Understanding microbial growth with the use of mathematical models has a long history that dates back to the pioneering work of Jacques Monod in the 1940s. Monod’s famous growth law expressed microbial growth rate as a simple function of the limiting nutrient concentration. However, to explain growth laws from underlying principles is extremely challenging. In the second half of the 20th century, numerous experimental approaches aimed at precisely measuring heat production during microbial growth to determine the entropy balance in a growing cell and to quantify the exported entropy. This has led to the development of thermodynamic theories of microbial growth, which have generated fundamental understanding and identified the principal limitations of the growth process. Although these approaches ignored metabolic details and instead considered microbial metabolism as a black box, modern theories heavily rely on genomic resources to describe and model metabolism in great detail to explain microbial growth. Interestingly, however, thermodynamic constraints are often included in modern modeling approaches only in a rather superficial fashion, and it appears that recent modeling approaches and classical theories are rather disconnected fields. To stimulate a closer interaction between these fields, we here review various theoretical approaches that aim at describing microbial growth based on thermodynamics and outline the resulting thermodynamic limits and optimality principles. We start with classical black box models of cellular growth, and continue with recent metabolic modeling approaches that include thermodynamics, before we place these models in the context of fundamental considerations based on non-equilibrium statistical mechanics. We conclude by identifying conceptual overlaps between the fields and suggest how the various types of theories and models can be integrated. We outline how concepts from one approach may help to inform or constrain another, and we demonstrate how genome-scale models can be used to infer key black box parameters, such as the energy of formation or the degree of reduction of biomass. Such integration will allow understanding to what extent microbes can be viewed as thermodynamic machines, and how close they operate to theoretical optima.

In this paper we study the consequences of relaxing the hypothesis of the pressureless nature of the dark matter component when determining constraints on dark energy. To this aim we consider simple generalized dark matter models with constant equation of state parameter. We find that present-day low-redshift probes (type-Ia supernovae and baryonic acoustic oscillations) lead to a complete degeneracy between the dark energy and the dark matter sectors. However, adding the cosmic microwave background (CMB) high-redshift probe restores constraints similar to those on the standard $\mathrm{\Lambda }\mathrm{CDM}$ model. We then examine the anticipated constraints from the future Euclid survey on the same class of models, using a Fisher forecast estimation. We show that the Euclid survey allows us to break the degeneracy between the dark sectors, although the constraints on dark energy are much weaker than with standard dark matter. The use of CMB in combination allows us to restore the high precision on the dark energy sector constraints.

The amplitude of the photoacoustic signal is measured as a function of chopping frequency for thin benzophenone polycrystalline samples in the temperature range 100–300 K. Analysis of experimental results, obtained by rear surface illumination, allows us to determine the thermal diffusivity of benzophenone crystals in the whole temperature range.

We introduce a model for stochastic transport on a one-dimensional substrate with particles assuming different conformations during their stepping cycles. These conformations correspond to different footprints on the substrate: in order to advance, particles are subject to successive contraction and expansion steps with different characteristic rates. We thus extend the paradigmatic exclusion process, provide predictions for all regimes of these rates that are in excellent agreement with simulations, and show that the current-density relation may be affected considerably. Symmetries are discussed, and exploited. We discuss our results in the context of molecular motors, confronting a hand-over-hand and an inchworm stepping mechanism, as well as for ribosomes.

To understand microbial growth with mathematical models has a long tradition that dates back to the pioneering work of Jacques Monod in the 1940s. Growth laws are simple mathematical expressions that aim at describing growth rates of microbes as functions of external parameters, in particular nutrient concentrations. These laws are now widely applied to construct, e.g., dynamic ecosystem models. However, to explain the growth laws from underlying (first) principles is extremely challenging. In the second half of the 20th century, numerous experimental approaches aimed at precisely measuring heat production during microbial growth to determine the entropy balance in a growing cell and to quantify the exported entropy. This has led to the development of thermodynamic theories of microbial growth, which have generated fundamental understanding and identified principle limitations of the growth process. Whereas these approaches considered a growing microbe as a black box, modern theories heavily rely on genomic resources to describe and model genome-scale networks to explain microbial growth. Interestingly, however, thermodynamic constraints are often included in modern modelling approaches only in a rather superficial fashion, and it appears that recent modelling approaches and classical theories are disconnected fields. In order to stimulate a closer interaction between these fields, we here review various theoretical approaches that aim at describing microbial growth based on thermodynamic principles. We start with classical black-box models of cellular growth, and continue with genome-scale modelling approaches that include thermodynamics, before we place these models in the context of fundamental considerations based on non-equilibrium statistical mechanics. We conclude by identifying conceptual overlaps between the fields and suggest how the various types of theories and models can be integrated. We outline how concepts from one approach may help to inform or constrain another, and we demonstrate how genome-scale models can be used to infer classical black-box parameters, which are experimentally accessible in growth experiments. Such integration will allow understanding to what extent microbes can be viewed as thermodynamic machines, and how close they operate to theoretical optima.