相关实验视频

Updated: Jul 5, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

通过机器学习介导的Passiflora caerulea调生成优化优化

Marziyeh Jafari^1,2, Mohammad Hosein Daneshvar²

¹Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, Iran.

|January 24, 2024

概括

机器学习可以准确地预测Passiflora caerulea callogenesis的发生. 使用遗传算法优化植物生长调节器 (PGR) 和扩展型最大限度地提高了产量,为组织培养进步提供了强大的工具.

科学领域:

植物生物技术植物生物技术
计算生物学计算生物学
农业科学农业科学

背景情况:

呼生成对于Passiflora caerulea在体外二次代谢物生产和间接器官生成至关重要.
优化呼叫生成协议需要先进的计算方法.

研究的目的:

使用机器学习 (ML) 预测Passiflora caerulea中的生反应.
为了优化植物生长调节剂 (PGR) 度和扩增类型,以增强生.
通过灵敏度分析,评估PGR和扩展型对产生的影响.

主要方法:

多层感知子 (MLP) 用于建模生率和的新鲜重量.
爆炸物类型 (叶子,节点,内部节点) 和PGRs (2,4-D,BAP,NAA,IBA) 是不同的.
将MLP模型与遗传算法 (GA) 集成,用于优化和灵敏度分析.

主要成果:

对于呼生成参数,MLP表现出高预测精度 (R2>0.81).
确定了100%的生率的最佳条件:叶子扩张,具有特定度的IBA,NAA,2,4-D和BAP.
敏感性分析揭示了PGRs对产生的解释性依赖作用.

结论:

MLP和GA的组合有效地优化和预测Passiflora的体外培养系统.
这种方法提供了一个前性的解决方案,以应对Passiflora组织培养的挑战.
这些发现促进了高效的二次代谢物生产和间接的器官生成.

更多相关视频

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Published on: October 1, 2013

相关概念视频

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Adaptations that Reduce Water Loss

Adaptations that Reduce Water Loss

Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.

The Calvin Benson Cycle

The Calvin Benson Cycle

Ribulose 1,5- bisphosphate carboxylase/oxygenase (RuBisCo) is a critical enzyme that catalyzes carbon dioxide assimilation during photosynthesis. However, it is an inefficient enzyme, having an extremely slow catalytic rate. A typical enzyme can process about a thousand molecules per second; however, RuBisCo fixes only around three-carbon dioxides per second. Photosynthetic cells compensate for this slow rate by synthesizing very high amounts of RuBisCo, making it the most abundant single...