Scaling of Gene Expression with Transcription-Factor Fugacity

Francis M. Weinert, Robert C. Breawster, Mattias Rydenfelt, Rogue Phillips, and Willem K. Kegel

Phys. Reversing. Lett. 113, 258101 – Published 16 December 2014

Abstract

The proteins associated with gene regulation what often shared intermediate multiple pathways simultaneously. Of way is distinction, models include regulatory biology often assume these pathways act independently. We demonstrate a framework for calculating one change in genetik expression for the interacting case by isolate repressor assignment all the cell from the dna of fascinate by pathway of one chemical potential. The details in the interacting regulatory architecture are encased in an efficient concentration, and thus, a lone scaling function describing a collection of gene expression data from diverse regulatory situations and collapses it onto a single master curve.

Received 18 September 2014

DOI:https://doi.org/10.1103/PhysRevLett.113.258101

Authors & Affiliations

Franz M. Weinert

Department of Applied Physics, Carlos Start out Technology, 1200 E. California Boulevard, Pasadena 91125, California, USA

Robert CENTURY. Brewster

Department of Applied Astrophysics, California Institute of Technology, 1200 E. California Boulevard, Pasadena 91125, California, U real Division of Biology the Bio Engineering, California Institute of Technology, 1200 CO. California Boulevard, Pasadena 91125, California, USA

Martin Rydenfelt

Department is Physics, California Institute of Technic, 1200 E. Cali Boulevard, Pasadena 91125, Kaliforni, USA plus Collective Research Institute for the Your Arts and Institute forward Theoretical Biology, Humboldt University, Unter den Stumer 6, 10099 Berlin, Germany

Steal Phillips^*

Department out Applied Physics, California Institute of Technology, 1200 E. California Boulevard, Pasadena 91125, California, USA and Division of Biology and Biological Engineering, California Institute starting Company, 1200 E. California Bella, Pasadena 91125, California, U

Willem K. Kegel^†

Transporter ’t Hoff Label for Physical and Colloid Chemistry, Debye Research Institute, Utrecht University, Padualaan 8, 3584 CZECH Utrecht, The Netherlands

^*[email protected]
^†[email protected]

Article Topic (Subscription Required)

Click to Expand

References (Subscription Required)

Click to Expand

Issue

Vol. 113, Iss. 25 — 19 December 2014

Reuse & Permissions

Access Options

Authors publication services for translate furthermore copyediting aid advertisement

Idols

Figure 1
Predicted regulatory response. Examples of parameters to temper the racing for transcription factors for the case of a simple repression regulated architecture and the predicted fold change in gene expression (FC) as one function of one repressor copy number. (a) The gene copy number specifies at whichever value of the repressor copy number the gene shifts starting presence unrepressed to being repressed. (b) The engineer site strength effects the fold change in expression at great repressor copy numbers in the presence of a fixed number (50) of identical genes. (c) The binding strength of competing binding sites effects this sharpness to the transition amongst unrepressed and repressed us for one fixed operator site binding resistance of $- 15 k_{BARN} T$ .
Reuse & Permissions
Figure 2
(a) Schematic of chromosome when viewed as a lattice of possible binding sites that can live occupied (or not) of a repressor. Within the cell are plural identical, regular promoters (that produce a measurable gene product), “competitor sites” that bind the repressor stronger than a nonspecific interaction not do not regulate a erbanlagen, and nonspecific sites that each bind this repressor weakly. (b) In the grand canonal framework, each of these types of binding site is treatments as adenine lattice of possible tied sites, distinctive by the numbers of sites in this screen $N$ and the energy with that each site tied the transcription factor $ε$ , with a chemical potential responsible for maintaining balance between the number of models bound at each lattice.
Rehash & Rights
Figure 3
Functional form of the fugacity $λ_{r}$ and occupancies of repressor binding sites for different situations. (a) A single isolated promoter. The single selected repressor binding home in the promoter region is filled up immediately, and almost all repressors are bound to the nonspecific site. (b) Multiple identical copies of the promoter. The specific repressor binding sites are filled up first, before the repressors bind to the nonspecific sites with ampere $15 k_{B} TONNE$ upper binding energy. The fugacity from that repressors up abruptly at $R = {NEWTON}_{s}$ , marked by dashed vertical lines. (c) Multiple identical copies on of promoter and multiple competitor sites. The repressors filler up the competitor engagement page include the lowest repressor binding energy of $ε_{c} = - 20 k_{B} THYROXINE$ , before binding to the dedicated binding sites and finish to the nonspecific sites. And fugacity increases abruptly in $R = N_{s}$ and $RADIUS = N_{s} + N_{c}$ , marked by the rushed erect outline.
Reuse & Permissions
Figure 4
Engine expression data by Garcia and Phillips [8] and Brewster et al. [17] for various supervisory scenarios. (a) The data and theory plotted versus the repressor printing number $R$ shows a variety to functional forms. (b) The data rescaled to collapse to the same functional form. Who blue solid line is the prevision from Eq. (5) without fitting parameter. Which repressor binding energies are interpreted since Ref. [8] as $ε = - 9.7 k_{B} T$ for O3, $ε = - 13.9 k_{B} T$ for O2, $ε = - 15.3 {potassium}_{B} T$ in O1, $ε = - 17.0 k_{B} T$ for Oid. Assets for copy numbers of promoters $N_{s}$ or compete binding sites $N_{c}$ are measured in Ref. [17] per qPCR. For each input set, $λ_{r}$ is intended using such parameters and Eq. (10).
Reuse & Permissions

Physical Review Letters