Ron Weiss

Bio: Ron Weiss is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Speech synthesis. The author has an hindex of 82, co-authored 292 publications receiving 89189 citations. Previous affiliations of Ron Weiss include French Institute for Research in Computer Science and Automation & Google.

Composition and search with a video algebra

Abstract: A new data model called algebraic video provides operations for composing, searching, navigating,and playing back digital video presentations. A prototype system helps find video segments of interest from existing collections and create new video presentations with algebraic combinations of these segments. >

Stochastic Turing patterns in a synthetic bacterial population.

Abstract: The origin of biological morphology and form is one of the deepest problems in science, underlying our understanding of development and the functioning of living systems. In 1952, Alan Turing showed that chemical morphogenesis could arise from a linear instability of a spatially uniform state, giving rise to periodic pattern formation in reaction–diffusion systems but only those with a rapidly diffusing inhibitor and a slowly diffusing activator. These conditions are disappointingly hard to achieve in nature, and the role of Turing instabilities in biological pattern formation has been called into question. Recently, the theory was extended to include noisy activator–inhibitor birth and death processes. Surprisingly, this stochastic Turing theory predicts the existence of patterns over a wide range of parameters, in particular with no severe requirement on the ratio of activator–inhibitor diffusion coefficients. To explore whether this mechanism is viable in practice, we have genetically engineered a synthetic bacterial population in which the signaling molecules form a stochastic activator–inhibitor system. The synthetic pattern-forming gene circuit destabilizes an initially homogenous lawn of genetically engineered bacteria, producing disordered patterns with tunable features on a spatial scale much larger than that of a single cell. Spatial correlations of the experimental patterns agree quantitatively with the signature predicted by theory. These results show that Turing-type pattern-forming mechanisms, if driven by stochasticity, can potentially underlie a broad range of biological patterns. These findings provide the groundwork for a unified picture of biological morphogenesis, arising from a combination of stochastic gene expression and dynamical instabilities.

Programming gene and engineered-cell therapies with synthetic biology

Abstract: BACKGROUND Gene and engineered-cell therapies promise new treatment modalities for incurable or difficult-to-treat diseases First-generation gene and engineered-cell therapies are already used in the clinic, including an ex vivo gene-replacement therapy for adenosine deaminase deficiency, chimeric antigen receptor (CAR) T cell therapies for certain types of leukemias and lymphomas, an adeno-associated virus gene therapy for inherited retinal diseases, and investigational therapies for β-thalassemia, sickle cell disease, hemophilia, and spinal muscular atrophy Despite these early successes, safety concerns may hamper the broader adoption of some of these approaches: For example, overexpression of a therapeutic gene product with a narrow therapeutic window may be toxic, and excessive activation of T cells can be fatal More-sophisticated control over cellular activity would allow us to reliably “program” cells with therapeutic behaviors, leading to safer and more effective gene and engineered-cell therapies as well as new treatments ADVANCES Recent advances in synthetic biology are enabling new gene and engineered-cell therapies These developments include engineered biological sensors that can detect disease biomarkers such as microRNAs and cell-surface proteins; genetic sensors that respond to exogenous small molecules; and new methods for interacting with various components of the cell—editing DNA, modulating RNA, and interfacing with endogenous signaling networks These new biological modules have therapeutic potential on their own and can also serve as building blocks for sophisticated synthetic gene “circuits” that precisely control the strength, timing, and location of therapeutic function This advanced control over cellular behavior will facilitate the development of treatments that address the underlying molecular causes of disease as well as provide viable therapeutic strategies in situations where the biomolecular targets have been previously considered “undruggable” Recent publications have demonstrated several strategies for designing complex therapeutic genetic programs by combining basic sensor, regulatory, and effector modules These strategies include (i) external small-molecule regulation to control therapeutic activity postdelivery, (ii) sensors of cell-specific biomarkers that activate therapeutic activity only in diseased cells and tissues, and/or (iii) feedback control loops that maintain homeostasis of bodily systems Example therapeutic systems include a genetic circuit that senses two specific cell-surface markers to activate CAR T cells only in the presence of target cancer cells, a circuit that programmatically differentiates pancreatic progenitor cells into insulin-secreting β-like cells, and a gene network that senses the amount of psoriasis-associated cytokines to release immune-modulatory proteins only during flare-ups These proof-of-concept systems may lead to new treatments that are dramatically safer and more effective than current therapies OUTLOOK Rapid progress in synthetic biology and related fields is bringing therapeutic gene circuits ever closer to the clinic Ongoing efforts in modeling and simulating mammalian genetic circuits will reduce the number of circuit variants that need to be tested to achieve the desired behavior The platforms used to test genetic circuits are also evolving to more closely resemble the actual human environment in which the circuits will operate Human organoid, tissue-on-a-chip, and whole-blood models will enable higher-throughput circuit characterization and optimization in a more physiologically relevant setting Progress in nucleic acid delivery will improve the safety and efficiency with which therapeutic nucleic acids are introduced to target cells, and new methods for immunomodulation will suppress or mitigate unwanted immune responses Together, these advances will accelerate the development and adoption of synthetic biology-based gene and engineered-cell therapies

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

XGBoost: A Scalable Tree Boosting System

Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

The anatomy of a large-scale hypertextual Web search engine

Abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

The PageRank Citation Ranking : Bringing Order to the Web

Abstract: The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

XGBoost: A Scalable Tree Boosting System

Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.