Synchronized cycles of bacterial lysis for in vivo delivery

The pervasive view of bacteria as strictly pathogenic has given way to an ppreciation of the widespread prevalence of beneficial microbes within the human body. Given this milieu, it is perhaps inevitable that some bacteria would evolve to preferentially grow in environments that harbor disease and thus provide a natural platform for the development of engineered therapies. Such therapies could benefit from bacteria that are programmed to limit bacterial growth while continually producing and releasing cytotoxic agents in situ. Here, we engineer a clinically relevant bacterium to lyse synchronously at a threshold population density and to release genetically encoded cargo. Following quorum lysis, a small number of surviving bacteria reseed the growing population, thus leading to pulsatile delivery cycles. We use microfluidic devices to characterize the engineered lysis strain and we demonstrate its potential as a drug deliver platform via co-culture with human cancer cells in vitro. As a proof of principle, we track the bacterial population dynamics in ectopic syngeneic colorectal tumors in mice. The lysis strain exhibits pulsatile population dynamics in vivo, with mean bacterial luminescence that remained two orders of magnitude lower than an unmodified strain. Finally, guided by previous findings that certain bacteria can enhance the efficacy of standard therapies, we orally administer the lysis strain, alone or in combination with a clinical chemotherapeutic, to a syngeneic transplantation model of hepatic colorectal metastases. We find that the combination of both circuit-engineered bacteria and chemotherapy leads to a notable reduction of tumor activity along with a marked survival benefit over either therapy alone. Our approach establishes a methodology for leveraging the tools of synthetic biology to exploit the natural propensity for certain bacteria to colonize disease sites.National Institute of General Medical Sciences (U.S.) (GM069811)San Diego Center for Systems Biology (P50 GM085764)National Cancer Institute (U.S.). Swanson Biotechnology Center (Koch Institute Support Grant (P30-CA14051))National Institute of Environmental Health Sciences (Core Center Grant (P30- ES002109))National Institutes of Health (U.S.) (NIH Pathway to Independence Award NIH (K99 CA197649-01))Misrock Postdoctoral fellowshipNational Defense Science and Engineering Graduate (NDSEG) Fellowshi

Mathematical modeling of signaling and synthetic networks in single cells /

Recent advances in quantification methods of regulatory and signaling gene networks has lead to an increasing amount of data that has opened the door for improved understanding of cell behavior. The key to that understanding is through the use of mathematical models that can explain existing data as well as help generate new hypotheses through prediction. Refinement of these models with new experimental data creates a feedback loop, where modeling drives experiments while newly generated data constrain the model. Mathematical principles underlying various models can then give us insight into basic biological principles that describe network dynamics. In this thesis, several different applications of mathematical modeling are used to help further our understanding of signaling and synthetic gene networks. First, mathematical modeling is used to explain the underlying mechanisms in coupling of two synthetic gene oscillators to each other as well as to the host environment, which leads to the observed non-trivial biological behavior. Second, focusing on a specific signaling protein network, characterized by transcription factor nuclear factor kappa B (NF-[kappa]B), mathematical modeling is used to understand how the underlying the cell -to-cell variability leads to variability in the response of the system to gradually increasing levels of the network-activating tumor necrosis factor alpha (TNF[alpha]). Finally, information-theoretic approach is applied to three different signaling networks to help gain insight into the role that various sources of noise and various forms of network responses play in signal transductio

Systems biology. Accurate information transmission through dynamic biochemical signaling networks.

Stochasticity inherent to biochemical reactions (intrinsic noise) and variability in cellular states (extrinsic noise) degrade information transmitted through signaling networks. We analyzed the ability of temporal signal modulation--that is, dynamics--to reduce noise-induced information loss. In the extracellular signal-regulated kinase (ERK), calcium (Ca(2+)), and nuclear factor kappa-B (NF-κB) pathways, response dynamics resulted in significantly greater information transmission capacities compared to nondynamic responses. Theoretical analysis demonstrated that signaling dynamics has a key role in overcoming extrinsic noise. Experimental measurements of information transmission in the ERK network under varying signal-to-noise levels confirmed our predictions and showed that signaling dynamics mitigate, and can potentially eliminate, extrinsic noise-induced information loss. By curbing the information-degrading effects of cell-to-cell variability, dynamic responses substantially increase the accuracy of biochemical signaling networks

Dual delayed feedback provides sensitivity and robustness to the NF-κB signaling module.

Many cellular stress-responsive signaling systems exhibit highly dynamic behavior with oscillatory features mediated by delayed negative feedback loops. What remains unclear is whether oscillatory behavior is the basis for a signaling code based on frequency modulation (FM) or whether the negative feedback control modules have evolved to fulfill other functional requirements. Here, we use experimentally calibrated computational models to interrogate the negative feedback loops that regulate the dynamic activity of the transcription factor NF-κB. Linear stability analysis of the model shows that oscillatory frequency is a hard-wired feature of the primary negative feedback loop and not a function of the stimulus, thus arguing against an FM signaling code. Instead, our modeling studies suggest that the two feedback loops may be tuned to provide for rapid activation and inactivation capabilities for transient input signals of a wide range of durations; by minimizing late phase oscillations response durations may be fine-tuned in a graded rather than quantized manner. Further, in the presence of molecular noise the dual delayed negative feedback system minimizes stochastic excursions of the output to produce a robust NF-κB response