Abstract
Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.
Highlights
Elucidating the regulatory structure and dynamics of gene networks is a major objective in biology
Gap Gene Expression Patterns: mRNA vs. Protein Over the last two decades, the potential of reverse engineering has been demonstrated by a pioneering case study—led by John Reinitz and colleagues—where gene circuits have been used to characterise and analyse the gap gene network in Drosophila melanogaster [40,45,46,47,48,49,50,51]
We address the question whether it can still be used to reconstruct the regulatory structure and dynamics of the gap gene system in a manner which is consistent with previous efforts based on modelling, as well as genetic and molecular approaches to study gap gene regulation
Summary
Elucidating the regulatory structure and dynamics of gene networks is a major objective in biology. Many reverse engineering studies aim to determine regulatory structure from large-scale perturbation- or time-series data based on microarray or transcriptome-sequencing technology (reviewed in [5,17]). This approach has two significant limitations: first, spatial information on gene expression is lost, since homogenised tissue samples or disaggregated cells are studied. There are many important biological questions that absolutely require consideration of non-linear and spatial aspects of a system We discuss such a case, and show that reverse engineering can be used for its study with a reasonable amount of experimental and computational effort
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