ABSTRACT
Stem cells are unique in their ability to differentiate into diverse phenotypes capable of displaying radically different, yet stable, gene expression profiles. Understanding this multistable behavior is key to rationally influencing stem cell differentiation for both research and therapeutic purposes. To this end, mathematical paradigms have been adopted to simulate and explain the dynamics of complex gene networks. In this chapter, we introduce strategies for building deterministic and stochastic mathematical models of gene expression and demonstrate how analysis of these models can benefit our understanding of complex observed behaviors. Developing a mathematical understanding of biological processes is of utmost importance in understanding and controlling stem cell behavior.
Subject(s)
Cell Lineage , Gene Regulatory Networks , Models, Theoretical , Stem Cells/cytology , Cell Differentiation , Humans , Phenotype , Stochastic ProcessesABSTRACT
Robust and precise ratio control of heterogeneous phenotypes within an isogenic population is an essential task, especially in the development and differentiation of a large number of cells such as bacteria, sensory receptors, and blood cells. However, the mechanisms of such ratio control are poorly understood. Here, we employ experimental and mathematical techniques to understand the combined effects of signal induction and gene expression stochasticity on phenotypic multimodality. We identify two strategies to control phenotypic ratios from an initially homogeneous population, suitable roughly to high-noise and low-noise intracellular environments, and we show that both can be used to generate precise fractional differentiation. In noisy gene expression contexts, such as those found in bacteria, induction within the circuit's bistable region is enough to cause noise-induced bimodality within a feasible time frame. However, in less noisy contexts, such as tightly controlled eukaryotic systems, spontaneous state transitions are rare and hence bimodality needs to be induced with a controlled pulse of induction that falls outside the bistable region. Finally, we show that noise levels, system response time, and ratio tuning accuracy impose trade-offs and limitations on both ratio control strategies, which guide the selection of strategy alternatives.
Subject(s)
Cell Differentiation/physiology , Intracellular Space/physiology , Models, Biological , Synthetic Biology , Algorithms , Escherichia coli/physiology , Gene Expression/physiology , Stochastic ProcessesABSTRACT
The RNA-guided endonuclease Cas9 can be converted into a programmable transcriptional repressor, but inefficiencies in target-gene silencing have limited its utility. Here we describe an improved Cas9 repressor based on the C-terminal fusion of a rationally designed bipartite repressor domain, KRAB-MeCP2, to nuclease-dead Cas9. We demonstrate the system's superiority in silencing coding and noncoding genes, simultaneously repressing a series of target genes, improving the results of single and dual guide RNA library screens, and enabling new architectures of synthetic genetic circuits.
Subject(s)
CRISPR-Cas Systems , Gene Expression Regulation , Gene Silencing , CRISPR-Associated Protein 9/genetics , CRISPR-Associated Protein 9/metabolism , Genes, Synthetic , HEK293 Cells , Humans , Methyl-CpG-Binding Protein 2/genetics , Methyl-CpG-Binding Protein 2/metabolism , RNA, Guide, Kinetoplastida/genetics , Recombinant Fusion Proteins/genetics , Recombinant Fusion Proteins/metabolism , Repressor Proteins/genetics , Repressor Proteins/metabolismABSTRACT
Efficient clustered regularly interspaced short palindromic repeat (CRISPR) guide RNA (gRNA) expression from RNA Polymerase II (Pol II) promoters will aid in construction of complex CRISPR-based synthetic gene networks. Yet, we require tools to properly visualize gRNA directly to quantitatively study the corresponding network behavior. To address this need, we employed a fluorescent gRNA (fgRNA) to visualize synthetic CRISPR network dynamics without affecting gRNA functionality. We show that studying gRNA dynamics directly enables circuit modification and improvement of network function in Pol II-driven CRISPR circuits. This approach generates information necessary for optimizing the overall function of these networks and provides insight into the hurdles remaining in Pol II-regulated gRNA expression.
Subject(s)
RNA, Guide, Kinetoplastida/chemistry , RNA, Guide, Kinetoplastida/metabolism , Clustered Regularly Interspaced Short Palindromic Repeats/genetics , Computational Biology/methods , RNA Polymerase II/genetics , RNA Polymerase II/metabolism , Synthetic Biology/methodsABSTRACT
Widespread quorum-sensing (QS) enables bacteria to communicate and plays a critical role in controlling bacterial virulence. However, effects of promiscuous QS crosstalk and its implications for gene regulation and cell decision-making remain largely unknown. Here we systematically studied the crosstalk between LuxR/I and LasR/I systems and found that QS crosstalk can be dissected into signal crosstalk and promoter crosstalk. Further investigations using synthetic positive feedback circuits revealed that signal crosstalk significantly decreases a circuit's bistable potential while maintaining unimodality. Promoter crosstalk, however, reproducibly generates complex trimodal responses resulting from noise-induced state transitions and host-circuit interactions. A mathematical model that integrates the circuit's nonlinearity, stochasticity, and host-circuit interactions was developed, and its predictions of conditions for trimodality were verified experimentally. Combining synthetic biology and mathematical modeling, this work sheds light on the complex behaviors emerging from QS crosstalk, which could be exploited for therapeutics and biotechnology.
