Project description:Whole-cell dynamical models of human cells are a central goal of systems biology. Such models could help researchers understand cell biology and help physicians treat disease. Despite significant challenges, we believe that human whole-cell models are rapidly becoming feasible. To develop a plan for achieving human whole-cell models, we analyzed the existing models of individual cellular pathways, surveyed the biomodeling community, and reflected on our experience developing whole-cell models of bacteria. Based on these analyses, we propose a plan for a project, termed the Human Whole-Cell Modeling Project, to achieve human whole-cell models. The foundations of the plan include technology development, standards development, and interdisciplinary collaboration.
Project description:Bacillus subtilis is one of the best-studied organisms. Due to the broad knowledge and annotation and the well-developed genetic system, this bacterium is an excellent starting point for genome minimization with the aim of constructing a minimal cell. We have analyzed the genome of B. subtilis and selected all genes that are required to allow life in complex medium at 37°C. This selection is based on the known information on essential genes and functions as well as on gene and protein expression data and gene conservation. The list presented here includes 523 and 119 genes coding for proteins and RNAs, respectively. These proteins and RNAs are required for the basic functions of life in information processing (replication and chromosome maintenance, transcription, translation, protein folding, and secretion), metabolism, cell division, and the integrity of the minimal cell. The completeness of the selected metabolic pathways, reactions, and enzymes was verified by the development of a model of metabolism of the minimal cell. A comparison of the MiniBacillus genome to the recently reported designed minimal genome of Mycoplasma mycoides JCVI-syn3.0 indicates excellent agreement in the information-processing pathways, whereas each species has a metabolism that reflects specific evolution and adaptation. The blueprint of MiniBacillus presented here serves as the starting point for a successive reduction of the B. subtilis genome.
Project description:Diffuse large B cell lymphoma (DLBCL) comprises multiple molecular and biological subtypes, resulting in a broad range of clinical outcomes. With standard chemoimmunotherapy, there remains an unacceptably high treatment failure rate in certain DLBCL subsets: activated B cell (ABC) DLBCL, double-hit lymphoma defined by the dual translocation of MYC and BCL2, dual protein-expressing lymphomas defined by the overexpression of MYC and BCL2, and older patients and those with central nervous system involvement. The main research challenges for DLBCL are to accurately identify molecular subsets and to determine if specific chemotherapy platforms and targeted agents offer differential benefit. The ultimate goal should be to maximize initial cure rates to improve long-term survival while minimizing toxicity. In particular, a frontline trial should focus on biologically defined risk groups not likely to be cured with cyclophosphamide, doxorubicin, vincristine, and prednisone plus rituximab (R-CHOP). An additional challenge is to develop effective and personalized strategies in the relapsed setting, for which there is no current standard other than autologous stem cell transplantation, which benefits a progressively smaller proportion of patients. Relapsed/refractory DLBCL is the ideal setting for testing novel agents and new biomarker tools and will require a national call for biopsies to optimize discovery in this setting. Accordingly, the development of tools with both prognostic and predictive utility and the individualized application of new therapies should be the main priorities. This report identifies clinical research priorities for critical areas of unmet need in this disease.
Project description:Nitrogen vacancy (NV) centers in diamond have been used as ultrasensitive magnetometers to perform nuclear magnetic resonance (NMR) spectroscopy of statistically polarized samples at 1-100 nm length scales. However, the spectral linewidth is typically limited to the kHz level, both by the NV sensor coherence time and by rapid molecular diffusion of the nuclei through the detection volume which in turn is critical for achieving long nuclear coherence times. Here we provide a blueprint supported by detailed theoretical analysis for a set-up that combines a sensitivity sufficient for detecting NMR signals from nano- to micron-scale samples with a spectral resolution that is limited only by the nuclear spin coherence, i.e. comparable to conventional NMR. Our protocol detects the nuclear polarization induced along the direction of an external magnetic field with near surface NV centers using lock-in detection techniques to enable phase coherent signal averaging. Using the NV centers in a dual role of NMR detector and optical hyperpolarization source to increase signal to noise, and in combination with Bayesian inference models for signal processing, nano/microscale NMR spectroscopy can be performed on sample concentrations in the micromolar range, several orders of magnitude better than the current state of the art.
Project description:Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cell identity code (CIC). The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen during the training. It also can resist some noise in the measurement of the GEP. Furthermore, we introduce classifier autoencoder, an architecture that can accurately identify cell type based on the GEP or the CIC.
Project description:To artificially reprogram cell fate, experimentalists manipulate the gene regulatory networks (GRNs) that maintain a cell's phenotype. In practice, reprogramming is often performed by constant overexpression of specific transcription factors (TFs). This process can be unreliable and inefficient. Here, we address this problem by introducing a new approach to reprogramming based on mathematical analysis. We demonstrate that reprogramming GRNs using constant overexpression may not succeed in general. Instead, we propose an alternative reprogramming strategy: a synthetic genetic feedback controller that dynamically steers the concentration of a GRN's key TFs to any desired value. The controller works by adjusting TF expression based on the discrepancy between desired and actual TF concentrations. Theory predicts that this reprogramming strategy is guaranteed to succeed, and its performance is independent of the GRN's structure and parameters, provided that feedback gain is sufficiently high. As a case study, we apply the controller to a model of induced pluripotency in stem cells.
Project description:Bispecific T cell engagers (BiTEs) are an innovative class of immunotherapeutics that redirect T cells to tumor surface antigens. While efficacious against certain hematological malignancies, limited bioavailability and severe toxicities have so far hampered broader clinical application, especially against solid tumors. Another emerging cancer immunotherapy are oncolytic viruses (OVs) which selectively infect and replicate in malignant cells, thereby mediating tumor vaccination effects. These oncotropic viruses can serve as vectors for tumor-targeted immunomodulation and synergize with other immunotherapies. In this article, we discuss the use of OVs to overcome challenges in BiTE therapy. We review the current state of the field, covering published preclinical studies as well as ongoing clinical investigations. We systematically introduce OV-BiTE vector design and characteristics as well as evidence for immune-stimulating and anti-tumor effects. Moreover, we address additional combination regimens, including CAR T cells and immune checkpoint inhibitors, and further strategies to modulate the tumor microenvironment using OV-BiTEs. The inherent complexity of these novel therapeutics highlights the importance of translational research including correlative studies in early-phase clinical trials. More broadly, OV-BiTEs can serve as a blueprint for diverse OV-based cancer immunotherapies.
Project description:Perovskite minerals form an essential component of the Earth's mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the "no-rattling" principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design.
Project description:The impact of large and complex epigenomic datasets on biological insights or clinical applications is limited by the lack of accessibility by easy, intuitive, and fast tools. Here, we describe an epigenomics comparative cyber-infrastructure (EPICO), an open-access reference set of libraries to develop comparative epigenomic data portals. Using EPICO, large epigenome projects can make available their rich datasets to the community without requiring specific technical skills. As a first instance of EPICO, we implemented the BLUEPRINT Data Analysis Portal (BDAP). BDAP provides a desktop for the comparative analysis of epigenomes of hematopoietic cell types based on results, such as the position of epigenetic features, from basic analysis pipelines. The BDAP interface facilitates interactive exploration of genomic regions, genes, and pathways in the context of differentiation of hematopoietic lineages. This work represents initial steps toward broadly accessible integrative analysis of epigenomic data across international consortia. EPICO can be accessed at https://github.com/inab, and BDAP can be accessed at http://blueprint-data.bsc.es.
Project description:Given the heterogeneity of senescent cells, our knowledge of both the drivers and consequences of cellular senescence in tissues and organs remains limited, as is our understanding of how this process could be harnessed for human health. Here we identified five broad areas that would help propel the field forward.