Gene regulatory networks in plants: learning causality from time and perturbation

Krouk, Gabriel; Lingeman, Jesse; Colon, Amy Marshall; Coruzzi, Gloria; Shasha, Dennis

Gene regulatory networks in plants: learning causality from time and perturbation

2013

Krouk, Gabriel | Lingeman, Jesse | Colon, Amy Marshall | Coruzzi, Gloria | Shasha, Dennis

The goal of systems biology is to generate models for predicting how a system will react under untested conditions or in response to genetic perturbations. This paper discusses experimental and analytical approaches to deriving causal relationships in gene regulatory networks.

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Ключевые слова АГРОВОК

artificial intelligence gene expression regulation gene regulatory networks genes genetics methods models models plant plant plant plants prediction

Библиографическая информация

Опубликовано в

Genome biology

Том 14 Выпуск 6 Нумерация страниц 123 - 123 ISSN 1474-760X

Издатель

BioMed Central

Другие темы

Regulator genes; Statistics numerical data; Genetic; Genome; Signal transduction; Systems biology

Язык

Английский

Лицензия

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Тип

Journal Article; Text

В АГРИСе с: 2024-02-28

Формат: MODS

Поставщик данных

Эту запись предоставил National Agricultural Library

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Ссылки

DOI DOI http://dx.doi.org/10.1186/gb-2013-14-6-123

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