Jeff Hasty - US grants

0239165
Hasty
Many fundamental cellular processes are governed by a genetic circuitry that employs protein-DNA interactions in regulating function. The biochemistry of the feedback loops associated with protein-DNA interactions leads to nonlinear effects, and the tools of nonlinear analysis become invaluable. In this project synthetic gene oscillator is being modeled and constructed in bacterial cells. The PI intends to couple the oscillator to an intrinsic periodic cellular process, which could lead to possible strategies for entraining or inducing network oscillations in cellular protein levels, and prove useful in the design of networks that interact with cellular processes that require amplification or precise timing. A multi-gene memory-based synthetic network that can function as a sensor of multiple transient signals, and thus form the basis for general control schemes requiring an "if/then" structure, will also be designed and constructed.

The proposed engineered gene circuits will lead to testable predictions regarding the current understanding of complex biological networks. This ability to design synthetic gene networks offers the exciting prospect of extracting carefully chosen subsystems from natural organisms, and focusing both modeling and experimental effort on determination of the behavior of the subsystems in isolation. The long-range goal of such work would be to assemble increasingly complete models of the behavior of natural systems, while maintaining at each stage the ability to test models in a tractable experimental system. The power of this approach is that it can be used to study simplified systems in order to gain insight into the general "modules" of gene regulation. These modules include sub-networks that act as switches or oscillators, as well as networks that act to communicate across a population of cells.

Work stemming from this proposal will impact education through the following projects: (i) Development of systems biology curricula for the Bioinformatics Program. To this end, research from engineered gene circuits will be used to form the basis for the systems biology curricula for undergraduate and graduate students. (ii) Development of an elementary school science program. This program will be developed in a school with a large minority component within the San Diego Unified School District. (iii) Recruitment of REU students. The PI will actively recruit two students per summer from the NSF Research Experiences for Undergraduates (REU) Program.

An important theme in post-genomic research is the dissection and quantitative analysis of the complex dynamical interactions involved in gene regulation. The molecular interaction maps involved in many important cellular processes often resemble circuit diagrams, and this analogy highlights the motivation for a quantitative description of gene regulation. An electrical circuit is invariably accompanied by a set of equations which faithfully describe its functionality, and it is built from knowledge of the properties of the individual components (resistors, capacitors, inductors, etc.) to provide a framework for predicting the circuit behavior resulting from component modifications. An acceptable model describing a given molecular interaction map should be similarly built from knowledge of the basic regulatory themes in order to enable the prediction of the effects of genetic perturbations of the system.

This project focuses on the construction and utilization of genetic "circuits" for dissecting, analyzing, and controlling the dynamical interactions involved in gene regulation. Previous investigations of engineered gene circuits have included the development of positive feedback and co-repressive switching networks, as well as an oscillating circuit. These previous studies have explored several of the building-block modules that constitute large-scale genomic wiring, and thus represent a first step towards an understanding of whole-genome regulatory complexity. The current project will build upon these previous studies by designing and constructing higher order networks consisting of coupled genetic regulatory modules. Specifically, the investigators will model and construct a regulatory network which couples a co-repressive module with an unregulated constitutive module, and explore how such coupling can induce oscillations in a toggle switch. As a second project, they plan to model and construct a synthetic network which couples the phase oscillator known as the "repressilator" with a relaxation oscillator module, and explore the synchronization properties of the coupled oscillator system. This approach could lead to an experimentally validated set of mathematical rules for understanding the complex circuitry of whole-genome regulatory processes.

The top-down approaches, which are used by many investigators to analyze the expression states of thousands of genes, have contributed towards understanding the global patterns of gene expression and assessing gene lethality. The bottom-up approach to be used in this project, which reduces the complexity of these gene networks to their essential components, will lead to the modular dissection of network architectures and refined descriptions of gene expression dynamics. The combination of these two complementary approaches will eventually lead to the elucidation of the organization and functioning of gene regulatory networks. In addition, work stemming from this research should enhance the ability to utilize synthetic gene networks as new logical forms of cellular control, and could in turn lead to important applications in functional genomics, nanotechnology, and gene and cell therapies.

DESCRIPTION (provided by applicant): Gene regulatory networks lay at the foundation of biological function and are responsible for driving the diverse cellular tasks required to sustain life. Developing a comprehensive description of cellular function in healthy and diseased states will require a precise quantitative understanding of the dynamics of the underlying interactions. This project will address this need by developing and experimentally validating computational models with predictive capabilities that can be used to understand the complexities of gene regulation in model organisms. Each aim will investigate a particular dynamic behavior, using a combination of modeling and experimentation to design a synthetic network that mimics a natural system, build the network and ensure that it meets the general design goals, and re/ne the computational model by experimentally testing its predictions and assumptions. The /rst aim will probe the behavior of two synthetic clock networks, in order to elucidate the key properties that de/ne their dynamic behavior. New microbial strains will be developed to explore and test predictions of the oscillatory response to changes in degradation rate and copy number of the network components. The second aim will investigate the use of biological clocks to coordinate behavior across a population of independent organisms. Modeling will be used to predict the network response to various driving and coupling mechanisms, and the synthetic clocks will be coupled to native pathways to investigate the possibilities of oscillatory entrainment and synchronization. The third aim proposes to construct synthetic signaling networks that can process and output pulsatile signals. A model will be developed and re/ned to describe the mechanisms of basic information processing at the single-cell level. In the /nal aim, experience with the synthetic microbial networks in the /rst three aims will be applied to develop several genetic circuits in mammalian cells. While many of the basic principles of gene expression should be conserved, the process of developing similar functionality in mammalian cells will likely yield insight into how the particular cellular environment a.ects gene regulation. Each network will be monitored with 0uorescence microscopy at the single-cell level using customized micro0uidic devices. These studies will provide crucial insight into several of the fundamental regulatory motifs that are essential for the propagation of life. PUBLIC HEALTH REVEVANCE: The survival of cells depends on their ability to carry out diverse cellular tasks such as driving the cell division cycle, responding to unpredictable environmental changes, and mounting an appropriate defense against stress. Many diseases arise as the result of individual genes or gene regulatory modules that fail to perform a speci/c task, leading to a breakdown of overall cellular viability. The central goal of this proposal is to construct, study, and model novel synthetic gene circuits that mimic the functionality of native networks, in order to develop a precise quantitative understanding of the dynamic interactions that underlay essential biological functions.

With the completion of plant genome sequences and with numerous large data sets emerging, an essential need has arisen to train students in plant systems biology at the interface of computational genomics, systems modeling and plant sciences. This entirely new interdisciplinary program will provide a unique training environment for graduate students and will position them at the frontier of systems biology to address major challenges facing plant scientists and agricultural biotechnology. The program will include focused mentoring of each student in two labs by two advisors from distinct disciplines. An entirely new curriculum, made possible through new faculty recruitment, will train students in post genomic plant sciences, proteomics, systems biology and network modeling and will include rigorous professional career preparation. A dynamic outreach program will assist in the recruitment of underrepresented students. Industry internships will draw upon the local active biotechnology arena. There are several broader impacts of this project. Interdisciplinary training in plant systems biology will stimulate innovations in the nation's agricultural and biotechnology industries, address global needs to feed the growing population and contribute to reducing environmental impacts of agriculture. Fresh water and food shortages are predicted to grow substantially in the coming decades. Plant biotechnology and molecular breeding will provide powerful contributions toward solving these problems. This comprehensive educational program will train graduate students at the interface of systems modeling and plant sciences and will have far-reaching impacts by producing highly trained scientists who will emerge as leaders in fields arising from the revolution in genomics information. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

DESCRIPTION (provided by applicant): Cells growing in a constant environment, such as a laboratory culture, are free to dedicate all of their resources to growth and division and often reach their maximal growth rate. However, in the natural world, cells rarely if ever experience static conditions. Whether they exist as a single cell or as part of a large multicellular organism, natural-living cells must cope with frequent changes to their surroundings. To survive in a dynamic environment, cells are equipped with gene networks that allow growth to continue in spite of changing conditions. This exibility comes at a price, and cells experiencing environmental uctuations usually do not attain their fastest growth rate. To fully understand the interface between cell growth and dynamic metabolism, we must study cells as they grow in a changing environment. In the proposed project, we will use the yeast galactose network as a paradigm of environment-sensitive gene regulation to ask how cells balance the need to respond to changes in the growth medium against the pressure to maintain growth. Throughout this study, we will rely on innovative microuidic tools to grow and observe single cells in precisely controlled dynamic environments. The dynamic data we collect will inform a set of mathematical models that will be used to identify key points of regulation in the galactose network, which will then be rigorously, tested using previously established molecular biology techniques. This multi-disciplinary approach will bolster our ability to identify new mechanisms of gene regulation that specially inuence the way cells perceive the growth environment, which are diffcult to observe in standard laboratory cultures. Our rst aim will be to study the eects of regulatory loops inherent to the galactose network on the sensitivity of cells to available carbon sources, and to determine how they contribute to the metabolic cost of growth on galactose. In previous work, we observed that the transcripts of several galactose network genes are spatially regulated. In the second aim, we will focus on the localization of these transcripts to test the hypothesis that the spatial regulation of gene expression can lead to ne temporal control in the cellular response to environmental signals. Our preliminary data show that the synthesis of galactose proteins is negatively eected by the mRNA of a specic cell cycle regulator. In the third aim, we will use queuing theory to explain how a competition for translation between specic transcripts can lead to a coupling of cell division and galactose metabolism and result in slower growth rates when glucose is unavailable. Finally, in the fourth aim, we will study the function of the regulatory loops of the galactose pathway by determining the robustness of the network in the context of varying degrees of competitive protein synthesis. The successful completion of this project will lead to advances in our understanding of how cells solve the universal biological problem of survival in an unpredictable environment. This work will be particularly relevant to understanding the mechanisms involved in balancing growth rate according to environmental cues, as is important in cancer biology, tissue patterning, and embryonic development. 1

3'Untranslated Regions; Accounting; base; Biochemical Reaction; biological adaptation to stress; Biological Models; Cells; Cellular Stress Response; Computer Simulation; design; Equation; Feedback; Generations; Genes; Genetic; Genetic Transcription; Height; improved; Mammalian Cell; mathematical model; Measures; Messenger RNA; Metabolic; Modeling; mRNA Decay; mRNA Transcript Degradation; Noise; Physiologic pulse; Property; protein degradation; Proteins; Reaction; Recruitment Activity; Regulation; Relative (related person); Repressor Proteins; research study; response; Saccharomyces cerevisiae; Series; Signal Transduction; simulation; System; Systems Biology; Time; tool; Translations; Untranslated Regions; Work; Yeasts

The use of microfluidic devices has facilitated the detailed study of cellular behavior by providing the ability to tightly control the cellular microenvironment. Microfluidic devices can be used to generate stable or dynamic concentration gradients 227, temperature gradients 228, and dynamically changing media supplies. In this way, cells and organisms can be probed in environments that closely mimic their natural habitats, or in response to defined by dynamic challenges, and valuable information can be revealed that is masked by standard batch culture techniques. Microfluidic devices also have the potential to vastly improve microscopy technology. Combined with sensitive cameras, high-precision automated stages, and powerful computers, researchers have the ability to rapidly acquire and store large arrays of microscopic images, which can provide great detail about a population of living and growing cells 229. Utilizing this technology, researchers can track gene expression dynamics with more precision and higher temporal resolution than possible with standard microscopy. In a recent example of this technology, we have developed a platform that can subject a population of cells to a dynamically varying stimulus (Fig. D3.1a). The device was designed to generate a fluctuating media signal by dynamically combining two media reservoirs according to a time dependent function. We applied this technology to examine a well-studied eukaryotic gene-regulatory network ¿ the galactose utilization network in S. cerevisiae. By comparing the experimentally measured response of the network to dynamically changing metabolic conditions to computational simulations of an otherwise validated mathematical model of the network we were forced to predict that mRNA half-lifes of two key transcription factors GAL1 and GAL3 must be regulated by glucose 230. This form of post-transcriptional regulation, in which glucose acts to down-regulate GAL protein synthesis, was a previously unknown source of regulation in the galactose utilization network, and was only made possible by experimentally examining the systems emergent properties in response to dynamically regulated environmental conditions.

DESCRIPTION (provided by applicant): NFkB is an inducible transcription factor that is activated in response to stress, cytokines and bacterial and viral pathogens, and is a major regulator of immune responses. The NFkB regulatory network processes signals that originate at a variety of cell membrane receptors to control the transcription of genes involved in immune and inflammatory responses, cell proliferation and survival. NFkB signaling has been linked to a number of human inflammatory diseases, such as arthritis and asthma, and because its activity is often up- regulated in tumors, NFkB signaling is actively being pursued as a target for cancer therapies. A detailed understanding of the signaling pathways that culminate in NFkB activation will be crucial for the discovery of effective NFkB inhibitors. To this end, this project will develop and experimentally validate computational models with predictive capabilities that can be used to understand the complexities of NFkB signaling in mammalian cells. Each aim will investigate a particular aspect of NFkB signaling through a combination of modeling and quantitative single-cell experiments performed in controlled microfluidic environments. Specifically, the first aim will develop a stochastic delay-based computational model of NFkB dynamics driven by transient TNF signals. For this, novel cell lines will be created that allow IKK and NFkB expression to be tracked in real time by fluorescence microscopy. Modeling will be used to predict the network response to various driving conditions and mutations in network architecture. The second aim will focus on the role of the IKK regulatory cycle in amplifying or filtering fluctuations in signals emanating from receptors. The rate of IKK turnover within the cycle will be varied to study the propagation of upstream fluctuations into the core of the NFkB module. The third aim will address the role of two parallel pathways (MyD88 and TRIF) initiated by pathogen-derived lipopolysaccharide (LPS) signals. Experiments involving either carefully controlled pulses of external LPS or invasive E. coli will be used in conjunction with mathematical modeling to characterize the dynamics and variability of the LPS-NFkB pathways. In the fourth aim, the role of cell-to-cell signaling in generating NFkB-mediated responses will be addressed by examining autocrine and paracrine TNF signaling. The successful completion of this project will result not only in a predictive computational model for NFkB signaling but also in insights into how this central regulatory network processes information in response to stimulation, inhibition, and drug modulation. PUBLIC HEALTH RELEVANCE: Project Narrative Nuclear factor B (NFB) is a key regulator of innate and adaptive immune responses. Misregulation of NFB may lead to a wide range of human diseases such as cancer, neurodegenerative disorders, and pathological inflammatory conditions. The central goal of this proposal is to develop and experimentally validate a reliable quantitative modeling approach that can be used to describe the NFB signaling network and predict its behavior in dynamic natural environments. 1

This project combines synthetic biology, microfluidic technology, and computational modeling to probe metabolic networks in three model organisms in dynamic environments and to study the evolutionary process of developing a competitive growth advantage. A synthetic genetic oscillator will control expression of essential metabolic enzymes in E. coli and Synechocystis (a cyanobacterium), and the growth characteristics of these engineered organisms in a periodically changing environment will be compared to wildtype, non-modified organisms. In yeast, synthetic biology and microfluidic technology will be used to "evolve" a well-characterized metabolic network and microscopy will be used to identify changes that confer a growth advantage in a dynamic environment. The combination of modeling and experimental approaches will provide insight into how organisms can be modified, either through engineered or natural evolutionary processes, to give them a growth advantage in fluctuating environments.

Broader Impacts: A quantitative understanding of microbial evolution will contribute to the development of new strategies to control microbial infections. An elementary school science program will be developed in partnership with the San Diego Unified and North County school districts. Project personnel and graduate students will work with elementary school teachers to prepare the graduate students to teach hands-on experimental science lessons that are integrated with the classroom curriculum.

Project Summary We will continue to design, construct and characterize genetic circuits. We will use micro uidic tools to grow and observe single cells and colonies in precisely controlled environmental conditions, and we will test a subset of the engineered bacterial strains as therapies in animal models. Single cell and colony dynamics will inform mathematical models that will be used to identify key design characteristics, which will then be rigorously tested using previously established molecular biology techniques. Eight graduate students and postdocs will work on multiple aspects of the project, while maintaining a particular focus on modeling or technology development for monitoring bacteria or in vivo characterization. Our track record demonstrates our ability to train personnel in a multi-disciplinary approach that has led to new tools for Synthetic Biology, along with an increased understanding of gene and signaling networks generally. Our recent characterization of bacterial circuits in animal models has served to highlight the need for bene cial strains that are stable and safe over therapeutically relevant timescales. Accordingly, our Speci c Aims focus on stability (Aim 1), delivery (Aim 2), safety (Aim 3), and in vivo testing (Aim 4). Gene circuits inevitably generate mutations that are selected to decrease the additional burden created by the inserted genetic machinery. Our rst aim will develop strategies for extending the \lifetime of gene circuits in bacteria before selective pressure disables their desired functionality. We will develop computational models and experimentally quantify how circuit redundancy increases circuit lifetime. We will use our experimental platform to monitor functionality across scales from single-cell to batch culture environments. Our second aim will primarily focus on engineering small bacterial ecologies. Here we will use modeling to guide the design of up to three interacting strains that can deliver therapies in a pre-determinted sequential order. In the third aim, we will build a safety circuit that triggers the death of all bacteria at a given threshold population density. The goal is to create an irreversible intracellular switch that rapidly and eciently kills all cells before mutations can compromise the safety strategy. In the nal aim, we will test the circuits designed in the rst three aims in animal models. We will engineer optical markers that enable characterization of the dynamics of bacterial colonies and tumor size in vivo. Importantly, the relative ease and low cost of bacterial cloning will inevitably lead to a bottleneck for the eld of Synthetic Biology, as therapeutic strains can be created at a rate that will far exceed the ability to test them. This highlights an acute need for quantitative models that have been thoroughly validated using in vitro technologies. Consequently, only a fraction of the circuits built in Aims 1-3 will be deemed worthy of in vivo testing. More generally, we anticipate that the computational models arising from these studies will be generally applicable across a wide range of emerging applications that employ bacteria.

RESEARCH CORE B ? SUMMARY The Microfluidics and Synthetic Biology Core will provide state-of-the-art technology and expertise for the manufacture and use of high throughput, single-cell data collection devices in the life sciences. The Core will assist users in designing and manufacturing custom microfluidic devices, provide users with the microscopic resources needed to gather high quality, single-cell data, and work closely with the Network Assembly and Mathematical Modeling Core (Research Core C) to help users analyze their data. The Core will accomplish these tasks through four Specific Aims. First, the core aims to rapidly and reliably create experimental tools for users by leveraging a state-of-the-art manufacturing facility. It will seek to increase the use of microfluidic devices in the life sciences by developing and distributing easy-to-use, reliable microfluidic devices that are custom made for research needs of users. Second, it will conduct cutting edge microfluidics research into improved devices for novel organisms and laboratory techniques. The Core plans on developing microfluidic devices for isolating and culturing native bacteria from environmental samples, as well as large-scale evolution devices capable of selecting beneficial traits in a population of microbes. Third, it aims to educate members of the life sciences community in the development, production and use of microfluidic devices. It will provide numerous opportunities for Center members to learn microfluidic manufacturing techniques and the necessary experimental skills for using these tools. Fourth and finally, the Core will develop and distribute open source hardware and software for supporting microfluidics research. We will model this effort after our successful dial-a-wave system, which is fully described online in a freely-accessible webpage. Detailed instructions for assembling hardware will be provided as well as made-to-order systems for research groups both in the Center and at other institutions. The Core leader, Dr. Jeff Hasty, and the lab's supervisor, Dr. Michael Ferry, are leaders in the field of synthetic biology and the use of microfluidics tools in the life sciences. Together they will continue their work assisting the research of all investigators interested in exploring these powerful devices.

PROJECT 2: PROTEIN TURNOVER DYNAMICS SUMMARY Protein abundance levels are controlled through regulatory processes that govern protein synthesis and degradation. Although translational control is now a widely appreciated mechanism for regulating gene expression and proteome remodeling, a systems-level relationship between translational regulation and cellular physiology remains largely unexplored. The overarching objective of this project is to discover the mechanisms that lead to alterations in proteome flux and predict their responses to dynamic changes in the environment. This objective will be pursued through three aims. First, we will investigate how the availability of key components of the translational apparatus, such as ribosomes, tRNAs and initiation factors, are balanced with the transcriptome and proteome composition depending on a specific static carbon source. This will allow us to develop detailed mathematical models that link global translation, transcription and cell physiology. The predictions of the models can be tested by artificially perturbing the transcription-translation balance. Second, we will study the global feedback mechanisms by which the cell adapts its translational capacity to shifts in the carbon source experimentally, while in parallel extending our mathematical models to integrate the regulatory mechanisms linking carbon source and growth rate with a systems-view of the translation system. We will investigate, using experiments and mathematical models, how differences in the adaptation time of the various components of the translational machinery, proteome and mRNA composition to a fluctuating carbon source affect the cell's translational capacity, and hence, how the cell's global feedback mechanisms respond to dynamic and stochastic changes in the carbon source. Finally, we will build a quantitative mathematical model of proteome flux using a combination of quantitative proteomics and mathematical modeling. We will quantify key parameters of proteome flux including protein translation rate, protein degradation rate, mRNA abundance, and protein abundance. This will be done in both rapidly cycling cells and non-dividing neurons to determine how proteome flux is re-wired in post-mitotic cells. We will use serum starvation as a means to limit nutrients and measure alterations in proteome flux upon nutrient withdrawal and replenishment. We will also investigate dynamics in proteome flux upon mTOR inhibition as a pharmacological means to mimic nutrient deprivation. This will allow for deterministic modeling of how proteome resource allocation is altered, in two divergent cell types, upon nutrient limitation.  

? DESCRIPTION (provided by applicant): The mission of the San Diego Center for Systems Biology (SDCSB; sdcsb.ucsd.edu) is to advance the discipline and application of systems biology in the greater San Diego area and to serve as a nucleus for systems biology education and training. The SDCSB brings together a community of 19 outstanding faculty, over 100 trainees and technical staff spanning four world-renowned institutions: the University of California San Diego, the Salk Institute for Biological Studies, the Sanford-Burnham Medical Research Institute and the Ludwig Institute for Cancer Research, all located on the Torrey Pines research mesa. The SDCSB has been supported as an NIGMS National Center for Systems Biology since 2010. Over the past five years our research has led to significant systems biology advances, including genome-wide studies of how stress remodels transcriptional and genetic networks, discovery of an independent metabolic clock coordinating cell growth through cycles of TOR1 activity, demonstration that much of the Gene Ontology can be inferred directly from `omics data, prediction of cancer survival time and drug response by an approach called Network Based Stratification and a series of major feats in engineering of synthetic coupled genetic circuits. We began two successful annual symposia, formal systems biology coursework, a seminar series and journal club, workshops on systems biology techniques and a faculty seed grant program that was used to recruit 10 new systems biology faculty to UCSD. We successfully trained more than 90 graduate students and postdoctoral fellows, seven who are now in independent positions. The theme of this renewal application - From Maps to Models - addresses an important challenge in systems biology: traversing between network maps and mathematical models, two very successful but so far mostly separate biological representations and modes of study. Network maps tend to be global, static, abstract and descriptive, whereas mathematical models tend to be local, dynamic, detailed and predictive. Guided by this overarching theme, four SDCSB research projects seek to develop a general library of maps and models relevant to fundamental cellular and super-cellular processes, including the spatiotemporal architecture of the genome (Project 1), protein turnover dynamics (Project 2), cell-cell communication and heterogeneity (Project 3) and environment-genome interactions (Project 4). These mapping and modeling activities are fueled by technologies advanced across three SDCSB core platforms, aspects of which serve as an exemplar for systems biology efforts nationally and internationally. This renewal application represents a tightly integrated set of research projects, cores and educational efforts. The keys to achieving this integration are four-fold: (1) A consistent theme of developing global network maps coupled to predictive models; (2) Support of an innovative systems biology core platform jointly developed and applied across all projects; (3) Cross-cutting faculty recruitment, postdoctoral and graduate programs in systems biology, from which the center recruits and staffs its projects; and (4) Outstanding symposia, retreats, journal clubs and workshops in which we all participate. The teams spearheading these efforts are comprised of investigators with diverse backgrounds and expertise, resulting in a multidisciplinary approach incorporating genomics, bioinformatics, synthetic biology and biophysics.

Rapid and coordinated response to environmental fluctuations and the ability to maintain function in a broad range of conditions is a key to survival in the biological world. Therefore, organisms employ complex regulatory strategies that combine sensitivity and robustness in response to environmental changes. One potential mechanism for such responsiveness that has emerged through recent studies of enzymatic networks is a queueing effect in protein abundances. The phenomenon appears to be widespread in biology. This research project employs mathematical modeling and synthetic biology to explore and better understand this mechanism and its role in biological functions.

This project investigates a queueing effect in intracellular networks. As processing enzymes transition from underloaded to overloaded conditions, the corresponding protein abundances (queues) increase drastically, and in a highly coordinated fashion. A number of recent observations suggest that enzymatic networks are often poised near the balance point between underloaded and overloaded regimes where it makes them highly sensitive and responsive to transient environmental fluctuations. This phenomenon appears to be widespread in biology; furthermore it should be highly optimized to a broad range of environmental conditions by adaptive regulatory mechanisms. In this project, the investigators combine rigorous mathematical analysis with quantitative computational modeling and synthetic biology to pinpoint the mechanisms and probe the role of adaptive queueing in the dynamics of small gene circuits as well as large intracellular enzymatic networks in bacteria and yeast. The investigators make use of their combined expertise in queueing theory, stochastic kinetic modeling, design of robust synthetic gene circuits and microfluidic technology to elucidate and characterize the role of adaptive queueing as a fast and flexible signal transduction and regulatory mechanism in bacteria and higher organisms.

Rapid advances in the forward engineering of complex genetic circuits in living cells proved that the young discipline of Synthetic Biology has the potential to transform modern biotechnology. A predictive understanding of living systems is a prerequisite for designed manipulation in bioengineering and informed intervention in medicine. Such an understanding requires quantitative measurements, mathematical analysis, and theoretical abstraction. The advent of powerful measurement technologies and computing capacity has positioned biology to drive the next scientific revolution. Synthetic Biology provides a natural platform for the development and testing of models and general network principles using quantitative data from engineered genetic circuits. One of the major fundamental problems of Synthetic Biology is the propensity of synthetic circuits to evolve and obliterate their intended functionality. Thus, it is important to develop a quantitative approach to make genetic networks more robust and stable which also will make them more useful in therapeutic applications. The overall goal of this project to explore the challenges and exploit the opportunities that accompany the inevitable selective pressure that arises in synthetic biology through a combination of cutting-edge experimental and computational tools. Successful accomplishment of this project will lead to significant advances in understanding the interaction between synthetic circuits and host genome in the evolutionary context. This project will provide ample opportunities for cross-disciplinary training of the new generation of quantitative biologists. Furthermore, to broaden the impact of this project beyond academia, its participants will expand a highly successful elementary school science program that that foster collaboration between researchers and teachers at partner elementary schools to improve hands-on science education.

To reach the overall goal of the project, the recently developed "synchronized lysis circuit" will be used as the major focus. This synthetic gene circuit functions by triggering death of around 90% of the bacteria at a threshold population density, leaving the remaining 10% of the cells to grow back to threshold and restart the cycle. This generates a distinct phenotype characterized by population cycling that can be monitored across a wide range of length scales using micro- and bench-top chemostats. It also introduces a strong selective pressure that can lead to rapid evolution. The investigators will develop quantitative measurement technology and computational modeling that will lead to a quantitative statistical characterization of the circuit-host evolution process. Sequencing of the circuit and mutated sections of the host genome along the time course of the experiments will be used to reconstruct the evolutionary path the systems have taken. Mathematical modeling will guide experiments and help extract key parameters characterizing evolutionary dynamics of the synchronized lysis circuit. Using the gained knowledge, the researchers will engineer a "lysis circuit stabilizer" module that kills mutant bacteria losing lysis efficiency, and explore ways to use lysis circuit as a tool in directed evolution of synthetic circuits.

This award was co-funded by the Systems and Synthetic Biology (SSB) program in the Molecular and Cellular Biosciences (MCB) Division in the Biological Sciences Directorate and the Biotechnology and Biochemical Engineering (BBE) program of the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET) in the Engineering Directorate.

Project Summary Cellular aging is a complex biological process, associated with many diseases, such as cancer, diabetes, and neurodegenerative diseases. New therapeutic approaches to slow aging hold promise for reducing global healthcare burdens of chronic diseases. However, the development of these approaches requires a deep understanding of the mechanisms of aging, which remains a challenging goal. Static population-based studies are insufficient to reveal sophisticated molecular mechanisms that underlie the aging process, because genetically identical cells have various intrinsic causes of aging and widely different rates of aging. Furthermore, although many single genes have profound effects on lifespan, how they interact and function within gene regulatory networks to regulate aging and how these interactions change dynamically during aging remain largely unknown. To overcome these challenges, we have developed high-throughput microfluidic technologies to track the dynamics of molecular processes throughout the replicative lifespans of single S.cerevisiae cells. In the proposed research, these dynamic measurement technologies will be integrated with computational modeling to systematically characterize and quantify the collective dynamic behaviors of aging-related molecular networks. In Aim 1, we will quantitatively characterize the phenotypic changes associated with distinct causes of cell aging and, based on these data, construct a phenomenological model of the aging process, upon which we will build mechanistic models of the conserved Sir2 and protein kinase A (PKA)-regulated molecular networks, both of which are deeply connected to aging. In particular, in Aim 2, we will develop a mechanistic model of the Sir2-regulated molecular network to predict its dynamics and regulatory roles during aging. High-throughout single-cell analysis will be performed to track the dynamics of Sir2-regulated genes and test the model predictions. In Aim 3, we will systematically characterize the PKA- regulated stress response during aging and develop a mechanistic model to quantify and predict the effects of environmental cues on aging. We will systematically examine the dynamics and contribution of stress response genes under various environmental perturbations. These experimental measurements will be used to test the predictions, refine the model, and more importantly, provide insight into the basic mechanisms underlying the environmental control of aging. To accomplish these aims, we have assembled a strong interdisciplinary team of investigators with complementary expertise, who will work synergistically to tackle fundamental questions in the biology of aging from a systems biology perspective. !

Rapid advances in the forward engineering of complex gene circuits in living cells has established the transformative potential of synthetic biology for uncovering the rules of life and controlling biotechnological processes. This project aims to harness and further develop the ability of different strains and species of bacteria to communicate and respond synchronously to changing environments. Our multi-disciplinary approach combines advances in genetic engineering, microfluidic technologies, and computational methods to design novel gene networks that can orchestrate bacterial population dynamics in the complex environment using relevant environmental cues.

The overall goal of this project is to model, engineer, and characterize bacterial circuit dynamics in the complex environments. Two new bacterial gene circuit systems will be designed. The first is a long-range coupling platform for synchronizing colony behavior through hydrogen peroxide. The second is a system of lysis-mediated gene circuits that can be used to regulate the co-culture of multi- strain ecologies. The investigators will develop computational modeling and quantitative measurement technology that will lead to both informed development and quantitative characterization of newly engineered circuits in spheroid environments derived from animal tumors. Using this knowledge, the researchers will combine developed circuits with therapeutic delivery or biosensing strains to create relevant and functional synthetic systems for application to complex environments. An Elementary School Science Partnership program, which was initiated in 2011 with NSF support, will be expanded. The program currently serves two Title I elementary schools in the San Diego area, Ocean Knoll and Lafayette, and includes the District?s Deaf and Hard of Hearing program. The program is led and run by UC San Diego students under supervision from the investigators. This project will provide ample opportunities for cross-disciplinary training of the next generation of quantitative biologists. In a broader context, it will bridge the methodological gap between the study of bacteria in a lab setting and their deployment in complex real-world environments.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Summary There is a clear imperative to develop potent, cost effective therapeutics to confront the challenge cancer poses to society. Here we address this need by developing synthetically engineered cells effective against a broad range of cancer types with a special emphasis on colorectal cancer (CRC). This cancer type is the second most common cause of cancer death in the US, with more than 50,000 Americans dying every year. Recent research demonstrates the power of genetic engineering to make significant advances towards more efficacious cancer therapy. The introduction of genetically engineered cells, such as chimeric antigen receptor T (CAR T) cells, has shown great promise for treating many types of B cell malignancies, but unfortunately targeting CAR T cells to solid tumors remains challenging. In this project we will use the tools of synthetic biology to make new engineered therapies based on bacterial rather than mammalian cells. Certain bacterial species have demonstrated a useful ability to ?home in? and selectively colonize solid tumors without infecting healthy tissue. This tumor targeting property will be exploited in the proposed work to deliver safe, effective therapies directly to the locations where they are needed most: the solid core of tumors. Previously we developed a bacterial therapeutic and tested it in an animal model of metastatic disease. In contrast to other approaches utilizing bacterial cells, this ?lysis strain? does not require specialized genetic modifications for the secretion of encoded cargo, it simply releases it into the environment when the cells burst. Initially we will genetically modify the lysis strain to produce a wide range of therapeutics for testing, including toxins (from bacteria, animals and plants), enzymes, antibiotics, and apoptotic peptides. Next we will analyze the tumor microbiome from human samples since we hypothesize that the native bacterial population's composition will provide a unique signature (analogous to a fingerprint) that can be used to divide tumors into distinct subtypes. We expect to use these fingerprints to identify other species with superior suitability for therapeutic delivery in treating CRC. Once identified we will develop two in vitro assays for testing the candidate strains. We will use microfluidic technology to create a high throughput co-culturing system for bacteria and a cancer cell line. In parallel, we will develop a co-culturing system for bacteria and organoids that are generated from the same human tumor samples which had been previously used for strain identification and fingerprinting. Lastly we will test the most promising therapies in an animal model of colorectal cancer to determine efficacy in a pre- clinical model.

Project Summary It is increasingly clear that bacteria play an important role in human health. While it is natural to focus on how intestinal bacteria affect disease, intriguing ?ndings have elucidated the extent to which bacteria inhabit solid tumors. Microbes have been detected in lung, pancreatic, breast, oral, gallbladder, ovarian, liver, and colorectal cancers. Localization has been ascribed to several mechanisms, including preference for anaerobic or facultative anaerobic bacteria to grow in the hypoxic core of tumors, presence of bacterial nutrients, lack of immune surveil- lance, and leakiness of the often poorly structured vasculature surrounding neoplastic tissue. This tendency for localization to solid tumors suggests that bacteria could be engineered for precise and robust drug production and delivery from within the solid tumor environment. This dovetails with 20 years of progress in synthetic biology, which has tended to focus on microbial engineering. However, information on how the tumor microenvironment affects bacterial growth is largely unknown. The microenvironment will affect bacterial gene expression that ul- timately underlies the functionality of engineered therapies, and it is dif?cult to imagine a predictive framework for engineered bacterial therapies without a quantitative understanding of how bacteria react to the environment of a growing tumor. We will use a probiotic strain of E. coli with an established safety record to develop a novel class of biosensors to noninvasively investigate bacterial growth in the tumor microenvironment. Initially, we will develop lysis-based biosensors that respond to speci?c tumor environment targets: hypoxia, pH, glucose, and lactate (Aim 1). We will also engineer an inducible quorum sensing (QS) system that enables external control of bacterial population dynamics, including the ability to eliminate a speci?c strain whenever desired (Aim 1). Together these strains will allow us to modulate and monitor population dynamics in vivo, enabling both sens- ing of the local environment and maintenance of an external control switch. We will test these strains using an established in vitro organoid model (Aim 2) and in two clinically relevant animal models for solid tumor growth. Additionally, we will use our previously developed dynOMICS technology to screen tumor extract from the two animal models and construct a second suite of biosensors for monitoring the tumor environment (Aim 2). These biosensors will then be tested in the animal models. We will visualize bacterial populations in a colorectal tumor model with bacteria that are engineered to produce luciferase in order to monitor colony dynamics using our es- tablished methods (Aim 3). We will also build on recently reported technology whereby bacteria are modi?ed for use with ultrasound through addition of gas vesicles that permit high resolution imaging of the engineered bac- teria. We will use the ultrasound method to investigate NASH-induced hepatocellular carcinoma (HCC) where a high-fat diet is used to induce HCC at 20 weeks in mice (Aim 4). This project will quantitatively characterize how bacterial strains sense, respond, and grow in the tumors. The results will establish a platform for future exploration of therapies that are produced and delivered by bacteria that grow within solid tumors.