What is biological noise and why is it interesting?

Noise analysis graphs and illustration of noise in single cell.
Noise analysis of genetic circuitry in a single cell requires single-cell imaging, image processing, and signal processing. In Austin et al., 2006, E.coli consisting of reporter plasmids (panels a and b) were imaged over long durations (lower-right number, in hours), and image and signal processing yields gene expression noise from gene circuits expressing a green fluorescent protein (GFP, panel c). Autocorrelation analysis yields both individual single-cell correlation times, as well as population-level correlations (bold trend, panel d).

Fluctuations in gene expression (or "noise") are due to the random timing and discrete nature of biochemical interactions that occur throughout gene expression processes (i.e., transcription, translation, etc.). Gene expression noise can be used both as a probe to understand underlying gene circuit dynamics and regulation and to quantify biophysical parameters. In certain cases, noise is naturally exploited for decision making and distributing probabilities between genetic and cellular states in a cell population.

Can cellular decision making be controlled using noise modulation? Enhanced control of decision making at the single-cell level has importance for systems ranging from viruses and cancer to cellular reprogramming.

Fundamentals of noise and regulation of genetic circuits

To make a functional use of noise in gene expression, we must comprehensively understand the fundamentals of stochasticity and regulation of gene circuits and networks. Among several projects under this topic, the relationship between genetic architectures or structure and their function is of great interest. We have previously developed theoretical and experimental tools to understand auto-regulation and episodic transcription in E. coli (Austin, et al., 2006; Cox, et al., 2008) and infected human T-cells (Weinberger, Dar, Simpson, 2008; Dar, Razooky, et al., 2012). We also have investigated the differences in genetic architectures that couple noise and transcriptional response to perturbations in budding yeast (Dar et al., 2010). Gene circuits in E. coli, yeast, and mammalian cells are studied using experimental and computational approaches. Our lab integrates both system-wide datasets and single-cell time-lapse fluorescence microscopy.

Graph showing noise scatter of viral gene expression.
Similar to particle scatter in characterizing a lattice in materials research, in Dar, et al., 2014, the HIV-1 LTR promoter was treated with ~2000 bioactive compound perturbations for changes in mean expression and an orthogonal noise axis. High-throughput flow cytometry measurements result in the noise scatter of viral gene expression, which was used to identify novel hits for synergistic drug cocktails.

Fundamentals and applications of noise drug screening

Recently we introduced noise screening of HIV-1 gene circuitry and demonstrated that combinations of noise-modulating drug treatments enhance viral reactivation through diverse mechanisms (Dar, et al., 2014). To extend noise drug screening beyond this system, we are interested in biasing diverse cellular decisions and advancing the fundamentals and tools for noise drug screening. A new field of “noise pharmacology” will require a currently non-existent foundation in three areas:

Graphic showing viral-host co-expression and migration.
Similarity in viral and host promoters couples viral reactivation with host cell migration Promoter similarity of human immunodeficiency virus (HIV) and a human surface receptor allows shared activators to co-regulate viral-host gene expression (blue and red in the cell nucleus). Viral proteins bind cell surface receptors enabling viral control of host cell migration (right side). Meanwhile the same viral proteins form viral offspring which are shed from the host cell and increase infectious risk to the moving cell’s environment. Drug treatments are shown to modulate viral-host co-expression and cell migration suggesting that HIV therapy needs to account for this viral-host relationship.

Computational and Systems Bioengineering: A gateway to reverse-engineer natural systems

In addition to noise, our lab is interested in integrating genome-wide and perturbation datasets with experimentation towards understanding Nature’s blueprints. This will advance applications and the reverse-engineering of biology by top-down and bottom-up approaches. Currently we are interested in the structure of genome-wide noise sources in E. coli and other organisms (Dar et al., 2015) and studying viral-host relationships to improve therapeutic strategies (Bohn-Wippert et al., 2017).





We would like to acknowledge the following organizations for their funding and support: