Biology subjects

El-Samad, H.

Publications and source records attributed to El-Samad, H..

4 recordsLinked to original sources

bioRxiv2018

Controlling CRISPR-Cas9 with ligand-activated and ligand-deactivated sgRNAs

The CRISPR-Cas9 system provides the ability to edit, repress, activate, or mark any gene (or DNA element) by pairing of a programmable single guide RNA (sgRNA) with a complementary sequence on the DNA target. Here we present a new method for small-molecule control of CRISPR-Cas9 function through insertion of RNA aptamers into the sgRNA. We show that CRISPR-Cas9-based gene repression (CRISPRi) can be either activated or deactivated in a dose-dependent fashion over a >10-fold dynamic range in response to two different small-molecule ligands. Since our system acts directly on each target-specific sgRNA, it enables new applications that require differential and opposing temporal control of multiple genes.

synthetic biology↗

bioRxiv2018

Design and analysis of a Proportional-Integral-Derivative controller with biological molecules

The ability of cells to regulate their function through feedback control is a fundamental underpinning of life. The capability to engineer de novo feedback control with biological molecules is ushering in an era of robust functionality for many applications in biotechnology and medicine. To fulfill their potential, feedback control strategies implemented with biological molecules need to be generalizable, modular and operationally predictable. Proportional-Integral-Derivative (PID) control fulfills this role for technological systems and is a commonly used strategy in engineering. Integral feedback control allows a system to return to an invariant steady-state value after step disturbances, hence enabling its robust operation. Proportional and derivative feedback control used with integral control help sculpt the dynamics of the return to steady-state following perturbation. Recently, a biomolecular implementation of integral control was proposed based on an antithetic motif in which two molecules interact stoichiometrically to annihilate each others function. In this work, we report how proportional and derivative implementations can be layered on top of this integral architecture to achieve a biochemical PID control design. We illustrate through computational and analytical treatments that the addition of proportional and derivative control improves performance, and discuss practical biomolecular implementations of these control strategies.

synthetic biology↗

bioRxiv2018

Complexity-Aware Simple Modeling

Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach -- complexity-aware simple modeling -- that can bridge the gap between the small- and large-scale approaches.\n\nHighlightsO_LISimple or small-scale models allow deduction of fundamental principles of biological systems\nC_LIO_LIDetailed or large-scale models can be quantitatively accurate but difficult to analyze\nC_LIO_LIComplexity-aware simple models can extract principles that are robust to the presence of unknown complex interactions\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC=\"FIGDIR/small/248419_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@1cc6c8borg.highwire.dtl.DTLVardef@1ea1eb3org.highwire.dtl.DTLVardef@185a0b4org.highwire.dtl.DTLVardef@1f9c6b9_HPS_FORMAT_FIGEXP M_FIG Graphical abstract\n\nC_FIG

systems biology↗

bioRxiv2018

Real time genetic compensation operationally defines the dynamic demands of feedback control

Biological signaling networks use feedback control to dynamically adjust their operation in real time. Traditional static genetic methods such as gene knockouts or rescue experiments often can identify the existence of feedback interactions, yet are unable to determine what feedback dynamics are required. Here, we implement a new strategy, closed loop optogenetic compensation (CLOC), to address this problem. Using a custom-built hardware and software infrastructure, CLOC monitors in real time the output of a pathway deleted for a feedback regulator. A minimal model uses these measurements to calculate and deliver--on the fly--an optogenetically-enabled transcriptional input designed to compensate for the effects of the feedback deletion. Application of CLOC to the yeast pheromone response pathway revealed surprisingly distinct dynamic requirements for three well-studied feedback regulators. CLOC, a marriage of control theory and traditional genetics, presents a broadly applicable methodology for defining the dynamic function of biological feedback regulators.

systems biology↗