Research

Our research is at the nexus of biology, engineering, and medicine. We combine mathematical modeling and experiments to analyze dynamics of cell signaling processes, including cell cycle regulation, bacterial response to antibiotics, and cell-cell communication. These studies will allow us to gain insights into the Òesign lawsÓof natural biological systems. Based on insights learnt from natural systems, we construct synthetic gene circuits with well-defined functions. In addition to generating useful systems, implementation of such synthetic gene circuits will also enable us to reduce biological complexity and to probe biological design strategies in a well-defined framework. Our research is primarily in two related directions.  

  • Bacterial Population Dynamics These projects focus on engineering synthetic gene circuits that can precisely program bacterial growth, death, and aggregation in complex environments. Projects in this direction have implications for developing new technologies for gene and drug delivery, designing effective treatment strategies against bacterial infections, and for green fabrication of new materials.
  • Mammalian Cell Cycle Regulation These projects aim to define a quantitative framework to analyze and perturb mammalian cell cycle regulation. Their outcome will provide insight into development of novel strategies to reprogram and interfere with cell cycle regulation for cancer therapy.

Despite their apparent diversity, all our projects are united by a common theme as illustrated in the figure. In each case, we examine the dynamics of a specific network of interacting genes, chemicals, or cell populations. We ask what dynamics can result from the network, how the dynamics can be modulated by perturbing different network nodes, how the dynamics account for the biological function of the network, or how the dynamics can serve as foundation for practical applications. The modeling or experimental approaches we take are determined by the specific questions we ask.

Ongoing Projects

  • Programming bacterial dynamics using gene circuits

    A major challenge in synthetic biology is to identify general, scalable strategies that enable construction of increasingly complex gene circuits with reliable performance, as well as to develop novel technological platforms for quantitative circuit characterization. To address this challenge, we have used quorum sensing to program bacterial dynamics. Quorum sensing is a mechanism by which many bacteria synthesize, sense, and respond to small signaling molecules to achieve cell-to-cell communication. Using this strategy, we have generated a number of synthetic gene circuits that program autonomous control of one or multiple bacterial populations, including a population controller (You et al, Nature 2004; Balagadde, You et al, Science 2005) and a predator-prey ecosystem (Balagadde et al, Molecular Systems Biology, 2008).

    In addition to demonstrating the programming of complex population dynamics, our engineered ecosystem serves as a well-defined model for exploring the questions arising from study of natural ecological systems. For example, we have used the synthetic ecosystem to explore fundamental ecological questions, including maintenance and evolution of biodiversity (Song et al, Nature Chemical Biology, 2009).

  • Exploring evolutionary dynamics of bacterial cooperation using synthetic gene circuits

    Cooperation is critical for bacteria to deal with stress, which may arise from competition, host infection, starvation, and antibiotic treatment. Disruption of cooperation in pathogens has been proposed as an alternative to antibiotic treatment that directly inhibits pathogen growth, as the latter tends to select for resistant mutants. Early studies have shown promise but highlighted a critical need to better understand the evolutionary dynamics of bacterial cooperation and its intervention.

    A major conceptual difficulty associated with cooperation is its susceptibility to exploitation by defectors, which do not pay the cost of cooperation but benefit from the public good generated by cooperators. For example, how is cooperation maintained against invasion by defectors? How is the maintenance of cooperation affected by environmental parameters, such as the strength and duration of stress? Indeed, while many traits can be intuitively interpreted as cooperation, their benefit and maintenance are poorly understood and sometimes controversial. Moreover, susceptibility of cooperation to exploitation suggests the potential to develop antibacterial strategies that target cooperation. To examine these questions, we construct synthetic gene circuits in bacterium Escherichia coli to implement these cooperative traits. In a recent example, we engineered synthetic gene circuits to realize programmed altruistic death in E. coli and demonstrated conditions where PAD can be beneficial for a clonal population (Tanouchi et al, 2012).

  • Non-genetic bacterial tolerance to antibiotic treatment

    Antibiotics have been hailed as the single most significant therapeutic discovery in medicine in the 20th century. However, decades of overuse and misuse is causing a major crisis: bacteria have developed resistance for every existing antibiotic; and they can do so at an alarming rate, considering the timescale at which new antibiotics can be developed. In addition to developing new antibiotics, it is now recognized that there is a critical need to design better treatment protocols, using currently existing antibiotics. Achieving this goal will require a better understanding on how bacteria can tolerate antibiotic treatment at the level of individuals or populations.

    A common phenomenon of bacterial tolerance is the inoculum effect: for a given concentration of an antibiotic, its ability to inhibit bacterial growth decreases with the size of the bacterial inoculum. This phenomenon has been found to occur for all known bacterial pathogens when treated by many antibiotics. Occurrence of inoculum effect is often undesirable in the clinical setting: it can increase mortality rates of infected host by applying insufficient dose of antibiotics and cause overestimation of bacterial resistance. But its underlying mechanism remains undefined. In our recent work (Tan et al, Molecular Systems Biology, 2012), we show that for an antibiotic targeting the ribosomes, the inoculum effect can be explained by the bistable inhibition of bacterial growth. Our results demonstrate that a critical requirement for this bistability is the fast degradation of the core replication machinery induced by the antibiotic. We also find that the inoculum effect can lead to Òand-passÓbacterial response to periodic antibiotic treatment. When this occurs, treatment efficacy would drastically diminish at intermediate frequencies of antibiotic treatment. This property has implications for designing effective treatment against bacterial pathogens, which is part of our ongoing research.

    This project was inspired by the analysis of a simple synthetic bistable switch we recently published (Tan et al, Nature Chemical Biology, 2009). Interestingly, the fundamental mathematics underlying the inoculum effect is identical to that describing the synthetic gene circuit. This aspect illustrates the underlying connection between our apparently diverse projects.

  • Analysis of mammalian cell cycle regulation

    These projects aim to establish an integrated framework for an in-depth view of signaling pathways central to mammalian cell cycle control, focusing on the Myc/Rb/E2F network. The network and its uptream and downstream signaling pathways provide a well-defined context for exploring design principles of complex biological networks. Also, such analyses have direct medical implications given the critical role of Rb-E2F circuit in controlling cellular proliferation and its frequent deregulations in human cancers.

    Bistable Rb-E2F switch: Integrating modeling with single-cell experiments, we have shown that the Rb-E2F circuit functions as a bistable switch that separates the quiescence and proliferation states of a mammalian cell (Yao et al, Nature Cell Biology 2008). Once turned ON, as represented by E2F activation, this switch can trigger cell cycle entry in an Òll-or-noneÓmanner. The switch will then stay ON to drive cells through the proliferation cycle without sustained growth stimulation. We also demonstrate that E2F activation underlies the entry of the cell cycle in individual cells. The bistability of the Rb-E2F circuit provides a direct mechanistic explanation for the concept of restriction point, proposed four decades ago.

    Stochastic E2F activation during cell cycle entry: For each cell, the decision to enter the cell cycle is critically dependent on the interplay between environmental cues and the internal state of the cell and is influenced by random fluctuations in cellular processes. Experimental evidence indicates that cell cycle entry is highly variable from cell to cell, even within a clonal population. To account for such variability, a number of phenomenological models have been previously proposed. These models primarily fall into two types depending on their fundamental assumptions on the origin of the variability: Òransition probabilityÓmodels and Òrowth-controlledÓmodels. While both kinds of models provide a good fit to experimental data, their lack of mechanistic details limits their predictive power and has led to unresolved debate between their practitioners. We developed a mechanistically based stochastic model of the temporal dynamics of the network. Using this model, we show that Òransition probabilityÓand Òrowth-controlledÓmodels can be reconciled by incorporation of a small number of basic cellular parameters related to protein synthesis and turnover, protein modification, stochasticity, and the like. We suggest that incorporation of basic cellular parameters in this manner into phenomenological models may constitute a broadly applicable approach to defining concise, quantitative phenotypes of cell physiology (Lee et al, PLoS Biology, 2010).

    Biphasic E2F response revealed by viral-mediated noisy gene expression: It is now well appreciated that variability (noise) in gene expression is intrinsic to many cellular processes. Recent investigations into noise have primarily focused on two lines of investigation. One is the origin, propagation, and control of cellular noise. The other is its impact on biological functions, such as cell-fate decisions and adaptation to changing environments. In mammalian cells, drastic variability is inherent in gene expression mediated by viral vectors, which are employed ubiquitously in cell cultures and live animals. This lack of uniformity limits the precision of gene delivery in basic research and clinical applications. However, we have demonstrated that variability can be exploited in order to probe signaling dynamics in single mammalian cells in a high-throughput manner.

    Specifically, we utilized the cell-cell variabilities in adenoviral-mediated gene expression to map how different levels of the proto-oncoprotein Myc impact gene expression in the context of the Myc/Rb/E2F network. This approach enabled us to observe a hitherto undiscovered mode of network behavior: E2F expression first increased then decreased with increasing MYC. This unique behavior reconciles the several conflicting reports on how E2F responds to Myc overexpression and suggests a safeguard mechanism by which cells limit uncontrolled proliferation (Wong et al, Molecular Cell, 2011).

    Our current efforts focus on developing experimental tools that can allow us to (1) precisely perturb different nodes of the network, (2) examine the consequences on both the network dynamics and the corresponding phenotypic responses (e.g., proliferation and apoptosis), (3) using variable gene expression as a quantitative phenotype to probe cellular functions.

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