Deep generative modeling for volume reconstruction in cryo-electron microscopy

Advances in cryo-electron microscopy (cryo-EM) for high-resolution imaging of biomolecules in solution have provided new challenges and opportunities for algorithm development for 3D reconstruction. Next-generation volume reconstruction algorithms that combine generative modelling with end-to-end unsupervised deep learning techniques have shown promise, but many technical and theoretical hurdles remain, especially when applied to experimental cryo-EM images. In light of the proliferation of such methods, we propose here a critical review of recent advances in the field of deep generative modelling for cryo-EM reconstruction. The present review aims to (i) provide a unified statistical framework using terminology familiar to machine learning researchers with no specific background in cryo-EM, (ii) review the current methods in this framework, and (iii) outline outstanding bottlenecks and avenues for improvements in the field.     

How mathematical modelling elucidates signalling in Bacillus subtilis

Appropriate stimulus perception, signal processing and transduction ensure optimal adaptation of bacteria to environmental challenges. In the Gram‐positive model bacterium Bacillus subtilis signalling networks and molecular interactions therein are well‐studied, making this species a suitable candidate for the application of mathematical modelling. Here, we review systems biology approaches, focusing on chemotaxis, sporulation, σ^B‐dependent general stress response and competence. Processes like chemotaxis and Z‐ring assembly depend critically on the subcellular localization of proteins. Environmental response strategies, including sporulation and competence, are characterized by phenotypic heterogeneity in isogenic cultures. The examples of mathematical modelling also include investigations that have demonstrated how operon structure and signalling dynamics are intricately interwoven to establish optimal responses. Our review illustrates that these interdisciplinary approaches offer new insights into the response of B. subtilis to environmental challenges. These case studies reveal modelling as a tool to increase the understanding of complex systems, to help formulating hypotheses and to guide the design of more directed experiments that test predictions.     

Pneumolysin plays a key role at the initial step of establishing pneumococcal nasal colonization

Nasopharyngeal colonization by Streptococcus pneumoniae is an important initial step for the subsequent development of pneumococcal infections. Pneumococci have many virulence factors that play a role in colonization. Pneumolysin (PLY), a pivotal pneumococcal virulence factor for invasive disease, causes severe tissue damage and inflammation with disruption of epithelial tight junctions. In this study, we evaluated the role of PLY in nasal colonization of S. pneumoniae using a mouse colonization model. A reduction of numbers of PLY-deficient pneumococci recovered from nasal tissue, as well as nasal wash, was observed at days 1 and 2 post-intranasal challenges, but not later. The findings strongly support an important role for PLY in the initial establishment nasal colonization. PLY-dependent invasion of local nasal mucosa may be required to establish nasal colonization with S. pneumoniae. The data help provide a rationale to explain why an organism that exists as an asymptomatic colonizer has evolved virulence factors that enable it to occasionally invade and kill its hosts. Thus, the same pneumococcal virulence factor, PLY that can contribute to killing the host, may also play a role early in the establishment of nasopharynx carriage.     

Gene regulatory network inference: Data integration in dynamic models—A review

Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein–DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.     

Vaginal Immunization to Elicit Primary T-Cell Activation and Dissemination

Primary T-cell activation at mucosal sites is of utmost importance for the development of vaccination strategies. T-cell priming after vaginal immunization, with ovalbumin and CpG oligodeoxynucleotide adjuvant as model vaccine formulation, was studied in vivo in hormone-synchronized mice and compared to the one induced by the nasal route. Twenty-four hours after both vaginal or nasal immunization, antigen-loaded dendritic cells were detected within the respective draining lymph nodes. Vaginal immunization elicited a strong recruitment of antigen-specific CD4⁺ T cells into draining lymph nodes that was more rapid than the one observed following nasal immunization. T-cell clonal expansion was first detected in iliac lymph nodes, draining the genital tract, and proliferated T cells disseminated towards distal lymph nodes and spleen similarly to what observed following nasal immunization. T cells were indeed activated by the antigen encounter and acquired homing molecules essential to disseminate towards distal lymphoid organs as confirmed by the modulation of CD45RB, CD69, CD44 and CD62L marker expression. A multi-type Galton Watson branching process, previously used for in vitro analysis of T-cell proliferation, was applied to model in vivo CFSE proliferation data in draining lymph nodes 57 hours following immunization, in order to calculate the probabilistic decision of a cell to enter in division, rest in quiescence or migrate/die. The modelling analysis indicated that the probability of a cell to proliferate was higher following vaginal than nasal immunization. All together these data show that vaginal immunization, despite the absence of an organized mucosal associated inductive site in the genital tract, is very efficient in priming antigen-specific CD4⁺ T cells and inducing their dissemination from draining lymph nodes towards distal lymphoid organs.     

Wetland restoration challenges and eco-volunteerism

Depending on planned behaviour theory most studies have assumed a direct effect between socio-demographics and biophilia attitudes on eco-volunteerism without considering prior eco-restoration challenges. Using the expectancy motivation theory, this study assessed the challenges eco-volunteers faced during an eco-restoration exercise within the Muni-Pomadze Ramsar Site (Ghana) and its effects on future eco-volunteerism. The study collected data from eco-volunteers and analysed it using a paired-sampled t-test and generalised linear modelling. Per the results, eco-volunteers face challenges including transportation difficulties, limited refreshment, inadequate equipment, and interpersonal conflict with other eco-volunteers. Ecological reasons superseded social reasons as a motivating factor for eco-volunteerism. Expectancy motivation theory influence of valence played out when the combined effect of transportation difficulties, limited refreshment, inadequate equipment, and interpersonal conflicts on social reasons negatively affected future eco-volunteerism. The study recommends that organisers of eco-restoration exercises should be more sensitive to the needs of eco-volunteers and address these challenges to help encourage future eco-volunteerism.     

Federated learning for molecular discovery

Federated Learning enables machine learning across multiple sources of data and alleviates the risk of leaking private information between partners thereby encouraging knowledge sharing and collaborative modelling. Hence, Federated Learning opens the ways to a new generation of improved models. Domains involving molecular informatics, like Drug Discovery, are progressively adopting Federated Learning; this review describes the main projects and applications of Federated Learning for molecular discovery with a special focus on their benefits and the remaining challenges. All the studies demonstrate a real benefit of Federated Learning, namely the improvement of the performance of models as well as their applicability domain thanks to knowledge aggregation. The selected publications also reveal several remaining challenges to be addressed to fully exploit Federated Learning.     

Analysis of nasal potential in murine cystic fibrosis models

The nasal epithelium of the mouse closely mimics the bioelectrical phenotype of the human airways. Ion transport across the nasal epithelium induces a nasal transepithelial potential difference. Its measurement by a relatively non-invasive method adapted from humans allows in vivo longitudinal measurements of CFTR-dependent ionic transport in the murine nasal mucosa. This test offers a useful tool to assess CFTR function in preclinical studies for novel therapeutics modulating CFTR activity.Here we extensively review work done to assess transepithelial transport in the murine respiratory epithelium in the basal state and after administration of CFTR modulators. Factors of variability and discriminative threshold between the CF and the WT mice for different readouts are discussed.     

Translational Informatics for Parkinson’s Disease: from Big Biomedical Data to Small Actionable Alterations

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.     

Synthesising 30 Years of Mathematical Modelling of Echinococcus Transmission

Background

Echinococcosis is a complex zoonosis that has domestic and sylvatic lifecycles, and a range of different intermediate and definitive host species. The complexities of its transmission and the sparse evidence on the effectiveness of control strategies in diverse settings provide significant challenges for the design of effective public health policy against this disease. Mathematical modelling is a useful tool for simulating control packages under locally specific transmission conditions to inform optimal timing and frequency of phased interventions for cost-effective control of echinococcosis. The aims of this review of 30 years of Echinococcus modelling were to discern the epidemiological mechanisms underpinning models of Echinococcus granulosus and E. multilocularis transmission and to establish the need to include a human transmission component in such models.

Methodology/Principal Findings

A search was conducted of all relevant articles published up until July 2012, identified from the PubMED, Web of Knowledge and Medline databases and review of bibliographies of selected papers. Papers eligible for inclusion were those describing the design of a new model, or modification of an existing mathematical model of E. granulosus or E. multilocularis transmission. A total of 13 eligible papers were identified, five of which described mathematical models of E. granulosus and eight that described E. multilocularis transmission. These models varied primarily on the basis of six key mechanisms that all have the capacity to modulate model dynamics, qualitatively affecting projections. These are: 1) the inclusion of a ‘latent’ class and/or time delay from host exposure to infectiousness; 2) an age structure for animal hosts; 3) the presence of density-dependent constraints; 4) accounting for seasonality; 5) stochastic parameters; and 6) inclusion of spatial and risk structures.

Conclusions/Significance

This review discusses the conditions under which these mechanisms may be important for inclusion in models of Echinococcus transmission and proposes recommendations for the design of dynamic human models of transmission. Accounting for the dynamic behaviour of the Echinococcus parasites in humans will be key to predicting changes in the disease burden over time and to simulate control strategies that optimise public health impact.     

Biomechanics of Musculoskeletal System and Its Biomimetic Implications： A Review

Biological musculoskeletal system （MSK）, composed of numerous bones, cartilages, skeletal muscles, tendons, ligaments etc., provides form, support, movement and stability for human or animal body. As the result of million years of selection and evolution, the biological MSK evolves to be a nearly perfect mechanical mechanism to support and transport the human or animal body, and would provide enormously rich resources to inspire engineers to innovate new technology and methodology to develop robots and mechanisms as effective and economical as the biological systems. This paper provides a general review of the current status of musculoskeletal biomechanics studies using both experimental and computational methods. This includes the use of the latest three-dimensional motion analysis systems, various medical imaging modalities, and also the advanced rigid-body and continuum mechanics musculoskeletal modelling techniques. Afterwards, several representative biomimetic studies based on ideas and concepts inspired from the structures and biomechanical functions of the biological MSK are dis- cussed. Finally, the major challenges and also the future research directions in musculoskeletal biomechanics and its biomimetic studies are proposed.     

Theoretical models of wound healing: past successes and future challenges

The complex biology of wound healing is an area in which theoretical modelling has already made a significant impact. In this review article, the authors describe the key features of wound healing biology, divided into four components: epidermal wound healing, remodelling of the dermal extracellular matrix, wound contraction, and angiogenesis. Within each of these categories, previous modelling work is described, and the authors identify what they regard as the main challenges for future theoretical work.     

Merocel versus Nasopore for Nasal Packing: A Meta-Analysis of Randomized Controlled Trials

Objective

To compare the clinical outcomes, including efficacy and complications, of Merocel versus Nasopore as a nasal packing material after nasal surgery.

Methods

Relevant randomized controlled trials were identified from electronic databases (The Cochrane Library, PubMed, EMBASE, China National Knowledge Infrastructure and Chinese Biomedical Database). Conference proceedings and references from identified trials and review articles were also searched. Outcome measures were pain during nasal packing, pain and bleeding upon packing removal, pressure sensation, nasal blockage, formation of synechiae, mucosal healing, and patients'' general satisfaction.

Results

Seven randomized controlled trials met criteria for analysis. Compared with Merocel, Nasopore significantly reduced patients'' subjective symptoms including in situ pain (pain experienced while packing is in place), nasal pressure, pain and bleeding during packing removal, and increased patients'' general satisfaction with nasal packing. There were no significant differences in nasal obstruction, adhesion and mucosal healing between the Merocel and Nasopore groups.

Conclusions

Preliminary evidence suggests that Nasopore may be superior to Merocel as a nasal packing material with regard to in situ pain, pain and bleeding upon removal, pressure, and general satisfaction and does not differ from Merocel in terms of nasal obstruction, tissue adhesion, and long-term mucosal healing.     

Kinetic modelling of plant metabolic pathways

This paper provides a review of kinetic modelling of plant metabolic pathways as a tool for analysing their control and regulation. An overview of different modelling strategies is presented, starting with those approaches that only require a knowledge of the network stoichiometry; these are referred to as structural. Flux-balance analysis, metabolic flux analysis using isotope labelling, and elementary mode analysis are briefly mentioned as three representative examples. The main focus of this paper, however, is a discussion of kinetic modelling, which requires, in addition to the stoichiometry, a knowledge of the kinetic properties of the constituent pathway enzymes. The different types of kinetic modelling analysis, namely time-course simulation, steady-state analysis, and metabolic control analysis, are explained in some detail. An overview is presented of strategies for obtaining model parameters, as well as software tools available for simulation of such models. The kinetic modelling approach is exemplified with discussion of three models from the general plant physiology literature. With the aid of kinetic modelling it is possible to perform a control analysis of a plant metabolic system, to identify potential targets for biotechnological manipulation, as well as to ascertain the regulatory importance of different enzymes (including isoforms of the same enzyme) in a pathway. Finally, a framework is presented for extending metabolic models to the whole-plant scale by linking biochemical reactions with diffusion and advective flow through the phloem. Future challenges include explicit modelling of subcellular compartments, as well as the integration of kinetic models on the different levels of the cellular and organizational hierarchy.     

Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling

Aim Robust and reliable predictions of the effects of climate change on biodiversity are required in formulating conservation and management strategies that best retain biodiversity into the future. Significant challenges in modelling climate change impacts arise from limitations in our current knowledge of biodiversity. Community‐level modelling can complement species‐level approaches in overcoming these limitations and predicting climate change impacts on biodiversity as a whole. However, the community‐level approaches applied to date have been largely correlative, ignoring the key processes that influence change in biodiversity over space and time. Here, we suggest that the development of new ‘semi‐mechanistic’ community‐level models would substantially increase our capacity to predict climate change impacts on biodiversity. Location Global. Methods Drawing on an expansive review of biodiversity modelling approaches and recent advances in semi‐mechanistic modelling at the species level, we outline the main elements of a new semi‐mechanistic community‐level modelling approach. Results Our quantitative review revealed a sharp divide between mechanistic and non‐mechanistic biodiversity modelling approaches, with very few semi‐mechanistic models developed to date. Main conclusions We suggest that the conceptual framework presented here for combining mechanistic and non‐mechanistic community‐level approaches offers a promising means of incorporating key processes into predictions of climate change impacts on biodiversity whilst working within the limits of our current knowledge.     

Modelling the functional genomics of Parkinson’s disease in Caenorhabditis elegans: LRRK2 and beyond

For decades, Parkinson’s disease (PD) cases have been genetically categorised into familial, when caused by mutations in single genes with a clear inheritance pattern in affected families, or idiopathic, in the absence of an evident monogenic determinant. Recently, genome-wide association studies (GWAS) have revealed how common genetic variability can explain up to 36% of PD heritability and that PD manifestation is often determined by multiple variants at different genetic loci. Thus, one of the current challenges in PD research stands in modelling the complex genetic architecture of this condition and translating this into functional studies. Caenorhabditis elegans provide a profound advantage as a reductionist, economical model for PD research, with a short lifecycle, straightforward genome engineering and high conservation of PD relevant neural, cellular and molecular pathways. Functional models of PD genes utilising C. elegans show many phenotypes recapitulating pathologies observed in PD. When contrasted with mammalian in vivo and in vitro models, these are frequently validated, suggesting relevance of C. elegans in the development of novel PD functional models. This review will discuss how the nematode C. elegans PD models have contributed to the uncovering of molecular and cellular mechanisms of disease, with a focus on the genes most commonly found as causative in familial PD and risk factors in idiopathic PD. Specifically, we will examine the current knowledge on a central player in both familial and idiopathic PD, Leucine-rich repeat kinase 2 (LRRK2) and how it connects to multiple PD associated GWAS candidates and Mendelian disease-causing genes.     

An Immature Pachyrhinosaurus perotorum (Dinosauria: Ceratopsidae) Nasal Reveals Unexpected Complexity of Craniofacial Ontogeny and Integument in Pachyrhinosaurus

A new specimen attributable to an immature individual of Pachyrhinosaurus perotorum (Dinosauria, Ceratopsidae) from the Kikak-Tegoseak Quarry in northern Alaska preserves a mix of features that provides refinement to the sequence of ontogenetic stages and transformations inferred for the development of the nasal boss in Pachyrhinosaurus. The new specimen consists of an incomplete nasal that includes the posterior part of the nasal horn, the dorsal surface between the horn and the left-side contacts for the prefrontal and frontal, and some of the left side of the rostrum posteroventral to the nasal horn. The combination of morphologies in the new specimen suggests either an additional stage of development should be recognized in the ontogeny of the nasal boss of Pachyrhinosaurus, or that the ontogenetic pathway of nasal boss development in P. perotorum was notably different from that of P. lakustai. Additionally, the presence of a distinct basal sulcus and the lateral palisade texture on the nasal horn of the specimen described here indicate that a thick, cornified horn sheath was present well before the formation of a dorsal cornified pad. A separate rugose patch on the nasal well posterior to the nasal horn is evidence for a cornified integumentary structure, most likely a thick cornified pad, on the posterior part of the nasal separate from the nasal horn prior to the onset of nasal boss formation in P. perotorum.     

Studying metabolism with multi-organ chips: new tools for disease modelling,pharmacokinetics and pharmacodynamics

Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms.