Biochemical thermodynamics.

R A Alberty

doi:10.1016/0167-4838(94)90045-0

Home/Archive/PubMed

PubMed · 8043597

Biochemical thermodynamics.

R A Alberty

View original source ↗Export metadata ↓

Abstract

Biochemists need two types of reaction equations, chemical equations in terms of species and biochemical equations in terms of reactants at specified pH and concentrations of free metal ions that are bound by reactant species. Both types of reaction equations have corresponding equilibrium constants, K for chemical reactions and K' for biochemical reactions. When the pH is specified you enter a whole new world of thermodynamics. There are new thermodynamic properties, new names (transformed thermodynamic properties), and new values, which are quite different, especially for the standard transformed Gibbs energy. This raises nomenclature problems because it is important to be able to distinguish between chemical equations and biochemical equations at a glance. It is also important to distinguish between the standard thermodynamic properties calculated from K and its temperature coefficient and the standard transformed thermodynamic properties calculated from K' and its temperature coefficient.

Explore related subjects

Biochemistry Electrolytes Hydrogen-Ion Concentration Pressure Temperature Thermodynamics Journal Article Research Support, Non-U.S. Gov't Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

R A Alberty. 1994-07-20. Biochemical thermodynamics.. https://doi.org/10.1016/0167-4838(94)90045-0

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Molecular differences between young and mature stria vascularis from organotypic explants and transcriptomics.

The stria vascularis (SV) is an essential component of the inner ear that regulates the ionic environment required for hearing. SV degeneration disrupts cochlear homeostasis, leading to irreversible hearing loss, yet a comprehensive understanding of the SV, and consequently therapeutic availability for SV degeneration, is lacking. We developed a whole-tissue explant model from neonatal and mature mice to create a platform for advancing SV research. We validated our model by demonstrating that the proliferative behavior of the SV in vitro mimics SV in vivo. We also provided evidence for pharmacological experimentation by investigating the role of Wnt/β-catenin signaling in SV proliferation. Finally, we performed single-cell RNA sequencing from in vivo neonatal and mature mouse SV and surrounding tissue and revealed key genes and pathways that may play a role in SV proliferation and maintenance. Together, our results contribute new insights into investigating biological solutions for SV-associated hearing loss.

Biochemistry↗

PubMed2007

Manipulation of cell-sized phospholipid-coated microdroplets and their use as biochemical microreactors.

Cell-sized water droplets coated by a phospholipid layer mimicking the inner surface of living cells were manipulated by laser tweezers and used as biochemical microreactors. The cell-sized phospholipid-coated microdroplets (CPMDs) consisted of a water droplet in mineral oil with a diameter of 1-100 microm and coated by 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine. We monitored the time development of biochemical reactions in a single CPMD obtained after the controlled fusion of two CPMDs containing a substrate and an enzyme, respectively. We present results on two enzymatic reactions: calcein production in the presence of esterase and green fluorescence protein expression.

Biochemistry↗

PubMed2007

Classical versus stochastic kinetics modeling of biochemical reaction systems.

We study fundamental relationships between classical and stochastic chemical kinetics for general biochemical systems with elementary reactions. Analytical and numerical investigations show that intrinsic fluctuations may qualitatively and quantitatively affect both transient and stationary system behavior. Thus, we provide a theoretical understanding of the role that intrinsic fluctuations may play in inducing biochemical function. The mean concentration dynamics are governed by differential equations that are similar to the ones of classical chemical kinetics, expressed in terms of the stoichiometry matrix and time-dependent fluxes. However, each flux is decomposed into a macroscopic term, which accounts for the effect of mean reactant concentrations on the rate of product synthesis, and a mesoscopic term, which accounts for the effect of statistical correlations among interacting reactions. We demonstrate that the ability of a model to account for phenomena induced by intrinsic fluctuations may be seriously compromised if we do not include the mesoscopic fluxes. Unfortunately, computation of fluxes and mean concentration dynamics requires intensive Monte Carlo simulation. To circumvent the computational expense, we employ a moment closure scheme, which leads to differential equations that can be solved by standard numerical techniques to obtain more accurate approximations of fluxes and mean concentration dynamics than the ones obtained with the classical approach.

Biochemistry↗