Language
Country/Area
No result found
The secrets of metabolic networks: How do FBAs decipher biochemical reactions within cells?
In biochemistry, flux balance analysis (FBA) is a mathematical method designed to model the metabolism of cells or single-celled organisms such as E. coli or yeast. This method uses genome-scale metabolic network reconstruction to describe all biochemical reactions of an organism. With the advancement of science and technology, FBA is changing the way we understand biochemical reactions within cells, allowing us to delve deeper into how cells operate.
FBA provides a fast and efficient way to calculate the steady-state metabolic fluxes of large-scale models in seconds, which is very important for studying various biological processes.
Basic concepts of FBA
Flux balance analysis basically geometrizes the changes in the metabolic system and calculates them through mathematical formulas. Its working principle is to use a matrix (chemical equivalent matrix) and a vector of unknown flux to perform a dot product, and the expected result is to obtain a vector that represents the situation of the system at the steady state moment.
At steady state, the concentration of metabolites remains constant and the rates of production and consumption are balanced, resulting in no net change in mass over time.
Historical background of FBA
The research history of FBA can be traced back to the early 1980s. As time goes by, this technology has been continuously improved, and more and more scholars have begun to apply it to solve various biochemical problems. For example, Papoutsakis first demonstrated the possibility of using metabolic maps to construct flux balance equations, while Fell and Small incorporated more complex objective functions to study constraints on lipogenesis.
Simulation and Application
FBA's computational efficiency makes it easily applicable to research in many fields, including bioprocess engineering and drug target identification of pathogens. By systematically modifying microbial metabolic networks, researchers can increase the production of industrial chemicals such as ethanol and succinic acid.
Reaction deletion and interference research
Single reaction deletion is a commonly used technique aimed at searching metabolic networks for reactions critical to biomass production. By removing each reaction from the network one by one and measuring the predicted flux, these reactions can be classified as important or non-important.
Explanation of gene and reaction deletion
The association between genes and enzyme-catalyzed reactions can be assessed by so-called gene-protein-reaction expressions (GPR). It provides valuable information about which gene deletion may affect the response, which has important implications for pathological research.
Optimization of growth media
FBA has also demonstrated its strong potential in designing optimal growth media. By gradually changing the absorption limits of nutrients and applying FBA repeatedly, the nutrient combination that best benefits a specific phenotype can be found.
This process allows scientists to predict bacterial growth rates in different growth media and validate the accuracy of the prediction models.
Future Outlook of FBA
With the improvement of computing power and the convenience of data access, the application prospects of FBA have become more extensive. Future research may integrate FBA into more complex biological systems to gain a deeper understanding of the interaction between the microbiome and the host, thereby uncovering more mysteries of the human microbiome.
Through FBA technology, we can more effectively reveal the biochemical reactions within cells, but can this help us find new ways to treat diseases?
