Genregulation Physics of transcription control and expression analysis

Genregulation Physics of transcription control and expression analysis Systems biophysics 2010/05/11 Literature - Alberts/Lehninger - Kim Sneppen & G. Zocchi: Physics in Molecular Biology - E. Klipp et al. : Systems Biology in Practice

From genetic approach to sytemic approach DNA mutations / evolution genregulation m. RNA regulation protein functions spatiotemporal structure formation Morphogenesis signal transduction => Topics of systems biophysics

Biological Pattern formation and Morphogenesis Reaction-Diffusion-Model of Morphogenesis Biochemical Network Enzymatic Reactions Michaelis-Menton-Kinetics Inhibation, Regulation 11. 05. 2010

E. coli as model system Genregulation allows adaption to changing environmental conditions, and regulation of metabolism E. coli has a single DNA molecule which is 4. 6 106 basepairs long. It encodes 4226 proteins and a couple of RNA molecules. The information content of the genome is is bigger than the structural information of the encoded Proteins -> regulatory mechanisms are encoded

Content of this lecture: Basics: Monod Model, Lac Operon Statistical Physics of DNA-binding Proteins Modelling of genregulatory Networks (ODE & Boolian Networks) Dynamics of Protein-DNA binding DNA looping Analysis of gene expression data Synthetic Networks

Operon-Modell Francois Jacob und Jaques Monod, 1961 operon Operon: Genetic subunit, that consists of regulated genes with similar functionality. It includes - Promotor: Binding site for RNA polymerase - Operator: controls access of the RNA-Polymerase structural gene - Structural genes: Polypeptide encoding genes

The Trp Operator as a switch: • Within the promotor lies a short DNA region as binding site for a repressor. A bound repressor prevents the Polymerase from binding.

The OUTSIDE of proteins can be recognized by proteins Small channel Large channel Distinct basepairs can be recognized by their margins DNA binding motivs

Binding of Tryptophane to the Tryptophane-Repressorproteine changes the conformation of the repressor, Repressor can bind to the repressor binding site

Identification of promotor sequences

Transcription-Activation proteins switch on genes

Gen-Regulation with Feedback: lac-Operon IPTG, TMG Lac. I

Non-metabolizable inducer are used to induce gene expression IPTG (Isopropyl β-D-1 -thiogalactopyranoside)This compound is used as a molecular mimic of allolactose, a lactose metabolite that triggers transcription of the lac operon. Unlike allolactose, the sulfur (S) atom creates a chemical bond which is non-hydrolyzable by the cell, preventing the cell from "eating up" or degrading the inductant. IPTG induces activity of betagalactosidase, an enzyme that promotes lactose utilization, by binding and inhibiting the lac repressor. In cloning experiments, the lac. Z gene is replaced with the gene of interest and IPTG is then used to induce gene expression. A cis-regulatory element or cis-element is a region of DNA or RNA that regulates the expression of genes located on that same strand. This term is constructed from the Latin word cis, which means "on the same side as". These cis-regulatory elements are often binding sites of one or more trans-acting factors. Campbell, N. A. , Biology

Variation of Protein-Concentration with IPTG Northern Blot: measurement of the messenger RNA (m. RNA) concentration Long, C et al, J. Bacteriol. 2001 External and internal Inductor-concentration is equal in equilibrium The m. RNA concentration increases linear with the concentration of inductor, saturation over 60% The operon enables a variation of Protein concentration. What is missing to make a switch?

Transkription und Translation in E. coli Typical times and rates 1 Molecule / cell = 1 n. M Complete mass 2. 5 106 Da TRANSKRIPTION rate 1/s - 1/18 s Transkriptionsrate: 30 bps-90 bps TRANSLATION 10. 000 -15. 000 Ribosomes Translation rate 6 -22 codons/s (40 Proteine/m. RNA)

The arabinose system 1 p. BAD 24 2 ~55 copies/cell Reporter Break down Regulator Uptake [1] R. Schleif. Trends in Genetics, 16(12): 559– 565, 2000 [2] L. M. Guzman, D. Belin, M. J. Carson, and J. Beckwith. J. Bacteriol. , 177(14): 4121– 4130, 1995 [3] D. A. Siegele and J. C. Hu. Proc. Natl. Acad. Sci. USA, 94(15): 8168– 8172, 1997

Time-lapse Fluorescence Microscopy and Quantitative Image Processing automated data aquisition DIC tn tn Fluorescence t 0 define ROIs measure total intensity DIC t 0 t 1 N background correction calibration and conversion into molecular units

Judith. Megerle@physik. lmu. de

Single cell expression kinetics Saturating induction Fluorescence measurement • Cell outlines are determined using bright field images • The signal is integrated within the outline in each fluorescence image 5 min 15 min 25 min 35 min 45 min Subsaturating induction 30 min 40 min 50 min 60 min 70 min Image series correspond to blue curves

Gene expression model Reporter module Uptake module Deterministic rate model with time delay d Induction: t=0 min

Curve Fitting Saturating induction Fit expression function Fixed Parameters Literature Measured Fit Parameters Time delay Protein synthesis rate Subsaturating induction

Ohter example: Quorum Sensing

Squid with floodlamp Phänomena: Squid (Euprymna scolopes) emmits light due the night Squid isn´t recognized as prey in the moonlight Explanation: Light organ of the squid collects luminescent bacteria (Vibrio fischerei) Question: Why does V. fischerei emmit light within the lightorgan of the squid, but not in open sea?

Quorum sensing Bacteria increase exponential OD: optical density K. Nelson, Cell-Cell Signalling in Bacteria Bakterien detect their own cell density Density regulates the expression of luminescent genes

Molekular picture of QS • Bakteria export oligopeptides (Pheromones) • Oligopeptides accumulate with increasing cell density • Oligopeptide diffuse into cell membrane and regulates the expression of luminescent genes

Searching the binding site

Searching the binding site: timescales Stokes Einstein equation (z. B. DGFP=3 -7µm 2/s) Probability distribution 1µm Typical timescale for a proteine to find an arbitrary point in an E. coli: t. D 0. 1 s

Diffusion to a target site (binding disc)

Residence times for transcription factors for specific bindings (operon) with 1 M-1=1. 6 nm 3 and Gspez=-12. 6 kcal/mol, =1 follows (from on=20 s/N follows, that 1 molecule in 1µm 3 occupies half an Operator) for unspecific binding sites with Guspez=-10 -4 kcal/mol, follows

Search of the binding sites on a DNA strand Unspecific binding events of TFs is a problem, since the time to find a binding site is increased. For a infinite staytime, a 1 D- random walk over the strand would last: (L=1. 5 mm und D 1≈D)

Accelerated search: jumps between strands decrease time to find a binding site. Mit L=1. 5 mm, l=150 nm follows

Boolian Networks, or what cells and computers have in common.

(Nature, Dec 99)

Combinatoric gene regulation: Genetic networks Genregulatoric proteine translation transcription

A transcription-activator and a transcription-repressor regulate the lac-Operon

Thermodynamicc model of a combinatoric transcription logics Gene regulation follows the mechanics of „Boltzmann-machines“ P : binding probability Gerland et al. PNAS, 2005

Statistical physics of protein - DNA binding Binding-isothermes:

Cooperativity due to dimer binding Cooperative binding

The statistical weight of the „on“ state The free-energy difference is normalized to 1 mol/l. The real change in free energy of the binding event depends on the concentration of TF in solution [Cl] :

A model for lac networks Glukose conc. constant GFP: Reportermolekül, Abbildung durch Fluoreszenz-Mikroskopie => je höher das Fluoreszenz-Signal desto mehr Lac. Z, Y wird exprimiert

Experimental proof for a switch Bistable area (grey) Start: not induced Arrow marks the start state: After induction exist 2 populations: on-off state of bacteria depend on the on-off state in the beginning! green: induced bacteria white, not induced population switch with hysteresis Ozbudak et al, Nature 2004

modelling of genregulatory networks: example

Modelling in m. RNA level

Timetrace of m. RNA concentrations Steady state Problem: kinetic binding constants are usually not known and hard to measure

Simplification of genregulatory networks Genregulatory protein translation transcription

Abstraction of genetic networks + Gen Y Gen X Gen Z

Boolean networks (Kauffman 1989)

Boolean networkmodel • N Genes (nodes) • with 2 N different states • with possible rules • K is the number of possible inputs per node

Boolean rules for N=2 und K=2

Back to the example: We learn: if a=0, then follows 0101 stationary if a=1, then follows oscilatory behaviour 1000 ->1001 ->1111 ->1010 ->1000