Ron Weiss - US grants

The proposed research will use genetically engineered stem cells capable of autoregulating their differentiation into insulin-producing beta cells by incorporating artificial cell-cell communication and carefully regulated multistep differentiation. The overall goal is to model an artificial tissue homeostasis system which contains regulatory elements that will allow cells to detect stem cell and beta cell populations and differentiate appropriately depending on cell population thresholds. Steps toward this goal will be carried out by controlling the differentiation of mES into pancreatic beta cells in vitro. The proposal integrates the multidisciplinary areas of synthetic, computational, and developmental biology; biological and tissue engineering; and modeling/computational and stem cell biology. Signaling elements are used in the first step of differentiation (ES cells into endoderm) where the native alpha-fetoprotein (AFP) is used to regulate expression of red fluorescent protein and allows for the visualization of mES transition into endoderm by cells) is?nfluorescence microscopy. The second step of differentiation (into beta visualized by GFP expression (from the Mouse Insulin Promoter (MIP)), which is caused by natural insulin production. These experimental observations will direct the forward-engineering of the synthetic autoregulating system.

From Retroactivity to Modularity:
Design and Implementation of a Genetic Insulation Device in Yeast
Domitilla Del Vecchio and Ron Weiss

Modularity is an important property of engineered systems, yet it is debatable whether it is a general property of natural bio-molecular systems. Discovering the extent of modularity and understanding its mechanisms is one of the most important open research problems in systems biology. Furthermore, the long term success of synthetic biology critically depends on the ability to implement modular systems in such a way that the properties of individual components do not change unpredictably upon their interconnection. Our proposed research seeks to understand the mechanisms of modularity in regulatory networks using a combined theoretical/experimental effort through the design, implementation, and analysis of a special genetic retroactivity insulation device in yeast. This novel device effectively decouples transcriptional components that would otherwise be highly interlocked by allowing propagation of a regulatory signal in the forward direction while minimizing the undesired phenomena of retroactivity in the reverse direction. Besides providing an important new circuit element for synthetic biologists, the mathematical analysis of an insulating device will lead to an improved understanding of the extent to which modularity is present in regulatory networks in biological systems. The first aim of our research is to show that retroactivity affects regulatory networks without insulation and therefore that modularity is not a natural property of bio-molecular signaling pathways. Second, we will demonstrate a novel insulation device that counteracts retroactivity and allows a circuit to transmit information reliably despite loading from downstream clients. This special circuit will be designed and placed between two connected components to insulate them from retroactivity effects. We will study the device?s performance, ability to regulate many copies of a downstream component, and requirements for correct operation. Third, we will study how well the insulation device is decoupled from the cellular environment. To this end, we will perform system-level analysis and investigate crosstalk between the device and various important cellular processes.
Intellectual Merit: Existing synthetic circuits lack an ability to insulate a driving input signal from retroactivity of the output load, precluding modular composition of complex biocircuits. To address this problem, we propose to construct and characterize a synthetic phosphorylation-based insulation device and instrumentation pathway that will demonstrate a general and modular technique for building sophisticated, large scale biological systems. Our novel technique leverages the integration of special rapid feedback mechanisms into biocircuits in order to create insulation devices, and hence has implications for the design and implementation of many other biological motifs and networks. Our research includes new theoretical and computational analysis of devices with feedback and retroactivity, and these new tools will also be applicable for the study of biological network problems other than retroactivity. This analysis is fundamentally important for tuning and characterizing desired insulation properties while minimizing interference with other cellular processes.
Broader Impact: In synthetic biology, this research will lead to a general understanding of the engineering principles of modularity for bio-molecular systems design. Likewise, in systems biology, this research will address a fundamental question ? To what extent is modularity an inherent property of biological systems? The product of our collaborative efforts will lead to the discovery in natural systems of motifs similar to our insulation device and to the explanation of how modularity is achieved, including insight into when and where natural systems implement modularity and for what purposes. Ultimately, the resulting capability of modular composition to achieve defined engineering goals in biological systems will have tremendous impact on human therapeutics, including regenerative medicine, diabetes, and cancer therapy, as well as in other diverse areas, including biofuel production, environmental remediation, pharmaceutical production, and biosensing applications. This research will contribute to new interdisciplinary courses and will be integrated into a ten week undergraduate synthetic biology Summer program that culminates in an international competition and has a track record of attracting women, under-represented groups, and high schools students to the field.

The STC on Emergent Behavior of Integrated Cellular Systems (EBICS) will develop the science and technology needed to engineer clusters of living cells (also called biological machines) that have desired functionalities and can perform prescribed tasks. These machines will consist of sensing, information processing, actuation, protein expression, and transport elements that can be effectively combined to create functional units. It is envisioned that these biological machines will perform tasks such as processing systems that detect toxins in the environment and neutralize them; smart plants that sense and respond to the need for water and nutrients, surrogate organs that are used in place of animals to test new drugs; and biological factories that sequester CO2 in a continuous flow process.
Intellectual merit of the proposed STC lies in the realization of the field of engineered biological systems that seeks to understand how cell systems interact to produce coordinated, emergent behavior under the control of local micro-environmental cues consisting of biochemical factors and physical stimuli. Recently, much attention has focused on research into the creation of single-cell machines or factories. While such uni-cellular machines hold enormous promise, the potential impact of multi-cellular systems that incorporate populations of specialized cells assembled into a single integrated unit is far greater. New insights and understanding into cell-cell and cell-matrix interactions are critical to attaining this goal, and this will require new technological platforms to bridge molecular scale interactions with macroscale behavior of complex biological systems. The proposed research direction is truly transformative and revolutionary at the interface of science and engineering.

Broader impacts of EBICS include the potential to significantly enhance the understanding of emergent biological behaviors under integrated biochemical and physical cues at the cellular, cell network, and cell population levels; and an outreach and knowledge transfer program that will attract more young students into science, technology, engineering, and mathematics (STEM), and particularly into the interdisciplinary field between biology and engineering. The center, through its alliance with minority serving institutions and active collaborative research plans with those institutions will improve the participation of underrepresented minorities in STEM. The EBICS education plan includes a two-track, integrated graduate program that effectively merges the essential sciences and technologies needed, a graduate teaching consortium to offer a selection of courses synchronously in all partnering institutions via the internet, and international experiences by internships. Graduate courses developed by EBICS will be recorded and made widely and freely accessible through OpenCourseWare. The novel and revolutionary nature of the research will attract the best and the brightest students into this program and strengthen the future US workforce?s competitiveness in a global economy. Applications based on the Center?s potential discoveries will have direct impact on critical national needs such as energy, the environment, security, and healthcare. The vision of the proposed STC is clearly aligned with the national research and education strategic priorities.

This NSF award by the Office of Emerging Frontiers in Research and Innovation supports work to understand how dynamic signaling and cell fate decision circuits in individual cells give rise to multicellular system-level behaviors such as developmental pattern formation and how perturbations to these circuits lead to undesirable consequences such as cancer. These questions will be addressed using three integrated methods: (a) Physical devices for tracking stem cell behaviors in a physiologically relevant environment; (b) Mathematical modeling of cell fate decision circuits; and (c) Engineering of synthetic genetic circuits capable of elucidating fundamental design principles.
Intellectual merit
The team will study the role of intercellular Notch signaling in intestinal colon crypt formation and colorectal tumorigenesis. In Goal 1, an integrated platform including in vitro microscale devices and in vivo mouse homing models will be developed to track signaling dynamics and cell fate decisions in a physiologically realistic environment. In Goal 2, signaling dynamics will be characterized to develop mathematical models of pattern formations in colon crypts. These models will help interpret experimental data and predict perturbation results. In Goal 3, synthetic circuits based on Notch signaling and bacterial quorum sensing components will be implemented in colon cancer stem cells to form de novo patterns, which will be tested using the integrated platform from Goal 1.
Broader impact
This work will have three main broader impacts: (a) facilitating biomedical applications, (b) generating broadly useful tools for the exploration of multicellular patterning systems, and (c) direct impacts on science education through specific activities of the principal investigators and other team members.
Facilitating development of therapeutics. The model system, colon cancer stem cells, is resistant to conventional chemotherapies. Developing effective therapeutics that specifically target cancer stem cells without damaging normal stem cells will require a deeper understanding of the underlying circuits that control self-renewal, differentiation and homeostasis in normal and cancer stem cells.
Tools for understanding differentiation and patterning. The physical devices developed here can be further developed into high throughput drug screening platforms; the synthetic constructs can be used to study signaling mechanisms in diverse cell types; and the mouse homing models enable in vivo studies of human colon stem cells and CRC tumorigenesis.
Educational activity. Using materials from the proposed study, the PIs will work together to teach synthetic biology and mathematical modeling for mammalian systems in systems and synthetic biology courses. The materials will also be included in a synthetic biology textbook in development. The proposed project will be used as an example for K-12 outreach lectures. Finally, simplified versions of the physical devices and the mathematical model from this project will be used as teaching tools for the CURIE Academy, which is a one-week summer residential program focused on creating an interactive atmosphere to engage female high school students in engineering.

DESCRIPTION (provided by applicant): The long-term objective of our proposal is to greatly improve anti-cancer therapy and dramatically reduce the side effects of anti-cancer drugs by engineering sophisticated regulatory mechanisms that govern the release of a drug in a patient. Cancer is caused by multiple changes in the molecular profile of a healthy cell, and a mechanism for detecting these changes based on the notion of logic functions forms the basis of our approach for cancer diagnostics in individual cells. Existing cancer therapies usually focus on single biological cues with occasional but limited success. As a result, there is an increasing appreciation of the fact that multiple disease markers must be considered in order to achieve precise detection of a disease state. The crux of our proposal is a new method where the therapeutic agent samples multiple aberrantly-expressed molecular markers in individual cells and uses pre-programmed diagnostic criteria to control the response based on the combinatorial over- or under-expressed state of the markers. We focus on microRNAs, a family of molecules whose expression is often altered in cancer, as the biomarker cues whose expression profile ultimately controls the activity of the drug. We engineer hybrid RNAi/transcriptional regulatory networks where network elements interact with the intracellular microRNA cues and with each other to implement the desired control mechanism. For example, for selective targeting of HeLa cells we require expression of a pro-apoptotic protein only when microRNAs miR-21, miR-17, and miR-30a are over-expressed and miR-141, miR-142, and miR-146 are not expressed, all at the same time. In our experiments we use human cell culture as a test-bed for the proposed approach. The specific aims of our proposal (1) conjecture that a relatively small number of microRNA markers is sufficient for precise diagnosis of specific cancer lines at the single cell level, (2) hypothesize that a 'logic circuit'designed to detect the microRNA expression profile of a specific cancer cell line can efficiently destroy these cell, and (3) verify that the operation of this circuit inflicts minimal damage to healthy cells. To design the circuits, we use published microRNA expression data and data that we collect with our own measurements using single-cell readouts. We will develop computational tools to elucidate the best possible diagnostic criteria aimed at maximizing selectivity and specificity while minimizing the number of markers used, even in the presence of cell-to-cell variability in phenotypically uniform populations. The diagnostic criteria, i.e. the expression profile of selected markers, will guide construction of the circuits. The circuits use a combination of transcriptional and post-transcriptional regulatory elements that respond to the microRNA markers and together control expression of the "killer" protein. The first criterion for our system's success is the efficiency with which cancer cells are eliminated from a mixed culture containing cancer and healthy cells. The second criterion is the absence of side-effects on healthy cells in the mixture. We will analyze side effects by a combination of observations, including phenotypic assays, gene expression analysis and functional assays. PUBLIC HEALTH RELEVANCE: Existing cancer therapies are often characterized by severe side effects and less-than-perfect elimination of their intended targets. We propose a paradigm shift to cancer therapy where engineered viruses that harbor sophisticated sensing and regulatory network determine with high precision whether any individual cell is cancerous or not, and carry out a pro-apoptotic response only when appropriate. Our approach will reduce side-effects and increasing the efficacy of anti-cancer drugs, hence extending the life expectancy and lessen the suffering of cancer patients.

This technology introduces a new non-invasive, biopsy-adjunct with a MNP platform for targeted tumor delivery. It employs closely sized magnetic nanoparticles (CS-MNP's) for determining their concentration and spatial distribution in tumor vasculatures as measured by magnetic resonance imaging (MRI). The relationship between CS-MNP size and Vibrating Sample Magnetometer (VSM) measurement follows from the physics of superparamagnetism (SP- theory). By correlating particle size and concentration within a tumor and its MRI response, the data can likely deliver tumor characterization without biopsy. This work characterizes CS-MNP's, animal testing and the quantification of the MRI imaging signals. Resulting data supports continued development of CS-MNP-products and new patents. It addresses the production of CS- MNP products for preclinical studies and business development for a very large market requiring more precise, higher margin products. These new products and services represent the future of MNP-based products for imaging and drug delivery. This technology has potential for a new biopsy-adjunct with CS-MNP's for targeted tumor delivery. Measurement of CS-MNP's in the tumor vasculature by MRI may become a major diagnostic tool and use for targeted therapeutics. It may provide for differentiating benign disease from malignancy, insightful surgical planning, and evaluation of chemotherapy and radiation treatment

DESCRIPTION (provided by applicant): The long-term objective of our proposal is to develop a novel technological platform for generating valuable live cell microRNA expression data for cancer cells. MicroRNAs are a class of evolutionary conserved non-coding RNAs that regulate stability and translation efficiency of target mRNAs, playing a critical role in regulating development as well as disease states. While contemporary platforms such as microarrays and RT-qPCR are capable of measuring aggregate miRNA levels, only limited research has addressed single-cell distributions and no systematically created dataset is available for cancer research. Most significantly, distributions and time-series data are required to identify multimodal miRNAs, characterize expression variability, find significant inter-miRNA correlations, and enable more accurate analysis and classification of cancer cell types and states. Finally, no functional datasets exist that characterize the interaction of miRNA with genetic circuit elements that will be useful for both cancer diagnosis and gene-based therapy. We propose an innovative combination of microfluidics and synthetic biology to overcome this hurdle, leading to massive new datasets, large libraries of biosensors, and ultimately therapeutic cancer cures. We will utilize a high throughput microfluidic platform to assemble libraries of genetic circuits that act as sensors to measure microRNA expression levels in target cell lines. These circuits will feature inputs for single or multiple microRNAs. We will assemble a library of single-input microRNA sensors for a large set of experimentally-validated human microRNAs (412, presented in the microRNA atlas) and use these sensors to measure expression levels in 15 target healthy and cancer cell lines. We will use sensors featuring multiple microRNA inputs to generate previously unavailable microRNA correlation data, thus providing an avenue for gaining deeper insight into the operations of pathways important to diseases such as cancer. These expression data sets, in contrast to data derived from microarrays and similar techniques, will be experimentally measured in real-time from large numbers of individual live cells via a separate microfluidic module. We will use the microRNA correlation data to increase the precision of cancer cell classifiers.

The MIT Center for Integrative Synthetic Biology will create the therapeutics of the future by integrating systems biology and synthetic biology. Systems biology, synthetic biology, and fundamental research in health-related applications are three major disciplines that have thrived in the last decade. These fields have pushed the frontiers of biomedical science with the development of high-throughput platforms for generating and analyzing systems-level data, unprecedented abilities to engineer biological systems, and significant advances in our understanding of the molecular mechanisms underlying human disease. However, due to the diverse expertise required in each discipline, these efforts have been largely independent from each other. Therefore there is a significant opportunity for the integration of systems biology, synthetic biology, and health-related applications in a single research center. Our core group of researchers at MIT is poised to spearhead efforts to overcome these challenges with the MIT Center for Integrative Synthetic Biology. A key feature of the proposed Center is our focus on interdisciplinary and collaborative research into next-generation therapeutics. Our major disease-related targets revolve around Cancer, Diabetes, and Infectious Diseases. Together systems biology and synthetic biology will contribute to significant advances in health-related applications. By integrating top-down systems views of disease with bottom-up synthetic construction of novel treatments, this community will create new disease therapies with the ability to integrate multiple inputs and deliver specific interventions. Numerous synergies in synthetic biology, systems biology, and health-related applications will be generated by the MIT Center for Integrated Synthetic Biology which would be difficult to achieve via solely independent investigator-led research efforts.

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Several common aspects and goals of computational and biological systems suggest that we can use one as a source for studies of the other and vice versa. With recent advances in our ability to generate and analyze biological data it is now possible, for the first time, to design new, bi-directional studies that directly link biology and computer science. This form of coupled experimental and computational thinking, which will be utilized in this project, can greatly benefit both biology and computer science. The proposal also seeks to help establish the usefulness of this approach to increase public interest in science and engineering and to provide interdisciplinary educational and research experiences for a diverse population of students.

This joint experimental-computational project will use a bi-directional approach to study the design, communication and coordination of networks utilized by Escherichia coli. The overall goal is to determine how biological systems utilize distributed networks over different scales, environments and varying communication strategies. The project will address biological questions ranging from how information processing is performed in signaling networks to the importance of various topological features of E. coli networks to coordination in a population of bacterial cells. In addition to addressing the biological questions these studies seek to provide insights into the design and usage of networks for distributed computational systems that can tolerate harsh environments, failures and limited resources making them applicable to a wide range of real world applications. Distributed networks are utilized by species ranging from single cell organisms to mammals. The proposal seeks to determine shared principles regarding the design and usage of such networks in E. coli. and the findings can be applied to understand similar systems in other species, as well. Beyond the immediate impact of the biological modeling and the algorithms developed, the synergy between computational and biological systems is of great interest to computer scientists, biologists and the general public. The proposal includes an interdisciplinary collaboration between computer scientists, engineers and biologists. Students funded as part of this project will spend time at collaborators' labs from other disciplines leading to interdisciplinary training and the research will support and provide training opportunities for undergraduate and graduate students from underrepresented groups. The PI and co-PIs plan to develop and offer a new class on biologically inspired computational methods and to organize workshops and tutorials in relevant international meetings about the topic of this proposal. Project outcomes will be disseminated at http://www.algorithmsinnature.org.

Recent developments in nanotechnology and synthetic biology have enabled a new direction in biological engineering: synthesis of collective behaviors and spatio-temporal patterns in multi-cellular bacterial and mammalian systems. This will have a dramatic impact in such areas as amorphous computing, nano-fabrication, and, in particular, tissue engineering, where patterns can be used to differentiate stem cells into tissues and organs. While recent technologies such as tissue- and organoid on-a-chip have the potential to produce a paradigm shift in tissue engineering and drug development, the synthesis of user-specified, emergent behaviors in cell populations is a key step to unlock this potential and remains a challenging, unsolved problem.

This project brings together synthetic biology and micron-scale mobile robotics to define the basis of a next-generation cyber-physical system (CPS) called biological CPS (bioCPS). Synthetic gene circuits for decision making and local communication among the cells are automatically synthesized using a Bio-Design Automation (BDA) workflow. A Robot Assistant for Communication, Sensing, and Control in Cellular Networks (RA), which is designed and built as part of this project, is used to generate desired patterns in networks of engineered cells. In RA, the engineered cells interact with a set of micro-robots that implement control, sensing, and long-range communication strategies needed to achieve the desired global behavior. The micro-robots include both living and non-living matter (engineered cells attached to inorganic substrates that can be controlled using externally applied fields). This technology is applied to test the formation of various patterns in living cells.

The project has a rich education and outreach plan, which includes nationwide activities for CPS education of high-school students, lab tours and competitions for high-school and undergraduate students, workshops, seminars, and courses for graduate students, as well as specific initiatives for under-represented groups. Central to the project is the development of theory and computational tools that will significantly advance that state of the art in CPS at large. A novel, formal methods approach is proposed for synthesis of emergent, global behaviors in large collections of locally interacting agents. In particular, a new logic whose formulas can be efficiently learned from quad-tree representations of partitioned images is developed. The quantitative semantics of the logic maps the synthesis of local control and communication protocols to an optimization problem. The project contributes to the nascent area of temporal logic inference by developing a machine learning method to learn temporal logic classifiers from large amounts of data. Novel abstraction and verification techniques for stochastic dynamical systems are defined and used to verify the correctness of the gene circuits in the BDA workflow.

Nontechnical:

This award by the Biomaterials Program in the Division of Materials Research, co-funded by the Division of Chemical, Bioengineering, Environmental, and Transport Systems, to Massachusetts Institute of Technology is for the development of new class of sustainable materials that are grown using bacteria to have enhanced structure and properties. More specifically, this research program proposes to gain unprecedented control and enhancement of the multiscale design of a technologically important living material system, bacterial cellulose, which has great potential for use as textiles, drug delivery devices, tissue engineering scaffolds, and sustainable building components. This research will enable simultaneous tuning of the material (structure and properties) and macroscopic 3D shape of this biopolymer. An interdisciplinary approach will be taken involving synthetic biology and genetic engineering, in-situ extracellular and materials processing, algorithmic design methods from the field of architecture, as well as powerful new additive manufacturing fabrication (3D printing with micron-scale spatial resolution). This study combines three disciplines - synthetic biology, materials science, and architectural design and has a broader impact contribution for all three. For synthetic biology, foundational methodologies are created that could be extended to any biological polymer (e.g. protein block co-polymers, cellulose, amyloids, etc.). For architectural design, the opportunity to apply methods of algorithmic design and additive manufacturing to living matter is novel and opens up new questions about possibilities of design in interaction with biological growth and material formation to produce sustainable and environmentally responsive materials and building components from renewable resources. For materials science, the project suggests systematic study of combination of material structure, properties and morphometry as a way to design materials and further enhance their function and performance with specific functionalization through synthetic gene networks regulated by external stimuli. Participation in these projects will educate students to cross disciplinary boundaries and work across scales of resolution to develop sustainable design manufacturing techniques for microbial production. Additional educational activities for this study include Independent Activity Period (IAP) interdisciplinary class at MIT "Designing Shape, Material, and Life", instruction in the worldwide synthetic biology competition for undergraduate and high school students iGEM (International Genetic Engineering Machine), and science exhibitions, such as MIT Museum and Cambridge Science Fair. Lastly, mentoring of summer students via undergraduate research programs at MIT will be carried out.


Technical:

This research program proposes to control and tune the material (structure and properties) and macroscopic morphometry (3D shape) of a technologically important model system (bacterial cellulose), which has potential for use as textiles, drug delivery devices, tissue engineering scaffolds, and sustainable building components. An interdisciplinary approach is taken involving synthetic biology and genetic engineering, in-situ extracellular and materials processing, algorithmic design methods from the field of architecture, as well as powerful new additive manufacturing fabrication (3D printing with micron-scale spatial resolution). The first aim of this research is to modulate the structure and properties of cellulose as it is synthesized by the bacteria Gluconacetobacter xylinus via the use of UV lithography regulation and synthetic biological networks encoding fusion proteins production. Secondly, the role of the in situ extracellular physicochemical environmental conditions and perturbations on the structure and properties of bacterial cellulose will be investigated. The resulting macromolecular structure and properties of the produced cellulose will be assessed by cross-polarized optical, electron and atomic-force microscopy, X-ray diffraction, nuclear magnetic resonance, Fourier Transform Infrared Spectroscopy, multi-directional mechanical testing, and nanoindentation. Lastly, algorithmic design and 3D printing will be utilized to fabricate increasingly complex macroscopic structures with tunable geometric parameters of molds which are subsequently used to cast polydimethylsiloxane substrates for in situ culture and growth of genetically engineered bacterial cellulose.

Successful computing systems leverage their underlying technologies to solve problems humans simply cannot. Electronic systems harness the power of radio waves and electrons. Mechanical systems use physical force and physical interactions. Biological systems represent a computing paradigm that can harness evolution, adaptation, replication, self-repair, chemistry, and living organisms. Engineered, living biological systems which make decisions, process "data", record events, adapt to their environment, and communicate to one another will deliver exciting new solutions in bio-therapeutics, bio-materials, bio-energy, and bio-remediation. This project will create a quantitative set of freely available design principles, computational tools, mathematical models, physical biological artifacts, educational resources, and outreach activities. Once available, these resources will allow for novel, living biological solutions to be built more quickly, perform better, be more reliable to manufacture, and cost less to produce. This project is unique in that these resources will be explicitly developed to validate key computational concepts to understand how well these concepts can be applied rigorously and repeatedly to biology. This project decomposes these concepts into three areas: Computing Paradigm (digital, analog, memory, and communication), Computing Activity (specification, design, and verification), and Computing Metric (time, space, quality, and complexity). Once complete, this project will provide the most comprehensive, freely available, and computationally relevant set of building blocks to engineer biological systems to date.

By developing the tools, techniques, and materials outlined in this project, this research will fundamentally change the way biological systems are specified, designed, assembled, and tested. Advanced bio-energy, bio-sensing, bio-therapeutics, and bio-materials all will become increasingly viable commercial technologies that can be made better, cheaper, faster, and more safely as a result of this project. The education of an entire new generation of engineers will occur through workshops, coursework, and community engagement activities. This new generation will have access to these approaches which will influence how biological computation is done and how that process is communicated to the community. This project will bring computational questions and methods to the forefront of biotechnology via an interdisciplinary research team focused not on one-off solutions but on foundational computing principles.

Explicitly five unanswered questions will be addressed in this project: (1) What computational models are available to biology, what are their limits, and how do they perform? (2) What communication mechanisms are available to biology, what are their limits, and how do they perform? (3) What are the theoretical and empirical measures of quality, scale, time, and space in biological computing systems? (4) How generalizable are the concepts and "design rules" which can be learned from studying biological systems? (5) How can the results (data and learnings) from biological specification, design, and verification be authoritatively disseminated to the community as design principles and grand challenges? This project addresses these questions with an interdisciplinary team with expertise in theoretical computer science, electronic design automation, bio-physics/chemistry, control theory, and molecular cell biology. For more information visit www.programmingbiology.org.

? DESCRIPTION: Biology uses complex regulatory networks to sense and regulate cell state. Synthetic molecular circuits that can similarly control the timing and location of gene expression will have important applications in targeted disease therapy, cellular reprogramming, and beyond. However, a reliable, scalable, and general molecular technology for programmable gene expression has not yet been demonstrated. Here, we propose a paradigm shifting approach to this challenge: we will develop RNA strand displacement-based cellular bio computers. To date, strand displacement has primarily been demonstrated with DNA oligonucleotides in cell free settings. Strand displacement has been used effectively in cell-free DNA nanotechnology to build complex multi-input logic circuits, programmable nanostructures and molecular motors. Logic circuits made from hundreds of DNA oligonucleotides constitute the largest man-made molecular circuits built so far. In fact, there is currently no other engineering technology that supports de novo design of similarly complex, scalable and modular molecular circuitry, making this approach an intriguing candidate for performing biological computation in cells. Here we plan to bring strand displacement circuits to the cellular environment through the use of RNA instead of DNA, including sensors for endogenous RNA and RNA-based gene regulation. By foregoing the use of transcription factors and promoter regulation orthogonal with we can rapidly build more sophisticated circuits, cellular processes , which can be delivered more easily. We estimate that encoding of strand displacement circuits can be up to 10-fold more compact than genetic encoding of an equivalent circuit using transcriptional regulation. DNA serves as the information-storage medium, while transcribed RNA acts as the information- processing medium. Our RNA parts are engineered to interact with the cell milieu through specialized sensing and actuation components. We will demonstrate that, in principle, any endogenous cellular mRNA or miRNA can be an input, and that output gene expression can be regulated through RNA-RNA interactions. We will construct multi-input sensory circuits that provide high content information about cell state, and apply this for understanding biomarker levels and correlations for an in vitro and an in vivo 4T1 mouse breast cancer model. Importantly, our ability to encode highly sophisticated genetic programs with a much smaller DNA footprint will allow us to overcome current in vivo delivery limitations of complex circuitry. We initially focus on breast cancer as a model system but our technology can readily be adapted to other biomarkers, cancer types, and disease models. In fact, we believe that this adaptability, grounded in a rational design approach, is the key strength of the proposed technology. We expect that our technology will become relevant for many other applications that require sensing, analysis and control of cell state, including diagnostics and imaging applications, understanding of disease models, or programmed control of multi-stage differentiation.

? DESCRIPTION: Data from patients in diverse cancer types show that increased immune cell infiltration of tumors correlates with improved patient prognosis. It is also clear that a host of immunosuppressive signals are produced by tumors in order to block immune attack, and thus the endogenous immune response is in most cases unable to eliminate established tumors. Recently, efforts to alter the suppressive signaling in tumors through checkpoint blockade drugs that block individual suppressive signaling receptors on T-cells have shown promising clinical results, demonstrating tumor regression through shifting of the tumor microenvironment toward a pro-immunity state. However, it is likely that blocking individual signaling pathways will be insufficient for cancer immunotherapy to reach its full potential, because tumors create a complex network of suppressive signals. Here we propose an approach based on integration of synthetic biology and systems biology to reprogram the suppressive microenvironment in tumors through delivery of sophisticated multi-step genetic circuits to cells in the tumor microenvironment. This strategy will be enabled by the development of self-replicating RNA-based circuits that can (i) identify cell types specifically in the tumor through miRNA expression profiles, (ii) utilize small molecule-regulated induction of new gene expression programs that act in trans on surrounding cells in the tumor, and (iii) alter the function of identified cells in a coordinated fashion to tip the balance within the tumor from immune suppression to immune-mediated tumor destruction. Our specific aims are: (1) We will design a platform for in vivo RNA-based synthetic biology comprising RNA-binding regulatory proteins and small molecule induced protein degradation domains; (2) We will profile the miRNA signatures of 4T1 breast cancer cells and B16F10 melanoma cancer cells and create RNA-encoded multi-input classifier circuits that permit replicon expression only in target cell types, and (3) We will build a series f increasingly sophisticated programmed cancer therapies where cytokine secretion and necroptotic gene expression is restricted to tumor cells and can be precisely regulated by FDA approved small molecules, along with safety switch mechanisms to eliminate replicon-encoded circuits in healthy cells. We will deliver our therapeutic circuits into mouse tumors in vivo and monitor anticancer immune responses, using systems biology principles to analyze the resulting response of multi-cell networks in the tumor. A key goal of these studies will be to design RNA circuits which drive transfected tumor/immune cells to act in trans on surrounding cells in the microenvironment, to achieve a tumor-wide change in the tumor milieu without the need for successful circuit delivery to every cell in the tumor. Results from this project will provide a ne RNA-based toolkit for engineering mammalian cell function, demonstrate the utility of these approaches in vivo, and provide a framework for reprogramming tumors that overcomes limitations of traditional chemotherapy and targeted drug treatments.

Synthetic genetic feedback controller circuits to reprogram cell fate PI: Domitilla Del Vecchio1;4 co-PIs: James J. Collins2;4;5;6, Thorsten Schlaeger7, and Ron Weiss2;3;4 1Department of Mechanical Engineering, MIT; 2Department of Biological Engineering, MIT 3Department of Electrical Engineering and Computer Science, MIT 4Synthetic Biology Center, MIT; 5Broad Institute of MIT & Harvard; 6The Wyss Institute 7 Stem Cell Transplantation Program, Boston Children's Hospital PROJECT SUMMARY The past decade has seen monumental discoveries in the stem cell ?eld, with demonstrations that the fate of a terminally differentiated cell, contrary to what was traditionally believed, could be reverted back to pluripotency or directly converted to other differentiated cell types. All of a sudden, new approaches to regenerative medicine seem within reach: lost or damaged cells could be replaced by patient-speci?c reprogrammed cells, thus providing on- demand, compatible, high-quality cells of any required type. To meet this vision, the scienti?c community has made tremendous efforts toward establishing robust and ef?cient protocols for cell fate reprogramming. These protocols are largely based on a priori ?xed (pre?xed) ectopic overexpression of suitable transcription factors (TFs), with the rationale that this overexpression could trigger transitions among the states of the gene regulatory networks (GRNs) that take part in cell fate determination. Yet, despite a decade of remarkable progress, the ef?ciency of these protocols remains low, the quality of produced cells is often unsatisfactory, and many potentially useful direct cell fate conversions still seem impossible. These issues pose a formidable obstacle to the practical use of both human induced pluripotent stem cells (hiPSCs) and transdifferentiated cells in regenerative medicine. Arguably, our ability to accurately and precisely steer the concentrations of GRNs' TFs within desired ranges is critical to the success of cell fate reprogramming. Unfortunately, current protocols based on pre?xed TFs' overexpression have not demonstrated this critical ability. To address this problem, we propose a completely new approach to cell fate reprogramming in this project: we replace pre?xed overexpression with feedback overexpression of TFs, which we realize with an in vivo synthetic genetic feedback controller circuit. Within this circuit, the overexpression level is not a priori ?xed and is adjusted based on the discrepancy between desired and actual TF's concentrations. It therefore can accurately and precisely control TFs' concentrations to desired values, independent of the endogenous GRN that also regulates these TFs. Our research plan focuses ?rst on hiPSC reprogramming as a test-bed for evaluating the bene?t of our approach and second on directed differentiation of hiPSCs into platelets as a directly clinically relevant application. Speci?cally, in AIM 1, we propose to systematically investigate the ef?cacy of pre?xed overexpression of pluripotency TFs for hiPSC reprogramming. In AIM 2, we propose to construct and test the synthetic genetic feedback controller circuits that implement feedback overexpression of a number of TFs concurrently. In AIM 3, we will leverage the synthetic genetic feedback controller circuits for human hiPSC reprogramming and for directed differentiation of hiPSCs into platelets. This project will result in substantially higher reprogramming ef?ciencies, in cell products that more closely resemble the target cell type, and in the future, in cell conversions that today seem not possible. More broadly, our synthetic genetic feedback controllers will empower scientists and practitioners with a new tool to accurately control the TFs' concentrations of any endogenous GRNs and, in particular, of those GRNs involved in cell fate determination. 1

Understanding the physiological state of normal and cancerous tumor cells has limited our understanding of how cells grow and respond to their environment. In this collaborative project the PIs will construct synthetic living biosensors that can be mixed with other cells to provide readily detectable and measurable information on the cells physiological state, including the response of tumor cells to therapeutic agents. This will improve our ability to model normal and cancer cell biology, contributing to our understanding of how potential therapeutic agents affect cells. This information will be provided in a manner for detecting multiple physiological states simultaneously, in a high-throughput manner. In terms of broader impacts this project will enhance ongoing outreach activities to engage undergraduate students, K-12 students, and the public in STEM via several ongoing programs at each institution as well as by new activities to enhance interactions between institutions.

This project will develop synthetic biology tools and experimental approaches that will enable the development of more sophisticated models of normal and cancer cell physiological properties, including information about the immediate environment and response to therapeutic agents. The first step will be to develop genetically encoded live cell sensors that will report information about multiple cell physiological states, including treatment biomarkers. The second step will be to develop real-time reporting by the sensors via loading of barcoded synthetic RNA molecules distinct for each biomarker to be detected in a cost-effective, high-throughput platform. The goal of this EAGER is to develop and validate each of the key technological foundations required to be successful. In terms of broader impacts, this project will contribute to the training of the next generation work-force in synthetic, cellular, and cancer biology. This project will also include cross-institutional virtual meetings to enhance student networking by the iGEM teams at each institution.

We propose a new platform that leverages synthetic biology genetic circuits and micro/mesofluidic instrumentation to rapidly advance the field of organoids. Specifically, we will genetically engineer self- organizing tissues from human pluripotent stem cells into co-developed liver and pancreas organoids possessing vascularization and other mature properties, such as adult level albumin production. The application of synthetic biology to organoid development (programmable organoids) provides an exciting new opportunity for engineering and testing of organoids encoding live cell sensors of cell state and for embedding circuits that express cell-type and cell-state specific transcription factors. We will engineer novel microfluidic and mesofluidic platforms to enable low cost and high throughput development and testing of programmable organoids. Our hypothesis is that co-development of hiPSC-derived liver and pancreas provides cell-cell interactions that contribute to vascularization and other important elements in organoid development that will lead to mature organ formation. To test our hypothesis, we will genetically encode live-cell sensors to monitor liver organoid develop, co-develop liver and pancreas organoids, and create genetic circuits that lead to mature organoid formation. We will use synthetic classifier genetic circuits that evaluate changes in cell state in real time, and generate relevant protein outputs for driving differentiation in a cell-type specific manner. This these circuits will enable autonomous generation of new and improved versions of organoids, including mature liver organoids and co-developed liver/pancreas organoids. Determining the precise spatiotemporal nature of cell state transitions and the relevant transcription factors to drive differentiation is not only essential for creating new and effective organoid developmental programs, but will also provide important scientific insights to understanding the fine aspects of differentiation and co-development. Successful achievement of our aims will have a broad impact in the areas of gene therapy, drug testing, and personalized medicine. For example, the ability to co-develop matched organoid systems will enable patient-specific drug development (e.g. for cancer therapy) that is more accurate than expensive and controversial alternatives, such as the use of humanized mice. This work will also support the long-term goal of producing mature organoids and organ systems suitable for transplantation.

Biological circuits control the way in which cells sense and respond to their environment, from microbes to mammals. Despite the fact that these circuits share and trade common cellular resources, they are surprisingly able to maintain separate (highly decoupled) functionalities. How can biological circuits be connected yet be decoupled? This project seeks to address this puzzling question. This research will improve our current understanding of natural systems and help create new biological circuits that control cellular behavior for energy, environment, and medical applications. Currently, human-engineered biological circuits are unpredictable and not sufficiently reliable for practical use. The biological discoveries of this project may serve to develop engineering solutions that decouple synthetic biological circuits from each other for predictable and reliable behavior. The research conducted under this project requires synergy between theory and experiments and between biology and engineering. As such, a new generation of interdisciplinary researchers will be trained, with cross-disciplinary expertise. This project will develop new educational curricula that cross department boundaries. The researchers will organize workshops and invited sessions at national and international conferences on the problems addressed in this project and will present the research at the Cambridge Science Festival and at its satellite event "Science on the Street". Teaching materials will be further disseminated to the broader community through MIT's OpenCourseWare and edX.

Modularity dictates that the input/output behavior of a system is practically independent of its context, thus allowing a bottom-up compositional approach to predict the behavior of complex systems. Today, a key challenge when creating biological circuits is that the input/output properties of a module changes unpredictably when the module is in a different context. While many elements contribute to dependence of modules on context, sharing limited cellular resources such as those required for gene expression remains a major unresolved cause of lack of modularity. This project will elucidate general rules to predict the emergent behavior of a circuit from the composition of intended regulatory links and indirect retroactivity arising from resource sharing. This will lead to formulate the breakdown of modularity as the control-theoretic problem of attenuating indirect retroactivity, which will be addressed with a decentralized feedback control solution inspired from nature. The experimental implementation of this solution will elucidate how natural feedback motifs keep homeostasis while working in orchestration and may substantially enhance our ability to create predictable systems in synthetic biology.

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.

PI: Irvine, Darrell J. Project Summary/Abstract: Strategies to promote the magnitude and quality of T cell and antibody responses following immunization have broad relevance for the development of new prophylactic and therapeutic vaccines for the treatment of cancer and infectious diseases. Recent studies, including work from our own laboratories, have demonstrated that the kinetic pattern of antigen and adjuvant exposure to lymphoid tissues has a substantial impact on the immune response to vaccination. However, active control over the temporal pattern of antigen/inflammatory cue delivery to lymph nodes is lacking in all current vaccine approaches. Here we propose an approach applying methods from synthetic biology to create nucleic acid-based vaccines where vaccine antigen/adjuvant expression dynamics can be controlled by (i) exogenous regulation by orally-available FDA-approved small molecule drugs or (ii) intrinsically programmed in genetic circuits carried by the RNA. Based on the promising features of RNA-based vaccines, in preliminary studies we established a lipid nanoparticle-delivered self- replicating alphavirus replicon RNA as the platform for these regulated vaccines. We will systematically study the impact of vaccine antigen and adjuvant kinetics on the immune response to vaccination, create pre- programmed vaccine kinetic patterns, and test the capacity of regulated replicons to enable single-shot vaccines with prime and boost controlled by an orally-available small molecule drug. Our specific aims are (1) To optimize small molecule-regulated expression of antigen and molecular adjuvants from RNA replicons, (2) To use the regulated replicon platform to define optimal kinetics of antigen and adjuvant expression during vaccination, (3) To design RNA-based replicon genetic circuits with pre-programmed temporal patterns, and (4) To determine factors limiting replicon expression lifetimes in vivo, and engineer strategies to prolong expression toward the goal of small molecule-regulated prime-boost regimens. These studies will lead to fundamental discoveries in basic immunology, provide a framework for rationally designing immunization regimens, and create technologies to practically implement them.

People with diseased or defective vital organs often need organ replacement to survive, but the availability of replacement organs is severely restricted by shortages of suitable tissue-matched donors and complexities such as postmortem organ deterioration and immunological rejection. These problems could be overcome by using high fidelity artificially-grown organs, but achieving that goal faces daunting and long-standing scientific and engineering challenges that this project aims to begin to meet. The project will focus on proof-of-concept generation of microscale patterns in a liver organoid to mimic the anatomical structure of lobules arranged in hexagonal patterns. The researchers will use microrobots to dynamically regulate gene expression in 3D vascularized liver organoids to generate the lobule like patterns. The results of this project will define a new area of robot-assisted biological design. This research will result in new biological rules, synthetic biology tools, and microrobotics that can be applied in numerous disciplines. If successful, another broader impact will be the demonstration of a method that could be used to create a new, in vitro, native-like organoid for biological and medical research, opening the door for research into the creation and repair of synthetic human organs. The project includes research training for graduate students and postdoctoral researchers.

Conventional methods of reproducing biological patterns in vitro suffer from multiple limitations. Previous research on pattern formation has largely relied on delivering global stimuli and studying reaction-diffusion mediated patterning of cell fates in the cell culture. Such methods yield only static patterns and give neither precise spatial nor temporal control over gene expression and resulting biological tissue formation. Current tissue engineering capabilities such as 3D printing and optogenetics are also unable to recapitulate the multiscale self-assembled patterns evident in native-like organs. The proposed approach will enable precise control of microrobots to achieve dynamic control over patterning in 3D biological systems, creating a paradigm shift in the field. The proof-of-concept goal is to modulate localized gene expression in engineered 3D tissue constructs to control the emergence of multiscale patterns. Machine learning will be used to derive and characterize desired multiscale patterns, synthetic biology to endow the stem cells with genetic circuits that can differentiate the cells to form desired tissue constructs, and microrobots to alter localized gene expression to form multiscale patterns in tissue constructs. In particular, the researchers will develop and control microrobots capable of sustaining and carrying engineered sender cells, drive the microrobots and associated sender cells within a vascularized 3D liver organoid to specific locations, and use the microrobot controlled sender cells to communicate with endothelial cells, inducing these endothelial cells to secrete Wnt and generate gradients controlling liver lobule zonation. This patterned lobule zonation will regulate the metabolic activity of the liver organoids.

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.

The different cell types that make up our body, such as skin cells or blood cells, have the ability to maintain distinct identities for the lifetime of an individual even if they all have the same DNA. How is memory of cell?s identity safeguarded for an individual?s lifetime? This project uncovers key molecular interaction circuits that enable the same DNA sequence to give rise to distinct and concurrent long-term cellular identities. This knowledge is valuable to understand diseases linked to loss of cell identity, such as cancer, to educate new approaches to stem cell reprogramming, and, ultimately, to program human cells for cell therapy. This project involves creating and analyzing mathematical models of molecular circuits that alter DNA compaction. These mathematical models enrich current educational curricula in quantitative molecular biology, systems biology, and synthetic biology. Graduate students receive a highly interdisciplinary training grounded on molecular biology, mathematical modeling, and mathematical theory of stochastic processes. Education aspects of the project impact K-12 and undergraduate students, members of underrepresented groups in science and women in mathematics through outreach and curriculum development activities.

The objective of this project is to uncover fundamental principles by which chromatin modification circuits mediate epigenetic cell memory (ECM). ECM is the ability of cells to maintain distinct cell-type-specific gene expression patterns through subsequent cell divisions without a change in genetic sequence. The key hypothesis of this project is that synergistic positive feedback loops within chromatin modification circuits govern ECM in combination with time scale separation between epigenetic erasure and read-write processes. To validate this hypothesis, this project follows a ?build-to-understand? approach driven by rigorous mathematical analysis of the (quasi)-stationary probability distribution of new multi-time scale stochastic processes. These processes naturally arise from the dynamics of chromatin modification circuits and take the form of small perturbations of non-ergodic processes, that capture time scale separation. The project has three aims. Aim 1 and Aim 2 focus on a single gene?s chromatin modification circuit, identifies biochemical parameters that control time scale separation, and establish an experimental model system to tune them. Aim 2 is focused on the extent to which DNA methylation biases ECM towards a repressed chromatin state and proposes a positive autoregulation mechanism to enhance ECM of an active chromatin state. Aim 3 investigates how, by wiring multiple genes? chromatin modification circuits together, a long-term memory of arbitrary gene expression patterns could be created. As a proof of principle, Aim 3 performs experiments on an epigenetic toggle switch test-bed, a motif highly represented in gene regulatory networks involved in cell fate determination, in which two gene mutually repress each other.

This project is co-funded by the Systems and Synthetic Biology and Genetic Mechanisms clusters in the Division of Molecular and Cellular Biosciences, the Mathematical Biology program in the Division of Mathematics and the Cellular and Biochemical Engineering program in the Division of Chemical, Bioengineering, Environmental and Transport Systems.

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.

COVID-19 necessitates new approaches to artificial immunity for people at-risk, recently exposed, or in early stages of viral infection, for which there are limited treatment options. Rapid onset of lung inflammation caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the respiratory illness responsible for the coronavirus pandemic, can in principle be overcome with immunomodulatory gene therapy, but achieving this goal faces long-standing challenges. Further, there is a need for anti-viral therapies across other viral families such as H1N1 (flu), West Nile, Zika, Yellow Fever Virus, and emerging variants. To address these challenges, this project will create Viral First Responder Cells (VFRCs), a new type of sentinel/therapeutic cell. VFRCs are genetically engineered patient cells programmed to mount a first line defense against highly contagious viral diseases with long incubation periods, vector-borne diseases, and future viral diseases. Upon viral detection, VFRCs produce a cocktail of outputs to stop viral replication and activate an appropriately modulated immune response. The project’s scientific output will focus on equitable distribution (e.g. genetically diverse target responses) and will broadly disseminate research-focused training material in novel media, including new grant-specific virtual lab training modules for remote and at-home learning. The project will provide inclusive viral therapy and COVID-related learning opportunities for underrepresented minority students, integrate with MIT’s efforts to address systemic racism, and increase retention of women and minorities via an outreach portal.

This project will create a new synthetic immune system to overcome limitations in traditional artificial immunity (e.g. immunization or antibody therapy) by genetically engineering patient cells (e.g. a small subset of lung epithelial cells) with broad-range and virus specific sensors of infection (VFRC sentinels). VFRCs rapidly detect viral entry using precise multi-input sensors that monitor for changes to cellular transcriptional signatures and genetic logic that responds by activating both pathways that lead to eradication of the virus, including appropriate activation of innate and adaptive host immune responses. Creating the capability to both sense immune state changes / viral entry and trigger powerful immunomodulatory responses in a controlled fashion will define a radically new approach for anti-viral therapies, define a new area of immunomodulatory biological design, and lead to new therapies for emergent viral threats. VFRC circuits limit side effects to cells already compromised by the virus and provide a more effective and tuned response based on the both the stage of infection and the degree of patient immunocompetence. An important element of VFRC safety is restricting intracellular response to occur only during active infection. When the virus is not present, genetic circuits introduced into the VFRCs remain in a vigilant but inactive “monitor” state. In this state, VFRCs monitor for signs of infection but do not alter the transcriptome. VFRCs also include a physician regulated genetic safety switch to deactivate any undesired response. Once proven safe and effective for one virus, this approach, which already includes broad viral entry sensors, can be customized for new viruses. This research will also answer fundamental scientific questions about viral immune responses and immunomodulatory therapy optimization.

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.

Major advancements in stem cell biology have paved the way for innovation in organoid engineering. Organoids are 3D tissues derived from human induced pluripotent stem cells (hiPSCs) generated by reprogramming patient- speci?c adult cells, such as ?broblasts. While organoids show great promise as testbeds for investigating devel- opmental biology, current methods for organoid production are limited by their reliance on external inputs, such as growth factors and small molecules, which affect cells imprecisely and give rise to immature organoids that do not faithfully recapitulate in vivo physiology and functionality. The resulting organoids are size-constrained, lim- ited to a small set of cell types, and do not generally develop mature tissue that exhibits the functionality of fully developed organs. While we have previously demonstrated genetic programs that enable organoids to generate all requisite cell types in liver, variability in cell ratios remains an open challenge for achieving reproducible, high quality organoids. Further, progress is blocked by the inability to reliably guide multi-lineage speci?cation, the lack of precise timing of multistep differentiation, and the inability to make robust bifurcation decisions that determine the ratios of the resulting cell types. To overcome these obstacles, we will combine synthetic biology, developmental biology, and control theory to design novel open and closed loop genetic controllers that individually guide differentiation from within each cell to form unique new 3D tissue: vascularized pancreatic organoids with de?ned ratios of endocrine and exocrine cells. We will demonstrate how these new organoids can serve as more sophisticated and comprehensive models for investigating developmental biology principles. This work will spearhead a transformation in organoid synthesis by shifting the ?eld from manual addition of inductive chemical signals to cell type conditional, self-timed ectopic expression of transcription factors that induce differentiation. Building upon the premise that 1) gene sensors can detect cell types speci?c to differentiation stages, and 2) at least in certain important cases, regulated expression of lineage-specifying transcription factors can guide differentiation to the next stage, our main hypothesis is that feedback regulation of cell lineage bifurcation decisions can lead to more robust and reproducible sub-population ratios in organoids in comparison to open loop approaches. Our organoids will contain synthetic developmental programs that are self-timed and globally-orchestrated, with cells working together to generate the requisite ratios. We will create a platform for programmed bifurcation decisions that can be used for other differentiation steps in the pancreas, and more broadly to other organoid and tissue types. We will use this platform to perform novel developmental studies to systematically vary the ratio of endocrine to exocrine cells and measure the consequences on exocrine/endocrine cells and their differentiation and function. The ability to precisely vary the ratio while studying gene expression pro?les, the organoid secretome, and its affects on target cells will provide invaluable bene?t in the investigation of pancreas development and dysfunction.

SpongeBot represents a new class of genetically modified cells to address the COVID-19 pandemic, leveraging a novel antiviral platform developed under the proposer team?s existing NIH NIBIB R01 project. This antiviral platform facilitates rapid, targeted SpongeBot development and deployment against SARS-CoV-2, its viral mutations, as well as entirely new viruses - providing a barrier to future viral pandemics. SARS-CoV-2 is highly communicable and individuals can transmit the disease even prior to becoming symptomatic, sharply increasing the rate of disease spread. During the first two weeks following infection, the innate immune system attempts to slow down the rapidly multiplying pathogen to provide time for the adaptive immune system to develop more specific and effective mechanisms to destroy the virus. However, in individuals with decreased or compromised immune responses, the excessive viral load can lead to elevated inflammation, severe tissue damage, and ultimately death. SpongeBot, our bioengineered cell-based therapy solution, provides vital support to the body?s immune system, through its genetically designed ability to sequester and destroy SARS-CoV-2 viral particles at sites of injury, in addition to attenuation of the immune system?s hyperinflammatory response to the virus. Administering SpongeBot cells to an infected individual reduces and keeps viral load below dangerous thresholds, prevents harmful hyperinflammation, and provides the adaptive immune system the time required to mount an effective defense against the virus. SpongeBot can be administered prophylactically to at-risk populations (e.g., healthcare workers, the elderly, or immunocompromised individuals), or therapeutically at any stage during the course of viral infection. Importantly, SpongeBot therapy is extremely safe; the base technology has a long proven clinical safety track record. Unlike the lengthy development times necessary for vaccines or antiviral medications, a targeted SpongeBot therapy against a predicted virus can be placed in clinical trials immediately. SpongeBot development for a novel virus would be ready for deployment in only about 12 weeks.

Project Summary Human induced pluripotent stem cell (hiPSC)-derived organoids hold great promise for tissue engineering and personalized drug screening, but obtaining the desired multicellular organization and function from these systems is usually performed in an ad hoc fashion without forward design specification. Recently, we reported successful liver bud formation containing stromal cells, vascular tube-like structures and hematopoiesis-like processes by synthetically inducing diversity in GATA6 expression from a single hiPSC population. This accomplishment suggests that expanding circuit logic operations to artificially control differentiation drivers at particular bifurcations in lineage specification could profoundly impact the complexity and functionality of organoids. In this project, we bring together mathematical modeling, machine learning, optimization, and innovative synthetic biology techniques to elucidate and design fundamental decision and communication rules for guiding cells into complex, heterogeneous tissues. Our overarching hypothesis is that appropriate timing and predictable stochastic control of the expression of intracellular and extracellular factors is critical for redirecting lineage choices in order to elicit desired multicellular organization from a population of differentiating cells. We will develop synthetic tools for sensing differentiation stages of iPSC-derived organoids and construct and characterize a stochastic commitment switch in an inducible reporter system. These tools will be integrated in synthetic gene circuits for engineering emergent multicellular organization through stochastic temporal control of developmental factors. The modular commitment switches developed in this project will be capable of exploring how the degree of subpopulation biasing of cell fate decisions and level of cell fate synchronization at bipotent differentiation stages impacts self-assembly and emergent multicellular organization of an organoid. Our aims - executed through a closed loop of computational and experimental investigations - will shed insight on how generalizable methods of controlled manipulation can elicit desired organoid-level emergent properties.

This project aims at defining a new area of dynamically-controlled, robot-assisted biological design. A convergent research team consisting of experts in microrobotics, machine learning, and synthetic biology will focus on developing a radically new approach towards analyzing and replicating intricate cellular patterning in mammalian tissues. Not only will this research result in new biological rules, synthetic biology tools, and microrobotics that can be applied in numerous disciplines, it will also create a new in vitro native-like liver organoid for biological and medical research. The work will open the door for research into the creation and repair of other synthetic human organs.<br/><br/><br/>The research team will generate multiscale, multicellular patterns and synthesize 3D patterns with vasculature to produce native-like organs. To enable mapping of pattern design problems to optimization problems, the researchers will define novel machine learning techniques to classify and quantify cellular patterns. To enable control of bio-compatible robots that guide cell organization and differentiation processes, they plan to develop novel technologies that leverage and far exceed current microrobotics. This will enable insights into fundamental scientific questions about cell differentiation and connections between spatial organization and function.<br/><br/>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.