Chris Bakal: Look and learn

نویسنده

  • Caitlin Sedwick
چکیده

JCB • VOLUME 203 • NUMBER 3 • 2013 378 W hat can you learn about a cell just by looking at it? Quite a lot, as it turns out. A cell’s shape can tell you about its behavior and its function in the body, just for a start. And, as Chris Bakal can attest, a cell’s shape can also tell you a lot about the signaling pathways at work. Bakal has been interested in cellular signaling pathways ever since his undergraduate days (1), but his approach to studying them (2) has evolved considerably over time (3–5). Today, in his lab at London’s Institute of Cancer Research, Bakal’s lab members use computational neural networks and advanced statistical methods to identify and analyze cellular features that inform us about signaling networks. We called him to learn about this approach to studying cell shape and to get a wider view of his career.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A direct look at RNAi screens

Dynamical Cell Systems Laboratory, Division of Cancer Biology, Chester Beatty Laboratories, Institute of Cancer Research, London, UK * Corresponding author. Dynamical Cell Systems Laboratory, Division of Cancer Biology, Chester Beatty Laboratories, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK. Tel.: þ 44 20 7153 5080; Fax: þ 44 20 7153 5177; E-mail: E-mail: chris.bakal@icr....

متن کامل

Realizing the promise of RNAi high throughput screening.

Recently reporting in Nature, Collinet et al. describes the application of quantitative multiparametric methods to a genome-wide RNAi screen for regulators of endocytosis. The study illustrates the power of this approach beyond the identification of new endocytic components to providing insights into the design principles of the endocytic system.

متن کامل

Visualizing cellular imaging data using PhenoPlot

Visualization is essential for data interpretation, hypothesis formulation and communication of results. However, there is a paucity of visualization methods for image-derived data sets generated by high-content analysis in which complex cellular phenotypes are described as high-dimensional vectors of features. Here we present a visualization tool, PhenoPlot, which represents quantitative high-...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره 203  شماره 

صفحات  -

تاریخ انتشار 2013