How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes

In response to increasing Arctic temperatures, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowlands, polygonal ice wedges are especially prone to degradation. Melting of ice wedges results in deepening troughs and the transition from low-centered to high-centered ice-wedge polygons. This process has important implications for surface hydrology, as the connectivity of such troughs determines the rate of drainage for these lowland landscapes. In this study, we present a comprehensive, modular, and highly automated workflow to extract, to represent, and to analyze remotely sensed ice-wedge polygonal trough networks as a graph (i.e., network structure). With computer vision methods, we efficiently extract the trough locations as well as their geomorphometric information on trough depth and width from high-resolution digital elevation models and link these data within the graph. Further, we present and discuss the benefits of graph analysis algorithms for characterizing the erosional development of such thaw-affected landscapes. Based on our graph analysis, we show how thaw subsidence has progressed between 2009 and 2019 following burning at the Anaktuvuk River fire scar in northern Alaska, USA. We observed a considerable increase in the number of discernible troughs within the study area, while simultaneously the number of disconnected networks decreased from 54 small networks in 2009 to only six considerably larger disconnected networks in 2019. On average, the width of the troughs has increased by 13.86%, while the average depth has slightly decreased by 10.31%. Overall, our new automated approach allows for monitoring ice-wedge dynamics in unprecedented spatial detail, while simultaneously reducing the data to quantifiable geometric measures and spatial relationships.BMBF PermaRiskNational Science FoundationPeer Reviewe

Augmenting biological pathway extraction with synthetic data and active learning

The corpus of biomedical literature is growing rapidly as many papers are recorded in PubMed every day. These papers often contain high-quality biological pathways in their figures/text, which are great resources for studying biological mechanisms and precision medicine. However, it can take a long time for many of these works to be put into practical use as each paper's contributions need to be curated by experts. This, often lengthy, process causes professional practice to lag behind research. To speed up this process, I helped develop a pipeline that integrates NLP and object detection processing to extract gene relationships reported in articles' figures and text. This pipeline was able to extract such relationships with high precision and recall on a small, annotated set. However, extending this pipeline for improved generalization and new settings was limited by the number of high-quality annotations available. Such labeled data is very time consuming to collect and traditional augmentations were observed to generate diminishing returns. To address this shortcoming, I developed an approach for generating purely synthetic data for object detection on biological pathway diagrams based on a set of rules and domain knowledge. Our method iteratively generates each pathway relationship uniquely and is demonstrated to improve the generalization of our object detection model significantly across a variety of settings. Additionally, with the capability to generate unique and informative samples, we integrated our synthetic generation methodology into an active learning setting. While traditional active learning relies on a pool of unlabeled data to draw from with an acquisition function, our method is pool-less and does not require any acquisition function. Instead, we generate each batch of data uniquely based on the training losses from the previous batch. Pool-less Active Learning (PAL) via synthetic data generation is demonstrated to reduce the number of iterations required for model convergence during training on pathway figures.Includes bibliographical references

Preparation and Characterization Of Oil-in-water Nano-emulsions Of Trifluoperazine For Parenteral Drug Delivery

Objectives: 1.) To develop and characterize an optimal formulation of oil-in-water nano-emulsions of trifluoperazine for parenteral delivery. 2.) To perform short term stability testing of the optimal formulation and monitor the potency using high performance chromatography (HPLC). Materials and Methods: Emulsions containing soybean oil, water, trifluoperazine hcl as an amphiphilic drug, phospholipon 90 and Tween 80 as surfactant blend were prepared using the Thin-layer hydration method. Z-average, polydispersity index, zeta potential of emulsions were determined. A fully randomized 2X2X2X2X2 statistical design was developed using JMP software. Optimal formulation was selected based on desirable properties of low z-average and polydispersity index, and high zeta potential. Stability of optimal formulation was determined using HPLC analysis and based on ICH specifications. Results: Z-average of optimal formulation was 72.9nm with zeta potential value of 25.59 mV and polydispersity index 0.2. After storage for 3 months, z-average values were below 200nm indicating optimal formulation was not physically degraded. Drug content analysis showed chemical degradation due to reduction of potency. Conclusions: Trifluoperazine nano-emulsions formulations had acceptable values of low z-average, low polydispersity index and high zeta potential and were physically stable but not chemically stable over 3 months

Building a cascading multi-product biorefinery process for Ascophyllum nodosum: a green chemistry approach

Brown macroalgae are an attractive third-generation feedstock for biofuel, as well as a source of natural products. A cascading biorefinery approach extracts potentially bioactive compounds, i.e., polyphenols, fucoidan, and commodity products i.e., alginate, proteins in the same process. In order to design a green chemistry-compliant process and reduce the use of organic solvents in bioactive product extraction, aqueous two-phase systems (ATPS) and low-concentration biodegradable acid extractions were applied. The present work aimed to develop a multi-product biorefinery concept using Ascophyllum nodosum as a model feedstock using life cycle assessment (LCA), techno-economic analysis (TEA), and technical feasibility trials (TFT) as early-design tools for its development. After a biochemical characterisation of three potential model species, A. nodosum was selected as model feedstock based on the high accumulation of high-value products with potential breakthrough in the market. A.nodosum exhibited higher contents of polyphenols, lipids, protein, and minerals than the other species analysed, with 4.63% DW, 8.13% DW, 11.33% DW, and 29.54% DW, respectively. Once the biochemical characterisation was completed, three biorefining scenarios using different technology pathways (solvent, physicochemical, and green techniques) were modelled to process 1,000 metric tonnes (MT) biomass/year, in order to evaluate their economic and environmental metrics. From all evaluated scenarios, a green chemistry-compliant cascading sequence showed the lowest capital expenditure (CAPEX) (£30 million), operational expenditure (OPEX) (£11 million), cost of goods per kg of feedstock processed (CoG/kg) (£0.08) and production costs (£0.03/kg), along with the highest internal rate of return (IRR) (75.0%). Additionally, this scenario exhibited the lowest environmental impacts in all categories assessed, around 2 – 10 times lower than the other scenarios. In addition, the cascading sequence performance was evaluated to obtain first-hand data and re-iterate the models. The cascading sequence approach has been proposed to maximise resource efficiency and, in this work, a cascading sequence aimed at the sequential extraction of fucoidan, alginate, polyphenols, and proteins. Bioprocessing hotspots were identified for polyphenol and fucoidan extraction steps and further optimised using automated high-throughput screenings (HTS) and Design of Experiments (DoE), recovering 89% of total polyphenols, and showing a 33% increase in fucoidan recovery. Finally, after completing the bioprocessing hotspots, economic and environmental models were re-iterated to confirm the robustness of the biorefinery concept developed. The re-iterated version of the green chemistry-compliant cascading sequence exhibited better recovery performance in the optimised extraction stages, and thus showed better sales revenues (£91 million) than its previous version, a higher IRR (82.1%), and lower CoG/kg (£0.05) and production costs (£0.01/kg). The re-iterated version of the cascading sequence also exhibited the lowest environmental impacts in every category assessed, of all scenarios analysed in this project. These findings confirm that a holistic approach to early bioprocesses design is a valuable addition for decision-making tool options in the development of green-compliant multi-product third generation biorefineries