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Plant sciences plays an essential role in preserving crop diversity and in the sustainable and efficient production of food, energy and raw materials. However, this is only possible by decoding the genetic material of crops such as wheat, barley and rapeseed and collecting data on the entire life cycle from seed to harvest.

This data helps us determine what each gene in the genome is responsible for and how they interact. This knowledge allows us to understand how changes in the environment and climate or the genome affect a plant's traits, such as yield, resistance to pests, and even heat and drought.

Data sciences and computational tools are indispensable key technologies to gain the necessary insights from vast amounts of data. Only through their consistent use will it be possible to develop plant breeding innovations that will allow us to fathom pressing questions about world food, raw material and energy supply.

The perspective group IMM uses mathematical models and computational biology to decipher molecular mechanisms involved in plant growth and development processes and regulating metabolism. Due to the complexity of these processes and the large quantities and variety of data, integrative approaches are needed to describe and investigate data in their context. At the centre of these integrative approaches are so-called metabolic networks. The metabolism reflects the expression of genetic information and directly affects a plant's traits. Several computer-based and mathematical tools are used in addition to methods for analyzing metabolic models, including graph theory, statistical methods, machine learning, concepts from systems theory and control engineering, and stochastic and deterministic modelling approaches.

The integrative approach aims not only to decipher and understand growth and developmental processes but also to build predictive models accounting for a plant's performance in different environments. These data- and mechanism-based predictive models are critical in plant breeding to react instantly and efficiently to new climatic conditions in the course of 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 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.

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.