Biology subjects

Lucas Carey

Publications and source records attributed to Lucas Carey.

3 recordsLinked to original sources

bioRxiv2015

Competition between binding sites determines gene expression at low transcription factor concentrations

The response of gene expression to intra-and extra-cellular cues is largely mediated through changes in the activity of transcription factors (TFs), whose sequence specificities are largely known. However, the rules by which promoters decode the amount of active TF into gene expression are not well understood. Here, we measure the activity of 6500 designed promoters at six different levels of TF activity in budding yeast. We observe that maximum promoter activity is determined by TF activity and not by the number of sites. Surprisingly, the addition of an activator-binding site often reduces expression. A thermodynamic model that incorporates competition between neighboring binding sites for a local pool of TF molecules explains this behavior and accurately predicts both absolute expression and the amount by which addition of a site increases or reduces expression. Taken together, our findings support a model in which neighboring binding sites interact competitively when TF is limiting but otherwise act additively\n\nSignificance StatementIn response to intracellular and extracellular signals organisms alter the concentration and activity of transcription factors (TFs), proteins that regulate gene expression. However, the molecular mechanisms that determine the response of a target promoter to changes in the number of active TF molecules are not well understood. By combining mathematical modeling with measurements of TF dose-response curves for thousands of designed promoters, we show that competition for active TF molecules is a major factor in determining gene expression. At low TF concentrations additional activator-binding sites within a promoter can actually reduce expression. Thermodynamic modeling suggests that steric hindrance between neighboring binding sites cannot explain this behavior, but that competition for limiting TF molecules can.

Systems Biology↗

bioRxiv2015

A synthetic gene circuit for measuring autoregulatory feedback control.

Auto regulatory feedback loops occur in the regulation of molecules ranging from ATP to MAP kinases to zinc. Negative feedback loops can increase a systems robustness, while positive feedback loops can mediate transitions between cell states. Recent genome-wide experimental and computational studies predict hundreds of novel feedback loops. However, not all physical interactions are regulatory, and many experimental methods cannot detect self-interactions. Our understanding of regulatory feedback loops is therefore hampered by the lack of high-throughput methods to experimentally quantify the presence, strength, and temporal dynamics of auto regulatory feedback loops. Here we present a mathematical and experimental framework for high-throughput quantification of feedback regulation, and apply it to RNA binding proteins (RBPs) in yeast. Our method is able to determine the existence of both direct and indirect positive and negative feedback loops, and to quantify the strength of these loops. We experimentally validate our model using two RBPs which lack native feedback loops, and by the introduction of synthetic feedback loops. We find that the the RBP Puf3 does not natively participate in any direct or indirect feedback regulation, but that replacing the native 3UTR with that of COX17 generates an auto-regulatory negative feedback loop which reduces gene expression noise. Likewise, the RBP Pub1 does not natively participate in any feedback loops, but a synthetic positive feedback loop involving Pub1 results in increased expression noise. Our results demonstrate a synthetic experimental system for quantifying the existence and strength of feedback loops using a combination of high-throughput experiments and mathematical modeling. This system will be of great use in measuring auto-regulatory feedback by RNA binding proteins, a regulatory motif that is difficult to quantify using existing high-throughput methods.

Synthetic Biology↗

bioRxiv2014

State-dependent information processing in gene regulatory networks

Cellular information processing is generally attributed to the complex networks of genes and proteins that regulate cell behavior. It is still unclear, however, what are the main features of those networks that allow a cell to encode and interpret its ever changing environment. Here we address this question by studying the computational capabilities of the transcriptional regulatory networks of five evolutionary distant organisms. We identify in all cases a cyclic recurrent structure, formed by a small core of genes, that is essential for dynamical encoding and information integration. The recent history of the cell is encoded by the transient dynamics of this recurrent reservoir of nodes, while the rest of the network forms a readout layer devoted to decode and interpret the high-dimensional dynamical state of the recurrent core. This separation of roles allows for the integration of temporal information, while facilitating the learning of new environmental conditions and preventing catastrophic interference between those new inputs and the previously stored information. This resembles the reservoir-computing paradigm recently proposed in computational neuroscience and machine learning. Our results reveal that gene regulatory networks act as echo-state networks that perform optimally in standard memory-demanding tasks, and confirms that most of their memory resides in the recurrent reservoir. We also show that the readout layer can learn to decode the information stored in the reservoir via standard evolutionary strategies. Our work thus suggests that recurrent dynamics is a key element for the processing of complex time-dependent information by cells.\n\nSummaryCells must monitor the dynamics of their environment continuously, in order to adapt to present conditions and anticipate future changes. But anticipation requires processing temporal information, which in turn requires memory. Here we propose that cells can perform such dynamical information processing via the reservoir computing paradigm. According to this concept, a structure with recurrent (cyclic) paths, known as the reservoir, stores in its dynamics a record of the cells recent history. A much simpler feedforward structure then reads and decodes that information. We show that the transcriptional gene regulatory networks of five evolutionary distant organisms are organized in this manner, allowing them to store complex time-dependent signals entering the cell in a biologically realistic manner.

Synthetic Biology↗