Data Management and Modeling in Plant Biology

Abstract

The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.

Keywords: differential equations; genome-scale networks; machine learning; mathematical modeling; metabolic regulation; omics analysis; plant-environment interactions.

Figure 1

Number of articles found by article search in the PubMed® library covering 2 decades, i.e., 2000–2020 (https://pubmed.ncbi.nlm.nih.gov). (A) Timeline of number of articles on different omics disciplines (blue: genomics; orange: transcriptomics; gray: proteomics; and yellow: metabolomics). Articles were searched by single key word search, (B) Timeline of number of articles found by search on omics data integration (green line; single words were connected by AND-expression) and multi-omics (or multiomics, blue line).

Figure 2

An artificial neural network. Information, i.e., x₁, x₂, and x₃, enters the network via the input layer (layer 1, blue). Weights w_ij determine the quantity by which information is passed to layer 2 (hidden, green). Processed information, here y_1n and y_2n, leaves the network by the output layer (layer n, yellow). Indices refer to neuron number and layer number, respectively. Calculations for neurons are depicted exemplarily for h₁₂ (first neuron in second layer), which is composed of a bias (b₁₂) and summed information of the previous layer, here layer 1. Resulting information, y₁₂, is passed to the next layer and might comprise nonlinearities in f(h₁₂). Deep neural networks typically comprise several and up to numerous hidden layers, indicated in grey.

Figure 3

Conceptual workflow for data management and modeling in plant sciences.