The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black-box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models (GEMs) provide us with unprecedented detail about metabolic processes inside the cell. However, mostly due to computational complexity issues, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black-box models and modern approaches that integrate thermodynamics into GEMs. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield.

Phosphorus (P) is an essential non-renewable nutrient that frequently limits plant growth. It is the foundation of modern agriculture and, to a large extent, demand for P is met from phosphate rock deposits which are limited and becoming increasingly scarce. Adding an extra stroke to this already desolate picture is the fact that a high percentage of P, through agricultural runoff and waste, makes its way into rivers and oceans leading to eutrophication and collapse of ecosystems. Therefore, there is a critical need to practise P recovery from waste and establish a circular economy applicable to P resources. The potential of microalgae to uptake large quantities of P and use of this P enriched algal biomass as biofertiliser has been regarded as a promising way to redirect P from wastewater to the field. This also makes the study of molecular mechanisms underlying P uptake and storage in microalgae of great interest. In the present paper, we review phosphate models, which express the growth rate as a function of intra- and extracellular phosphorus content for better understanding of phosphate uptake and dynamics of phosphate pools.

Understanding microbial ecosystems means unlocking the path toward a deeper knowledge of the fundamental mechanisms of life. Engineered microbial communities are also extremely relevant to tackling some of today's grand societal challenges. Advanced meta-omics experimental techniques provide crucial insights into microbial communities, but have been so far mostly used for descriptive, exploratory approaches to answer the initial ‘who is there?’ question. An ecosystem is a complex network of dynamic spatio-temporal interactions among organisms as well as between organisms and the environment. Mathematical models with their abstraction capability are essential to capture the underlying phenomena and connect the different scales at which these systems act. Differential equation models and constraint-based stoichiometric models are deterministic approaches that can successfully provide a macroscopic description of the outcome from microscopic behaviors. In this mini-review, we present classical and recent applications of these modeling methods and illustrate the potential of their integration. Indeed, approaches that can capture multiple scales are needed in order to understand emergent patterns in ecosystems and their dynamics regulated by different spatio-temporal phenomena. We finally discuss promising examples of methods proposing the integration of differential equations with constraint-based stoichiometric models and argue that more work is needed in this direction.

The photosynthetic carbon reduction cycle, or Calvin–Benson–Bassham (CBB) cycle, is now contained in every standard biochemistry textbook. Although the cycle was already proposed in 1954, it is still the subject of intense research, and even the structure of the cycle, i.e. the exact series of reactions, is still under debate. The controversy about the cycle's structure was fuelled by the findings of Gibbs and Kandler in 1956 and 1957, when they observed that radioactive 14 CO 2 was dynamically incorporated in hexoses in a very atypical and asymmetrical way, a phenomenon later termed the ‘photosynthetic Gibbs effect’. Now, it is widely accepted that the photosynthetic Gibbs effect is not in contradiction to the reaction scheme proposed by CBB, but the arguments given have been largely qualitative and hand-waving. To fully appreciate the controversy and to understand the difficulties in interpreting the Gibbs effect, it is illustrative to illuminate the history of the discovery of the CBB cycle. We here give an account of central scientific advances and discoveries, which were essential prerequisites for the elucidation of the cycle. Placing the historic discoveries in the context of the modern textbook pathway scheme illustrates the complexity of the cycle and demonstrates why especially dynamic labelling experiments are far from easy to interpret. We conclude by arguing that it requires sound theoretical approaches to resolve conflicting interpretations and to provide consistent quantitative explanations.

Starch is the most widespread and abundant storage carbohydrate in plants and the main source of carbohydrate in the human diet. Owing to its remarkable properties and commercial applications, starch is still of growing interest. Its unique granular structure made of intercalated layers of amylopectin and amylose has been unraveled thanks to recent progress in microscopic imaging, but the origin of such periodicity is still under debate. Both amylose and amylopectin are made of linear chains of α-1,4-bound glucose residues, with branch points formed by α-1,6 linkages. The net difference in the distribution of chain lengths and the branching pattern of amylose (mainly linear), compared with amylopectin (racemose structure), leads to different physico-chemical properties. Amylose is an amorphous and soluble polysaccharide, whereas amylopectin is insoluble and exhibits a highly organized structure of densely packed double helices formed between neighboring linear chains. Contrarily to starch degradation that has been investigated since the early 20th century, starch production is still poorly understood. Most enzymes involved in starch growth (elongation, branching, debranching, and partial hydrolysis) are now identified. However, their specific action, their interplay (cooperative or competitive), and their kinetic properties are still largely unknown. After reviewing recent results on starch structure and starch growth and degradation enzymatic activity, we discuss recent results and current challenges for growing polysaccharides on granular surface. Finally, we highlight the importance of novel stochastic models to support the analysis of recent and complex experimental results, and to address how macroscopic properties emerge from enzymatic activity and structural rearrangements.

Along with the development of several large-scale methods such as mass spectrometry or micro arrays, genome wide models became not only a possibility but an obvious tool for theoretical biologists to integrate and analyse complex biological data. Nevertheless, incorporating the dynamics of photosynthesis remains one of the major challenges while reconstructing metabolic networks of plants and other photosynthetic organisms. In this review, we aim to provide arguments that small-scale models are still a suitable choice when it comes to discovering organisational principles governing the design of biological systems. We give a brief overview of recent modelling efforts in understanding the interplay between rapid, photoprotective mechanisms and the redox balance within the thylakoid membrane, discussing the applicability of a reductionist approach in modelling self-regulation in plants and outline possible directions for further research.