Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Non-equilibrium in the Cell

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

Bioreactor Design and Operational System

Bioreactor Design and Operational System

Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...

Distributed Loads

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Critical assessment of machine learning approaches for classification, dynamic prediction and surrogate Modeling in food fermentation.

Food research international (Ottawa, Ont.)·2026

Same author

Identification of dynamic models of microbial communities: A workflow addressing identifiability and modeling pitfalls.

PLoS computational biology·2025

Same author

Conformal prediction for uncertainty quantification in dynamic biological systems.

PLoS computational biology·2025

Same author

Combined Isotopic Tracer and Modelling Approach Reveals Differences in Nitrogen Metabolism in S. cerevisiae, S. uvarum and S. kudriavzevii Species.

Microbial biotechnology·2025

Same author

An integrated multiphase dynamic genome-scale model explains batch fermentations led by species of the <i>Saccharomyces</i> genus.

mSystems·2025

Same author

A birth-death model to understand bacterial antimicrobial heteroresistance from time-kill curves.

Mathematical biosciences·2024

Same journal

Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice.

BMC systems biology·2019

Same journal

Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.

BMC systems biology·2019

Same journal

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.

BMC systems biology·2019

Same journal

A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

BMC systems biology·2019

Same journal

GNE: a deep learning framework for gene network inference by aggregating biological information.

BMC systems biology·2019

Same journal

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.

BMC systems biology·2019

See all related articles

Search research articles

Related Experiment Videos

Dynamic optimization of distributed biological systems using robust and efficient numerical techniques.

Carlos Vilas¹, Eva Balsa-Canto, Maria-Sonia G García

¹BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain.

BMC Systems Biology

|July 4, 2012

Summary

This summary is machine-generated.

This study introduces a novel computational method for optimizing spatially distributed biological systems. The approach efficiently designs perturbations to achieve desired system behaviors, demonstrated in bacterial chemotaxis and neural modeling.

Related Experiment Videos

Area of Science:

Systems biology
Computational biology
Mathematical modeling

Background:

Systems biology enables in silico experimentation to analyze biological system behavior.
Designing perturbations for specific biological outcomes is crucial but challenging, especially for systems described by partial differential equations.
Dynamic optimization problems for spatially distributed biological systems are complex due to nonlinear models, large scales, and constraints.

Purpose of the Study:

To address the numerical solution of dynamic optimization problems for spatially distributed biological systems.
To develop robust and efficient numerical techniques for designing perturbations in complex biological systems.
To overcome challenges like suboptimal solutions in dynamic optimization.

Main Methods:

Control vector parameterization approach.
Hybrid global optimization methods.
Reduced order model methodology.

Main Results:

Successfully applied the strategy to bacterial chemotaxis and the FitzHugh-Nagumo model.
Efficiently computed time-varying optimal chemotractant concentrations for predefined cell distributions in chemotaxis.
Demonstrated dynamic optimization's utility in guiding systems from undesired to desired patterns with minimal actuators.

Conclusions:

The proposed methodology provides an efficient approach for dynamic optimization of generic distributed biological systems.
Results for bacterial chemotaxis align with existing literature.
The FitzHugh-Nagumo model illustrates the power of dynamic optimization in pattern control.