How and Why to Build a Unified Tree of Life

Phylogenetic trees are a crucial backbone for a wide breadth of biological research spanning systematics, organismal biology, ecology, and medicine. In 2015, the Open Tree of Life project published a first draft of a comprehensive tree of life, summarizing digitally available taxonomic and phylogenetic knowledge. This paper reviews, investigates, and addresses the following questions as a follow‐up to that paper, from the perspective of researchers involved in building this summary of the tree of life: Is there a tree of life and should we reconstruct it? Is available data sufficient to reconstruct the tree of life? Do we have access to phylogenetic inferences in usable form? Can we combine different phylogenetic estimates across the tree of life? And finally, what is the future of understanding the tree of life?

     

Integrating Cellular Metabolism into a Multiscale Whole-Body Model

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.     

Nonlinear Compliance Modulates Dynamic Bronchoconstriction in a Multiscale Airway Model

The role of breathing and deep inspirations (DI) in modulating airway hyperresponsiveness remains poorly understood. In particular, DIs are potent bronchodilators of constricted airways in nonasthmatic subjects but not in asthmatic subjects. Additionally, length fluctuations (mimicking DIs) have been shown to reduce mean contractile force when applied to airway smooth muscle (ASM) cells and tissue strips. However, these observations are not recapitulated on application of transmural pressure (P_TM) oscillations (that mimic tidal breathing and DIs) in isolated intact airways. To shed light on this paradox, we have developed a biomechanical model of the intact airway, accounting for strain-stiffening due to collagen recruitment (a large component of the extracellular matrix (ECM)), and dynamic actomyosin-driven force generation by ASM cells. In agreement with intact airway studies, our model shows that P_TM fluctuations at particular mean transmural pressures can lead to only limited bronchodilation. However, our model predicts that moving the airway to a more compliant point on the static pressure-radius relationship (which may involve reducing mean P_TM), before applying pressure fluctuations, can generate greater bronchodilation. This difference arises from competition between passive strain-stiffening of ECM and force generation by ASM yielding a highly nonlinear relationship between effective airway stiffness and P_TM, which is modified by the presence of contractile agonist. Effectively, the airway at its most compliant may allow for greater strain to be transmitted to subcellular contractile machinery. The model predictions lead us to hypothesize that the maximum possible bronchodilation of an airway depends on its static compliance at the P_TM about which the fluctuations are applied. We suggest the design of additional experimental protocols to test this hypothesis.     

How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies

There is a lack of standardized country-specific environmental data to combine with nutritional and dietary data for assessing the environmental impact of individual diets in epidemiology surveys, which are consequently reliant on environmental food datasets based on values retrieved from a heterogeneous literature. The aim of this study was to compare and assess the relative strengths and limits of a database of food greenhouse gas emissions (GHGE) values estimated with a hybrid method combining input/output and LCA approaches, with a dataset of GHGE values retrieved from the literature. France is the geographical perimeter considered in this study, but the methodology could be applied to other countries. The GHGE of 402 foodstuffs, representative of French diet, were estimated using the hybrid method. In parallel, the GHGE of individual foods were collected from existing literature. Median per-food-category GHGE values from the hybrid method and the reviewed literature were found to correlate strongly (Spearman correlation was 0.83), showing similar rankings of food categories. Median values were significantly different for only 5 (out of 29) food categories, including the ruminant meats category for which the hybrid method gave lower estimates than those from existing literature. Analysis also revealed that literature values came from heterogeneous studies that were not always sourced and that were conducted under different LCA modeling hypotheses. In contrast, the hybrid method helps build reliably-sourced, representative national standards for product-based datasets. We anticipate this hybrid method to be a starting point for better environmental impact assessments of diets.     

Know Your Enemy: How to Build and Vanquish a Global Fungal Scourge

The 8th International Conference on Cryptococcus and Cryptococcosis, chaired by Maurizio Del Poeta (Medical University of South Carolina), and organized together with June Kwon-Chung (National Institute of Allergy and Infectious Diseases), Stuart Levitz (University of Massachusetts Medical School), and John Perfect (Duke University), occurred in May 2011. This meeting brought together the world's leading researchers on Cryptococcus and cryptococcosis, including basic scientists, epidemiologists, and clinicians, to discuss new developments in Cryptococcus biology. With more than 60 oral presentations and 180 posters, this meeting enhanced our understanding of pathogenicity of Cryptococcus and served as a robust forum that facilitated cross-disciplinary discussions, research, and clinical collaborations. Due to space constraints, this brief overview highlights only a few of the topics discussed in this meeting, focusing on the evolution of virulence, host and pathogen interactions, fungal and host signaling, new advances of genomics studies on Cryptococcus, and the current status of the outbreak caused by C. gattii. The 8th International Conference on Cryptococcus and Cryptococcosis brought together scientists from across the globe in the beautiful historical downtown setting of Charleston to share their latest findings and highlight advances in Cryptococcus research. With more than 250 participants, this meeting was the largest gathering of the Cryptococcus international community in the 24-year history. Here, we review the advances presented and the current state of knowledge in the field.     

Capturing the Dynamics of a Hybrid Multiscale Cancer Model with a Continuum Model

Cancer is a complex disease involving processes at spatial scales from subcellular, like cell signalling, to tissue scale, such as vascular network formation. A number of multiscale models have been developed to study the dynamics that emerge from the coupling between the intracellular, cellular and tissue scales. Here, we develop a continuum partial differential equation model to capture the dynamics of a particular multiscale model (a hybrid cellular automaton with discrete cells, diffusible factors and an explicit vascular network). The purpose is to test under which circumstances such a continuum model gives equivalent predictions to the original multiscale model, in the knowledge that the system details are known, and differences in model results can be explained in terms of model features (rather than unknown experimental confounding factors). The continuum model qualitatively replicates the dynamics from the multiscale model, with certain discrepancies observed owing to the differences in the modelling of certain processes. The continuum model admits travelling wave solutions for normal tissue growth and tumour invasion, with similar behaviour observed in the multiscale model. However, the continuum model enables us to analyse the spatially homogeneous steady states of the system, and hence to analyse these waves in more detail. We show that the tumour microenvironmental effects from the multiscale model mean that tumour invasion exhibits a so-called pushed wave when the carrying capacity for tumour cell proliferation is less than the total cell density at the tumour wave front. These pushed waves of tumour invasion propagate by triggering apoptosis of normal cells at the wave front. Otherwise, numerical evidence suggests that the wave speed can be predicted from linear analysis about the normal tissue steady state.     

人工构建单细胞生物: 合成生物学新的里程碑

<正>2010年5月20日,一项用合成基因组制造支原体细胞的研究成果在《Sciences》上发表[1],该成果由美国克雷格·文特研究所(The J Craig Venter Institute)丹尼尔·吉布森(Daniel G Gibson)领导的研究小组完成。他们     

如何检索国外植物生物学相关网站

植物生长与发育、植物细胞信号和基因调控、植物基因组研究等三个领域是当前植物生物学研究的热点。我们介绍了与这三个领域有关的国外主要网站,以期对植物生物学研究者有所帮助。     

How to Build a Laser Speckle Contrast Imaging (LSCI) System to Monitor Blood Flow

Laser Speckle Contrast Imaging (LSCI) is a simple yet powerful technique that is used for full-field imaging of blood flow. The technique analyzes fluctuations in a dynamic speckle pattern to detect the movement of particles similar to how laser Doppler analyzes frequency shifts to determine particle speed. Because it can be used to monitor the movement of red blood cells, LSCI has become a popular tool for measuring blood flow in tissues such as the retina, skin, and brain. It has become especially useful in neuroscience where blood flow changes during physiological events like functional activation, stroke, and spreading depolarization can be quantified. LSCI is also attractive because it provides excellent spatial and temporal resolution while using inexpensive instrumentation that can easily be combined with other imaging modalities. Here we show how to build a LSCI setup and demonstrate its ability to monitor blood flow changes in the brain during an animal experiment.Download video file.^{(31M, mov)}     

A Multiscale Hybrid Model for Pro-angiogenic Calcium Signals in a Vascular Endothelial Cell

Cytosolic calcium machinery is one of the principal signaling mechanisms by which endothelial cells (ECs) respond to external stimuli during several biological processes, including vascular progression in both physiological and pathological conditions. Low concentrations of angiogenic factors (such as VEGF) activate in fact complex pathways involving, among others, second messengers arachidonic acid (AA) and nitric oxide (NO), which in turn control the activity of plasma membrane calcium channels. The subsequent increase in the intracellular level of the ion regulates fundamental biophysical properties of ECs (such as elasticity, intrinsic motility, and chemical strength), enhancing their migratory capacity. Previously, a number of continuous models have represented cytosolic calcium dynamics, while EC migration in angiogenesis has been separately approached with discrete, lattice-based techniques. These two components are here integrated and interfaced to provide a multiscale and hybrid Cellular Potts Model (CPM), where the phenomenology of a motile EC is realistically mediated by its calcium-dependent subcellular events. The model, based on a realistic 3-D cell morphology with a nuclear and a cytosolic region, is set with known biochemical and electrophysiological data. In particular, the resulting simulations are able to reproduce and describe the polarization process, typical of stimulated vascular cells, in various experimental conditions. Moreover, by analyzing the mutual interactions between multilevel biochemical and biomechanical aspects, our study investigates ways to inhibit cell migration: such strategies have in fact the potential to result in pharmacological interventions useful to disrupt malignant vascular progression.     

Development of a Three Dimensional Multiscale Computational Model of the Human Epidermis

Transforming Growth Factor (TGF-β1) is a member of the TGF-beta superfamily ligand-receptor network. and plays a crucial role in tissue regeneration. The extensive in vitro and in vivo experimental literature describing its actions nevertheless describe an apparent paradox in that during re-epithelialisation it acts as proliferation inhibitor for keratinocytes. The majority of biological models focus on certain aspects of TGF-β1 behaviour and no one model provides a comprehensive story of this regulatory factor''s action. Accordingly our aim was to develop a computational model to act as a complementary approach to improve our understanding of TGF-β1. In our previous study, an agent-based model of keratinocyte colony formation in 2D culture was developed. In this study this model was extensively developed into a three dimensional multiscale model of the human epidermis which is comprised of three interacting and integrated layers: (1) an agent-based model which captures the biological rules governing the cells in the human epidermis at the cellular level and includes the rules for injury induced emergent behaviours, (2) a COmplex PAthway SImulator (COPASI) model which simulates the expression and signalling of TGF-β1 at the sub-cellular level and (3) a mechanical layer embodied by a numerical physical solver responsible for resolving the forces exerted between cells at the multi-cellular level. The integrated model was initially validated by using it to grow a piece of virtual epidermis in 3D and comparing the in virtuo simulations of keratinocyte behaviour and of TGF-β1 signalling with the extensive research literature describing this key regulatory protein. This research reinforces the idea that computational modelling can be an effective additional tool to aid our understanding of complex systems. In the accompanying paper the model is used to explore hypotheses of the functions of TGF-β1 at the cellular and subcellular level on different keratinocyte populations during epidermal wound healing.