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Related Concept Videos

Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Genomics02:02

Genomics

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Overview of Algae01:28

Overview of Algae

The kingdom Archaeplastida encompasses red and green algae, along with land plants. Unlike other protists with chloroplasts that arose through secondary endosymbiosis, only red and green algae originated from primary endosymbiotic events. This diverse group of eukaryotic organisms contains chlorophyll and performs oxygenic photosynthesis.Algae exist in various forms, from large brown kelp in coastal waters to green scum in puddles and stains on rocks or soil. Some species are responsible for...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...

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Related Experiment Video

Updated: Jun 27, 2026

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.

Diego Tec-Campos1, Manish Kumar1, Natalia Parra2

  • 1Department of Pediatrics, University of California, 9500 Gilman Drive, San Diego, CA 92093, USA.

Bioengineering (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Algal systems biology uses omics, metabolic models, and AI to predict algae physiology and optimize biotechnology applications like biofuels and carbon capture. These integrated approaches advance understanding of metabolic regulation and environmental adaptation.

Keywords:
algal systemsdata-driven modelinggenome-scale modelsomics toolssystems biology

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A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

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Last Updated: Jun 27, 2026

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
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A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii
07:51

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Published on: August 8, 2019

Area of Science:

  • * Algal biology and systems biology.
  • * Biotechnology and metabolic engineering.

Background:

  • * Algae are metabolically diverse photosynthetic organisms crucial for global biogeochemical cycles.
  • * Their complex metabolism presents challenges for predictive physiological understanding.
  • * Algae hold significant potential for biotechnology applications, including biofuels, nutraceuticals, and carbon capture.

Purpose of the Study:

  • * To review the current state of algal systems biology.
  • * To highlight advances in omics technologies and modeling approaches.
  • * To discuss the integration of these methods for predictive algal biotechnology.

Main Methods:

  • * Integration of omics data (genomics, transcriptomics, proteomics, metabolomics).
  • * Development and application of genome-scale metabolic models (GEMs).
  • * Utilization of machine learning and artificial intelligence for physiological data analysis.

Main Results:

  • * Omics technologies provide mechanistic insights into algal metabolic regulation and adaptation.
  • * Algal GEMs are effective in predicting metabolic flux, nutrient use, and lipid biosynthesis.
  • * Data-driven methods and AI model algal growth and cultivation performance.

Conclusions:

  • * Systems biology approaches are transforming algal research toward predictive frameworks.
  • * Hybrid modeling strategies integrating mechanistic and data-driven methods are emerging.
  • * These integrated frameworks will enable next-generation predictive algal biotechnology and guide metabolic engineering.