Enzyme activities predicted by metabolite concentrations and solvent capacity in the cell

Probabilistic and maximum entropy modeling of chemical reaction systems: Characteristics and comparisons to mass action kinetic models

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell.

Transcriptomic Response of Differentiating Porcine Myotubes to Thermal Stress and Donor Piglet Age

Climate change is a current concern that directly and indirectly affects agriculture, especially the livestock sector. Neonatal piglets have a limited thermoregulatory capacity and are particularly stressed by ambient temperatures outside their optimal physiological range, which has a major impact on their survival rate. In this study, we focused on the effects of thermal stress (35 °C, 39 °C, and 41 °C compared to 37 °C) on differentiating myotubes derived from the satellite cells of Musculus rhomboideus, isolated from two different developmental stages of thermolabile 5-day-old (p5) and thermostable 20-day-old piglets (p20). Analysis revealed statistically significant differential expression genes (DEGs) between the different cultivation temperatures, with a higher number of genes responding to cold treatment. These DEGs were involved in the macromolecule degradation and actin kinase cytoskeleton categories and were observed at lower temperatures (35 °C), whereas at higher temperatures (39 °C and 41 °C), the protein transport system, endoplasmic reticulum system, and ATP activity were more pronounced. Gene expression profiling of HSP and RBM gene families, which are commonly associated with cold and heat responses, exhibited a pattern dependent on temperature variability. Moreover, thermal stress exhibited an inhibitory effect on cell cycle, with a more pronounced downregulation during cold stress driven by ADGR genes. Additionally, our analysis revealed DEGs from donors with an undeveloped thermoregulation capacity (p5) and those with a fully developed thermoregulation capacity (p20) under various cultivation temperature. The highest number of DEGs and significant GO terms was observed under temperatures of 35 °C and 37 °C. In particular, under 35 °C, the DEGs were enriched in insulin, thyroid hormone, and calcium signaling pathways. This result suggests that the different thermoregulatory capacities of the donor piglets determined the ability of the primary muscle cell culture to differentiate into myotubes at different temperatures. This work sheds new light on the underlying molecular mechanisms that govern piglet differentiating myotube response to thermal stress and can be leveraged to develop effective thermal management strategies to enhance skeletal muscle growth.

Quantitative modeling of pentose phosphate pathway response to oxidative stress reveals a cooperative regulatory strategy

Living cells use signaling and regulatory mechanisms to adapt to environmental stresses. Adaptation to oxidative stress involves the regulation of many enzymes in both glycolysis and pentose phosphate pathways (PPP), so as to support PPP-driven NADPH recycling for antioxidant defense. The underlying regulatory logic is investigated by developing a kinetic modeling approach fueled with metabolomics and 13C-fluxomics datasets from human fibroblast cells. Bayesian parameter estimation and phenotypic analysis of models highlight complementary roles for several metabolite-enzyme regulations. Specifically, carbon flux rerouting into PPP involves a tight coordination between the upregulation of G6PD activity concomitant to a decreased NADPH/NADP+ ratio and the differential control of downward and upward glycolytic fluxes through the joint inhibition of PGI and GAPD enzymes. Such functional interplay between distinct regulatory feedbacks promotes efficient detoxification and homeostasis response over a broad range of stress level, but can also explain paradoxical pertubation phenotypes for instance reported for 6PGD modulation in mammalian cells.

The formulation of chemical potentials and free energy changes in biochemical reactions

In 1994, an IUBMB-IUPAC joint committee recommended a revised formulation for standard chemical potentials and reaction free energies motivated by the fact that, in biochemistry, the reactants and products often exist in multiple charge states depending on the pH and pMg of the solution environment. The recommendation involved both the use of (1) a mathematical transform with the intent to hold the pH constant, and (2) the formulation of reference chemical potentials of ionized isomeric species based on the log sum of the individual standard chemical potentials of each isomeric species. Recently, several reports including a 2020 IUPAC report have appeared that challenged the need for such summary formulations, arguing that the standard chemical potentials were sufficient with full accounting of each of the different charge state isomers involved in a biochemical reaction. This work critically evaluates both the use of thermodynamic transforms and the different chemical potential formulations. It is shown that (1) transforms are not necessary to hold the pH constant and (2) demonstrates that the two chemical potential formulations are not equivalent. Which formulation is appropriate depends on what species are measured experimentally or whether an assumption of equilibrium among the charge state isomers is reasonable and desirable.

Machine learning for enzyme engineering, selection and design

Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.