Dynamics and Design Principles of a Basic Regulatory Architecture Controlling Metabolic Pathways

Chen-Shan Chin,Victor Chubukov,Hao Li,Emmitt R Jolly,Joe Derisi,Andre Levchenko

doi:10.1371/journal.pbio.0060146

Abstract

The dynamic features of a genetic network's response to environmental fluctuations represent essential functional specifications and thus may constrain the possible choices of network architecture and kinetic parameters. To explore the connection between dynamics and network design, we have analyzed a general regulatory architecture that is commonly found in many metabolic pathways. Such architecture is characterized by a dual control mechanism, with end product feedback inhibition and transcriptional regulation mediated by an intermediate metabolite. As a case study, we measured with high temporal resolution the induction profiles of the enzymes in the leucine biosynthetic pathway in response to leucine depletion, using an automated system for monitoring protein expression levels in single cells. All the genes in the pathway are known to be coregulated by the same transcription factors, but we observed drastically different dynamic responses for enzymes upstream and immediately downstream of the key control point—the intermediate metabolite α-isopropylmalate (αIPM), which couples metabolic activity to transcriptional regulation. Analysis based on genetic perturbations suggests that the observed dynamics are due to differential regulation by the leucine branch-specific transcription factor Leu3, and that the downstream enzymes are strictly controlled and highly expressed only when αIPM is available. These observations allow us to build a simplified mathematical model that accounts for the observed dynamics and can correctly predict the pathway's response to new perturbations. Our model also suggests that transient dynamics and steady state can be separately tuned and that the high induction levels of the downstream enzymes are necessary for fast leucine recovery. It is likely that principles emerging from this work can reveal how gene regulation has evolved to optimize performance in other metabolic pathways with similar architecture.

Highlights

In the past several years, genomic approaches have dramatically accelerated the discovery of biological regulatory networks
Our system consists of parallel batch cultures, in which yeast strains with different genes tagged by green fluorescent protein (GFP) are grown, connected to a flow cytometer by a syringe pump
Since a large number of cells (;105 to 106) are sampled in short time intervals (1 to 7 min per sample), we obtain accurate and highly reproducible induction profiles. It had been shown in a genome-wide study that measuring protein abundance by GFP tagging and flow cytometry yields highly reproducible data and is less susceptible to experimental variation compared with other techniques such as Western blotting or mRNA microarray measurements [21]

Summary

Introduction

In the past several years, genomic approaches have dramatically accelerated the discovery of biological regulatory networks. Combined with detailed biochemical and genetic studies, these approaches have yielded the intricate wiring diagrams for many biological systems Revealing, such wiring diagrams are usually drawn as arrows representing activation or repression that link regulators with the genes they regulate, and typically, one can only make qualitative statements (such as whether a gene is activated or repressed) based on network architecture. To understand the functional significance and design principles of complex regulatory networks, it is essential to analyze the dynamical output quantitatively. In engineering systems such as feedback control, specifications for dynamics—such as speed of the response and settling time—strongly constrain the possible choices of network architecture and control parameters [1]. A number of recent studies have explored the connection between architecture, dynamics, and fitness; examples include rationalizing simple network motifs by their dynamical response properties [3,4], connecting just-in-time expression with fitness advantage [5], and justifying seemingly redundant regulatory mechanisms by their contributions to the different aspects of the dynamical response [6]

Methods

Results

Conclusion