Amides are excellent mimics of phosphate internucleoside linkages and are well tolerated in short interfering RNAs

RNA interference (RNAi) has become an important tool in functional genomics and has an intriguing therapeutic potential. However, the current design of short interfering RNAs (siRNAs) is not optimal for in vivo applications. Non-ionic phosphate backbone modifications may have the potential to improve the properties of siRNAs, but are little explored in RNAi technologies. Using X-ray crystallography and RNAi activity assays, the present study demonstrates that 3\u27-CH2-CO-NH-5\u27 amides are excellent replacements for phosphodiester internucleoside linkages in RNA. The crystal structure shows that amide-modified RNA forms a typical A-form duplex. The amide carbonyl group points into the major groove and assumes an orientation that is similar to the P-OP2 bond in the phosphate linkage. Amide linkages are well hydrated by tandem waters linking the carbonyl group and adjacent phosphate oxygens. Amides are tolerated at internal positions of both the guide and passenger strand of siRNAs and may increase the silencing activity when placed near the 5\u27-end of the passenger strand. As a result, an siRNA containing eight amide linkages is more active than the unmodified control. The results suggest that RNAi may tolerate even more extensive amide modification, which may be useful for optimization of siRNAs for in vivo applications

Development of Methods to Predict the Effects of Test Media in Ground-Based Propulsion Testing

This report discusses work that began in mid-2004 sponsored by the Office of the Secretary of Defense (OSD) Test & Evaluation/Science & Technology (T&E/S&T) Program. The work was undertaken to improve the state of the art of CFD capabilities for predicting the effects of the test media on the flameholding characteristics in scramjet engines. The program had several components including the development of advanced algorithms and models for simulating engine flowpaths as well as a fundamental experimental and diagnostic development effort to support the formulation and validation of the mathematical models. This report provides details of the completed work, involving the development of phenomenological models for Reynolds averaged Navier-Stokes codes, large-eddy simulation techniques and reduced-kinetics models. Experiments that provided data for the modeling efforts are also described, along with with the associated nonintrusive diagnostics used to collect the data

Predicting the Effects of Test Media in Ground-Based Propulsion Testing

This paper discusses the progress of work which began in mid-2004 sponsored by the Office of the Secretary of Defense (OSD) Test & Evaluation/Science & Technology (T&E/S&T) Program. The purpose of the work is to improve the state of the art of CFD capabilities for predicting the effects of the test media on the flameholding characteristics in scramjet engines. The program has several components including the development of advance algorithms and models for simulating engine flowpaths as well as a fundamental experimental and diagnostic development effort to support the formulation and validation of the mathematical models. The paper will provide details of current work involving the development of phenomenological models for Reynolds averaged Navier-Stokes codes, large-eddy simulation techniques and reduced-kinetics models. Experiments that will provide data for the modeling efforts will also be described, along with with the associated nonintrusive diagnostics used to collect the data

Current state and challenges for dynamic metabolic modeling

While the stoichiometry of metabolism is probably the best studied cellular level, the dynamics in metabolism can still not be well described, predicted and, thus, engineered. Unknowns in the metabolic flux behavior arise from kinetic interactions, especially allosteric control mechanisms. While the stoichiometry of enzymes is preserved in vitro, their activity and kinetic behavior differs from the in vivo situation. Next to this challenge, it is infeasible to test the interaction of each enzyme with each intracellular metabolite in vitro exhaustively. As a consequence, the whole interacting metabolome has to be studied in vivo to identify the relevant enzymes properties. In this review we discuss current approaches for in vivo perturbation experiments, that is, stimulus response experiments using different setups and quantitative analytical approaches, including dynamic carbon tracing. Next to reliable and informative data, advanced modeling approaches and computational tools are required to identify kinetic mechanisms and their parameters.The authors EV, AT, KN, IR, MO, DM and AW are part of the ERA-IB funded consortium DYNAMICS (ERA-IB-14-081, NWO 053.80.724)

A model of the PI cycle reveals the regulating roles of lipid-binding proteins and pitfalls of using mosaic biological data

The phosphatidylinositol (PI) cycle is central to eukaryotic cell signaling. Its complexity, due to the number of reactions and lipid and inositol phosphate intermediates involved makes it difficult to analyze experimentally. Computational modelling approaches are seen as a way forward to elucidate complex biological regulatory mechanisms when this cannot be achieved solely through experimental approaches. Whilst mathematical modelling is well established in informing biological systems, many models are often informed by data sourced from multiple unrelated cell types (mosaic data) or from purified enzyme data. In this work, we develop a model of the PI cycle informed by experimental and omics data taken from a single cell type, namely platelets. We were able to make a number of predictions regarding the regulation of PI cycle enzymes, the importance of the number of receptors required for successful GPCR signaling and the importance of lipid- and protein-binding proteins in regulating second messenger outputs. We then consider how pathway behavior differs, when fully informed by data for HeLa cells and show that model predictions remain consistent. However, when informed by mosaic experimental data model predictions greatly vary illustrating the risks of using mosaic datasets from unrelated cell types

Minimum Information About a Simulation Experiment (MIASE)

The original publication is available at www.ploscompbiol.orgReproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes.The discussions that led to the definition of MIASE benefited from the support of a Japan Partnering Award by the UK Biotechnology and Biological Sciences Research Council. DW was supported by the Marie Curie program and by the German Research Association (DFG Research Training School ‘‘dIEM oSiRiS’’ 1387/1). This publication is based on work (EJC) supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). FTB acknowledges support by the NIH (grant 1R01GM081070- 01). JC is supported by the European Commission, DG Information Society, through the Seventh Framework Programme of Information and Communication Technologies, under the VPH NoE project (grant number 223920). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Publishers versio

Toward community standards and software for whole-cell modeling

Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate, comprehensive models of complex cells. Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in SBML. Results: Our analysis revealed several challenges to representing WC models using the current standards. Conclusion: We, therefore, propose several new WC modeling standards, software, and databases. Significance:We anticipate that these new standards and software will enable more comprehensive models

Wnt Pathway in Bone Repair and Regeneration – What Do We Know So Far

Wnt signaling plays a central regulatory role across a remarkably diverse range of functions during embryonic development, including those involved in the formation of bone and cartilage. Wnt signaling continues to play a critical role in adult osteogenic differentiation of mesenchymal stem cells. Disruptions in this highly-conserved and complex system leads to various pathological conditions, including impaired bone healing, autoimmune diseases and malignant degeneration. For reconstructive surgeons, critically sized skeletal defects represent a major challenge. These are frequently associated with significant morbidity in both the recipient and donor sites. The Wnt pathway is an attractive therapeutic target with the potential to directly modulate stem cells responsible for skeletal tissue regeneration and promote bone growth, suggesting that Wnt factors could be used to promote bone healing after trauma. This review summarizes our current understanding of the essential role of the Wnt pathway in bone regeneration and repair

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing