Synthetic MammalianTransgene

Negative Autoregulation

Harpreet Chawla

April 2, 2015

Vinay Shimoga, Jacob White, Yi Li, Eduardo Sontag & Leonidas Bleris

Outline

Introduction Transgene Transcriptional Network Auto Regulation Synthetic biology Network Motifs Cellular Noise & Its Types

Two Reporter Experiment

Circuit Integration

Results

Analysis of Noise

References

Connections

Transcriptional n/w

Auto regulation

TF’sGenes

Network Motifs Synthetic Circuits

Cellular Noise

Gene/Transgene

A Gene is "a locatable region of genomic sequence”, corresponding to a unit of inheritance.

A Transgene is gene/genetic material which has been transferred from one organism to another (naturally or by genetic engineering)

Transcriptional Network

The rate of production of each protein

Consist of interactions between Transcription factors (TFs) & Genes.

TF’s bind to the promoter region of the gene & affects the transcription rate. TFs can act as activators or as repressors.

TFs are themselves encoded by genes, which are regulated by yet another TFs, and so on. This set of interactions forms a Transcription Network.

Gene Transcription Regulation

TF as Activator/Repressor

Autoregulation

Self Loop

Autoregulation - Genes regulated by its own product.

Autoregulation - Process that works to adjust system's response to stimuli.

Can be positive or negative.

NAR/PAR

Synthetic Biology : Engineering life to examine it

Synthetic biological circuits are designed to perform logical functions mimicking those observed in electronic circuits.

Like electronic circuits, they can take a number of different inputs and deliver a particular kind of output.

Are used to control and manipulate living cells. These circuits serve as a method to modify cellular functions, create cellular responses to environmental conditions, or influence cellular development. 

E Coli transcriptional Network

E Coli transcriptional Network

Network Motifs

Isomorphic Subgraphs

Patterns that occur in the real network significantly more often than in randomized networks.

Recurrent and Statistically significant sub graphs or patterns

NAR is a common network motif in transcriptional networks.

What this paper is about

Synthetic Circuits stably integrated into Human Kidney cells (Mammalian) to study effect of Noise on genes & cells (Transgene) using NAR (Negative Autoregulation).

Network Motif – Negative feedback loop.

Study the effects of negative feedback regulation on cells and genes (extrinsic/intrinsic noise).

Study effects of negative feedback on total noise.

Cells continuously change their internal state to adapt to environmental changes, look for nutrients or coordinate with other cells.

Do always cells in identical conditions produce the same response (output) to an external stimulus (input)?

• No two cells are identical, even in a clonal population under the same conditions (pH, T, nutrients etc.)

Cellular Noise

Noise: cell-to-cell variability due to different in protein levels, structure or size in a homogenous population

• Coefficient of variance (CV) =

Gene expression in cells is variable, heterogeneous, noisy or stochastic process

How does the concentration of Protein P changes with time ?

Cellular Noise

Two Types :(a) the Intrinsic noise - originating from fluctuations internal to the module (b) the Extrinsic noise - originating from external fluctuations that impinge on the module.

Intrinsic noise refers to variation in identically-regulated quantities within a single cell: for example, the intra-cell variation in expression levels of two identically-controlled genes

Extrinsic noise refers to variation in identically-regulated quantities between different cells: for example, the cell-to-cell variation in expression of a given gene.

Types Of Noise

• The gene encoding a fluorescent protein (GFP) is placed under the control of a promoter, allowing gene expression to be monitored.

Experimental Measurement

Previous Work : Two Reporter Experiment

Two color experiments

Two identical copies of a gene under precisely the same conditions.

Two equivalent repressible fluorescent reporter genes inserted in the E. coli chromosome controlled by the same promoter, and on opposite sites and equidistant from the replication origin.

Previous Work : Two Reporter Experiment

• Promoters unrepressed due toaddition of IPTG• high transcription (highnumber of mRNA molecules)• low intrinsic noise

• Promoters repressed by wildtype repressor (lacI) gene• low transcription(low numberof mRNA molecules)• high intrinsic noise

Limitation - Two identically regulated reporters

Integration of the circuit

Results

Analysis of Noise

The total noise observed arises through the combination of global (extrinsic) fluctuations together with the fluctuations in that protein’s local regulation (intrinsic).

The intrinsic noise and extrinsic noise squared, sums to the CV-squared ofthe fluorescent reporter.

• Y the constitutive reporter (dsRed)• X the regulated reporter - controlled by an inducer IPTG (zsGreen1)• α is the coefficient that is 1 for two constitutive promoters with identical reporter

statistics but varies depending on the regulation of X.

Result - Negative feedback results in significant total noise reduction by reducing extrinsic noise while marginally increasing intrinsic noise.

Analysis of Noise

References

Abdullah Hamadeh & Domitilla Del Vecchio, Mitigation of resource competition in synthetic genetic circuits through feedback regulation.

Alon U, Network motifs: theory and experimental approaches.

Rosenfeld N, Elowitz MB and Alon U, Negative auto regulation speeds the response times of transcription networks.

Timothy K Lu, Ahmad S Khalil & James J Collins, Next generation synthetic gene circuits.

Katherine A. Riccione, Robert P. Smith, Anna J. Lee and Lingchong You, A Synthetic Biology approach to understanding cellular information.

Abhyudai Singh1 and Joao Hespanha1, Noise suppression in auto regulatory gene networks.

Hui Zhang, Yueling Chen and Yong Chen, Noise Propagation in gene regulation networks involving interlinked positive and negative feedback loops.

Questions

THANK YOU