medicine.

Metabolism is tied into complex interactions with cell intrinsic and extrinsic processes that go beyond the conversion of nutrients into energy and biomass. Indeed, metabolism is a central cellular hub that interconnects and influences the microenvironment, the cellular phenotype, cell signaling, and the (epi)genetic landscape. While these interactions evolved to support survival and function of normal cells, they are hijacked by cancer cells to enable cancer maintenance and progression. Thus, a mechanistic and functional understanding of complex metabolic interactions provides a basis for the discovery of novel metabolic vulnerabilities in cancer. In this review, we will summarize and provide context for the to-date discovered complex metabolic interactions by discussing how the microenvironment as well as the cellular phenotype define cancer metabolism, and how metabolism shapes the epigenetic state of cancer cells. Many of the studies investigating the crosstalk of metabolism with cell intrinsic and extrinsic processes have used integrative data analysis approaches at the interface between computational and experimental cancer research, and we will highlight those throughout the review. In conclusion, identifying and understanding complex metabolic interactions is a basis for deciphering novel metabolic vulnerabilities of cancer cells. WIREs Syst Biol Med 2018, 10:e1397. doi: 10.1002/wsbm.1397 This article is categorized under: Biological Mechanisms > Metabolism Physiology > Mammalian Physiology in Health and Disease.

Advances in high-resolution microscopy and other techniques have emphasized the spatio-temporal nature of information transfer through signal transduction pathways. The compartmentalization of signaling molecules and the existence of microdomains are now widely acknowledged as key features in biochemical signaling. To complement experimental observations of spatio-temporal dynamics, mathematical modeling has emerged as a powerful tool. Using modeling, one can not only recapitulate experimentally observed dynamics of signaling molecules, but also gain an understanding of the underlying mechanisms in order to generate experimentally testable predictions. Reaction-diffusion systems are commonly used to this end; however, the analysis of coupled nonlinear systems of partial differential equations, generated by considering large reaction networks is often challenging. Here, we aim to provide an introductory tutorial for the application of reaction-diffusion models to the spatio-temporal dynamics of signaling pathways. In particular, we outline the steps for stability analysis of such models, with a focus on biochemical signal transduction. WIREs Syst Biol Med 2018, 10:e1395. doi: 10.1002/wsbm.1395 This article is categorized under: Biological Mechanisms > Cell Signaling Analytical and Computational Methods > Dynamical Methods Models of Systems Properties and Processes > Mechanistic Models.

Cells employ various mechanisms for dynamic control of enzyme expression. An important mechanism is mutual feedback-or crosstalk-between transcription and metabolism. As recently suggested, enzyme levels are often much higher than absolutely needed to maintain metabolic flux. However, given the potential burden of high enzyme levels it seems likely that cells control enzyme expression to meet other cellular objectives. In this review, we discuss whether crosstalk between metabolism and transcription could inform cells about how much enzyme is optimal for various fitness aspects. Two major problems should be addressed in order to understand optimization of enzyme levels by crosstalk. First, mapping of metabolite-protein interactions will be crucial to obtain a better mechanistic understanding of crosstalk. Second, investigating cellular objectives that define optimal enzyme levels can reveal the functional relevance of crosstalk. We present recent studies that approach these problems, drawing from experimental transcript and metabolite data, and from theoretical network analyses. WIREs Syst Biol Med 2018, 10:e1396. doi: 10.1002/wsbm.1396 This article is categorized under: Biological Mechanisms > Metabolism Laboratory Methods and Technologies > Metabolomics Biological Mechanisms > Regulatory Biology.

Genome-editing therapeutics are poised to treat human diseases. As we enter clinical trials with the most promising CRISPR-Cas9 and CRISPR-Cas12a (Cpf1) modalities, the risks associated with administering these foreign biomolecules into human patients become increasingly salient. Preclinical discovery with CRISPR-Cas9 and CRISPR-Cas12a systems and foundational gene therapy studies indicate that the host immune system can mount undesired responses against the administered proteins and nucleic acids, the gene-edited cells, and the host itself. These host defenses include inflammation via activation of innate immunity, antibody induction in humoral immunity, and cell death by T-cell-mediated cytotoxicity. If left unchecked, these immunological reactions can curtail therapeutic benefits and potentially lead to mortality. Ways to assay and reduce the immunogenicity of Cas9 and Cas12a proteins are therefore critical for ensuring patient safety and treatment efficacy, and for bringing us closer to realizing the vision of permanent genetic cures. WIREs Syst Biol Med 2018, 10:e1408. doi: 10.1002/wsbm.1408 This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods Translational, Genomic, and Systems Medicine > Translational Medicine Translational, Genomic, and Systems Medicine > Therapeutic Methods.

The development and implementation of personalized medicine is paramount to improving the efficiency and efficacy of patient care. In the respiratory system, function is largely dictated by the choreographed movement of air and blood to the gas exchange surface. The passage of air begins in the upper airways, either via the mouth or nose, and terminates at the alveolar interface, while blood flows from the heart to the alveoli and back again. Computational fluid dynamics (CFD) is a well-established tool for predicting fluid flows and pressure distributions within complex systems. Traditionally CFD has been used to aid in the effective or improved design of a system or device; however, it has become increasingly exploited in biological and medical-based applications further broadening the scope of this computational technique. In this review, we discuss the advancement in application of CFD to the respiratory system and the contributions CFD is currently making toward improving precision medicine. The key areas CFD has been applied to in the pulmonary system are in predicting fluid transport and aerosol distribution within the airways. Here we focus our discussion on fluid flows and in particular on image-based clinically focused CFD in the ventilatory system. We discuss studies spanning from the paranasal sinuses through the conducting airways down to the level of the alveolar airways. The combination of imaging and CFD is enabling improved device design in aerosol transport, improved biomarkers of lung function in clinical trials, and improved predictions and assessment of surgical interventions in the nasal sinuses. WIREs Syst Biol Med 2017, 9:e1392. doi: 10.1002/wsbm.1392 For further resources related to this article, please visit the WIREs website.

Advances in genome sequencing, high throughput measurement of gene and protein expression levels, data accessibility, and computational power have allowed genome-scale metabolic models (GEMs) to become a useful tool for understanding metabolic alterations associated with many different diseases. Despite the proven utility of GEMs, researchers confront multiple challenges in the use of GEMs, their application to human health and disease, and their construction and simulation in an organ-specific and disease-specific manner. Several approaches that researchers are taking to address these challenges include using proteomic and transcriptomic-informed methods to build GEMs for individual organs, diseases, and patients and using constraints on model behavior during simulation to match observed metabolic fluxes. We review the challenges facing researchers in the use of GEMs, review the approaches used to address these challenges, and describe advances that are on the horizon and could lead to a better understanding of human metabolism. WIREs Syst Biol Med 2017, 9:e1393. doi: 10.1002/wsbm.1393 For further resources related to this article, please visit the WIREs website.

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.