Integrated Mechanistic Models (Young Investigators Group)
Plant science has a core responsibility to both preserve the genetic diversity of our crops and to develop sustainable and efficient systems to produce food, energy and raw materials. Decoding the genomes of our leading crop species, specifically those of wheat, barley and oilseed rape, along with documenting their entire life cycle, will help determine the contribution of each gene, both individually and jointly, to the plants’ growth and development. In particular, this knowledge will facilitate a deeper understanding of how changes in either the physical environment (soil and climate) or the genome alter trait expression, be it related to the crop’s economic yield, its resistance to pests or its tolerance of abiotic stress.
Data science and IT have become central to our efforts to obtain meaningful biological insights from the ever-growing volume of genetic and phenotypic data being generated. Their application has the potential to develop the innovations in plant breeding methodology needed to address the pressing issues of feeding the world’s population and maintaining the supply of raw materials and energy.
The group is engaged in developing mathematical models to elucidate the molecular basis of both growth and development, and the regulation of metabolism. The complexity of these processes, along with the large volumes of heterogeneous data involved, require an integrative approach to be taken, in particular targeting the assembly of metabolic networks, which largely underlie trait expression. In addition to the assembly of metabolic models, several in silico-based and mathematical tools are also being used by the group, including graph theory, various statistical methods, machine learning, concepts derived from systems theory and control engineering, and stochastic and deterministic modelling.
Beyond attempting to decipher and understand growth and development, our goal is to develop models able to predict plant performance in response to changes in the environment. Such data- and mechanism-driven models could make a significant contribution to crop improvement as they would support a more rapid breeding response to climate change.
Understanding Seed Development through Metabolic Networks as Integrative Frameworks for Data Analysis
The perspective group IMM is part of the AVATARS consortium funded by the BMBF. This consortium aims to make the development of rapeseed tangible in a VR/AR environment. To this end, numerous test series will initially be carried out in the field and greenhouse combined with extensive molecular analyses of the rapeseed at various stages of development. The focus of IMM is to analyze the data obtained comprehensively. At the heart of the approach is a canola metabolic model.
Specifically, the aim is to link data from individual molecular levels. Changes at each of these levels must be processed through changes in metabolism. The approach is primarily aimed at better understanding how metabolic changes drive seed development. Ultimately, the goal is to develop a model that will allow breeders to predict germination ability under different climatic conditions.
A Glimpse into the Evolution of Photosynthesis through Metabolic Networks
All plants, algae, and some bacteria carry out photosynthesis, but photosynthesis does not occur in the same way. The most common form of photosynthesis in plants is C3 photosynthesis. Despite the dominance of C3 photosynthesis, it not using the most efficient form of carbon fixation.
An alternative, C4 photosynthesis, has evolved independently at least 62 times in 19 different plant families. Plants with the C4 property enhance their carbon fixation by using a biochemical pump. As a result, C4 plants, such as corn, have increased growth rates. Metabolic networks were used to model the two types of photosynthesis. Their simulations mapped the evolutionary progression from C3 to C4 photosynthesis as a function of carbon dioxide content.
The model also predicted a kind of intermediate state as the optimal solution under certain conditions and explained why so many different variants of C4 photosynthesis exist. It also showed that nitrogen and light were specific parameters that played a role in the evolution of C4 photosynthesis. The study demonstrates the power of using metabolic models to study the evolution of complex traits in plants. Simultaneously, the successful analysis of the C4 developmental process paves the way for a detailed study of C4 evolution and metabolism and sheds light on new targets for future breeding efforts in C4 crops.
A Dive into the Regulatory Mechanisms of Fruit Development in Tomato
Fruit development is divided into two phases: growth and ripening. During growth, the fruit mass increases steadily until the fruit reaches its final shape and size. The ripening process begins with the orange-reddish colouring of the fruit. During ripening, the fruit transforms its content, which makes the tomato edible and tasty.
Strictly timed regulatory mechanisms control the complex molecular changes happening during fruit development. Some components of the regulatory network, centred on ethylene, are known. However, data on various molecular levels at different developmental stages show a wide variety of changes, which the known regulatory mechanisms cannot explain.
This project aims to integrate different data sets to decipher previously unknown regulatory mechanisms. For this purpose, we combine statistical data analysis with process-based mathematical modelling. This workflow makes it possible to predict how changes in the genome affect the individual molecular levels and thus the growth and ripening processes. This information is also valuable to the breeder, whose interest is to optimize nutrient content, taste, yield and shelf life, among other things.
Blätke M-A, Beier S, Scholz U, Gladilin E, Szymanski J J (Eds.):
Front. Plant Sci., Frontiers Research Topic “Advances in Applied Bioinformatics in Crops." Lausanne: Frontiers Media SA (2021) https://dx.doi.org/10.3389/978-2-88966-620-1
Blätke M-A, Szymanski J J, Gladilin E, Scholz U, Beier S:
Editorial: Advances in applied bioinformatics in crops. Front. Plant Sci. 12 (2021) 640394. https://dx.doi.org/10.3389/fpls.2021.640394
Sahu A, Blätke M-A, Szymański J J, Töpfer N:
Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput. Struct. Biotechnol. J. 19 (2021) 4626-4640. https://doi.org/10.1016/j.csbj.2021.08.004