Elucidating multi-input processing 3-node gene regulatory network topologies capable of generating striped gene expression patterns.

Jayson Gutiérrez,Boris A Rodríguez,David M Umulis,Juan Camilo Arboleda-Rivera,Gloria Machado-Rodríguez

doi:10.1371/journal.pcbi.1009704

Jayson Gutiérrez, Boris A Rodríguez + Show 3 more

Open Access

https://doi.org/10.1371/journal.pcbi.1009704

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Abstract

A central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cell’s gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps. In this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms. Out of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly.

Highlights

Cells interact continuously with their environment, which requires precise regulatory strategies to avoid potential detrimental responses
We use an in silico approach to probe the design space of multi-input, three-node Gene Regulatory Networks (GRNs) capable of generating a striped gene expression pattern in the context of a simplified 1-D morphogenetic field
The classification of GRNs showed that our initial set of 2061 GRNs can be grouped into 714 distinct network topology classes, with each containing a variable number of GRNs (Fig 2)

Summary

Introduction

Cells interact continuously with their environment, which requires precise regulatory strategies to avoid potential detrimental responses. Most cellular functions arise from the dynamic activity of Gene Regulatory Networks (GRNs), which play a central role in interpreting external and internal signals. This information processing function is critical in developmental processes such as those in which a group of cells differentiates in response to a signaling molecule. Such molecules were referred to as morphogens by Turing in 1952 [1], and posterior theoretical studies on patterning led to the conceptualization of the French Flag Problem by Wolpert, who stated in this respect that a gradient of concentration of a morphogen could trigger cell differentiation in a one-dimensional field of cells [2, 3]. Information processing by GRNs has proven to be a complex process, and a major goal of developmental biology is to understand mechanistically how positional information conveyed by morphogens is translated into spatial differentiation

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