The plant faces different pedological and climatic challenges that influence its growth and enhancement. While, plant-microbes interactions throught the rhizosphere offer several privileges to this hotspot in the service of plant, by attracting multi-beneficial mutualistic and symbiotic microorganisms as plant growth-promoting bacteria (PGPB), archaea, mycorrhizal fungi, endophytic fungi, and others…). Currently, numerous investigations showed the beneficial effects of these microbes on growth and plant health. Indeed, rhizospheric microorganisms offer to host plants the essential assimilable nutrients, stimulate the growth and development of host plants, and induce antibiotics production. They also attributed to host plants numerous phenotypes involved in the increase the resistance to abiotic and biotic stresses. The investigations and the studies on the rhizosphere can offer a way to find a biological and sustainable solution to confront these environmental problems. Therefore, the interactions between microbes and plants may lead to interesting biotechnological applications on plant improvement and the adaptation in different climates to obtain a biological sustainable agricultures without the use of chemical fertilizers.

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Plasma membrane transporters play pivotal roles in the import of nutrients, including sugars, amino acids, nucleobases, carboxylic acids, and metal ions, that surround fungal cells. The selective removal of these transporters by endocytosis is one of the most important regulatory mechanisms that ensures a rapid adaptation of cells to the changing environment (e.g., nutrient fluctuations or different stresses). At the heart of this mechanism lies a network of proteins that includes the arrestin-related trafficking adaptors (ARTs) which link the ubiquitin ligase Rsp5 to nutrient transporters and endocytic factors. Transporter conformational changes, as well as dynamic interactions between its cytosolic termini/loops and with lipids of the plasma membrane, are also critical during the endocytic process. Here, we review the current knowledge and recent findings on the molecular mechanisms involved in nutrient transporter endocytosis, both in the budding yeast Saccharomyces cerevisiae and in some species of the filamentous fungus Aspergillus. We elaborate on the physiological importance of tightly regulated endocytosis for cellular fitness under dynamic conditions found in nature and highlight how further understanding and engineering of this process is essential to maximize titer, rate and yield (TRY)-values of engineered cell factories in industrial biotechnological processes.