Project Description

The discovery of new treatments - microbial therapies, taking the biomedical industry by storm

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The academic study of Synthetic Biology is often referenced as the field responsible for reengineering new solutions from known biological processes. From Synthetic Biology, the world has come to know influential therapeutic discoveries like personalized CAR-T therapy and designing host bacteria to produce insulin for diabetic patients. The foundation of these methods stem from the ability to engineer biological systems to produce therapeutic molecules, such as proteins, antibodies, or even small molecules, with precision and efficiency.

Microorganisms and microbiomes are emerging as a novel and promising form of therapy, heralding a new era in healthcare. The human body hosts a diverse community of microorganisms that collectively form the microbiome, which plays a crucial role in maintaining health. Harnessing these microbial communities, researchers are exploring therapies like fecal microbiota transplantation (FMT) to treat conditions such as Clostridium difficile infections by restoring a healthy gut microbiome. Microbes can also be engineered to produce therapeutic compounds, like insulin or enzymes for metabolic disorders, offering a sustainable and efficient source of medication. Additionally, microbiome-based interventions are being investigated for conditions ranging from inflammatory bowel disease to mental health disorders, demonstrating the profound impact of our microbial inhabitants on overall well-being. This innovative approach has the potential to revolutionize medicine by providing natural, personalized, and often safer treatments that target the root causes of various diseases, offering hope for improved healthcare outcomes.

Testing new therapeutic pathways

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In 2022, the successful funding notification of the NSF proposal entitled, “NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr)” validated the field’s new found interest in better understanding microbial communities and their potential for groundbreaking therapeutics. Together, the researchers responsible for this proposal aim to “create diagnostic tools and develop microbiome engineering approaches to monitor and operate built environments that maximize human health protection” (1).

While the excitement for studying this vast new set of therapeutic potential is evident, the challenge lies in the efficiency/speed of testing the copious possibilities and combinations of microbial pathways. As stated in the now funded NSF proposal, “our capacity to engineer microbiomes requires a fundamental understanding of concepts of community ecology and an ability to track, control, and model those interactions” (1). In this characterization of functionality lies the potential for a plethora of new therapeutics.

The challenge: It is also known that there are over 100 trillion microbial cells to be characterized in just the human gut microbiome alone (2). The clear rate limiting step in making these therapeutic discoveries is the ability to test and identify these microbial communication pathways in an efficient, cost effective manner. The solution lies in APUS.

Figure 1. The foundational outline from the NSF-funded PreMiEr research proposal.

Currently, the process for characterizing these microbiomes is a tedious, laborious process done on the laboratory bench. Companies like mbiomics have attempted to market a solution to this problem by serving as a “characterizing factory” where interested clients can submit microbiome samples for detailed characterization analysis. However, this method removes the autonomy from the researcher, relying on outside scientists’ manpower to process their data. Additionally, the services provided by mbiomics only identify each microbe in a silo, without the ability to receive results regarding microbial interactions and the effects microbes may be able to have in a pair or group.

Figure 2. The services and technology tools provided by mbiomics company.

APUS will serve as a cost and time effective hardware tool that allows scientists to test their own data in house through open source software and hardware replication instructions. Uniquely, APUS will also allow scientists to test and thus better understand complex interactions within microbial communities.

Creating a reliable and accurate study environment

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As the discovery of these new microbial therapies came to light, we sought to fill the gap to test these therapies in a manner that would be most transferable to human applications. As we began to hypothesize how scientists would be able to best test these large sets of microbial therapies, we gained inspiration from Organ On a Chip methodology. Uniquely, Organ-on-a-chip models offer a groundbreaking approach to advancing our understanding of the human system. These microfluidic devices replicate the structure and function of specific organs, providing a range of benefits for biomedical research and drug development. First, they allow for more accurate and physiologically relevant modeling of human tissues and organs, reducing the reliance on animal testing and offering insights into human-specific responses to drugs and diseases. Second, organ-on-a-chip models enable real-time monitoring and control of cellular behavior and environment specifications, facilitating the study of dynamic processes and interactions within the body. Additionally, they offer a platform for high-throughput screening of potential therapeutics, accelerating drug discovery and personalized medicine.

The APUS model mirrors organ on a chip methodology in three ways:

We believe that the development of APUS hardware technology will help progress the discovery of microbial therapies in a manner parallel to the success of Organ on a chip models has brought to drug development.

Ultimately, the APUS hardware is a one of its kind technology that allows for live multiplexing and routing of signaling molecules to programmatically interconnect multiple combinations of species mimicking a microbial community. This community replication will provide the foundation for scientists to study microbes and their endless therapeutic potential in a way like never before.

Additional inspiration - Parallels to optimizing computer architecture

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When we first began thinking about how to support the biotechnology industry, we couldn’t help but be reminded of the beginning of computer invention and the progress made in this field in the mid-20th century. The traditional method of having to manually build logic circuits out of transistors after designing them was not a fast enough process for the rate at which the complexity of computation was increasing. The natural solution was to create platforms for electrical and computer engineers to prototype their hardware designs, hence the invention of the Field Programmable Gate Array (FPGA) for hardware design, Read Only Memory (ROM) and Flash memory for software design with microcontrollers, and standardized architecture for central processing units (CPUs). In the same way that computer engineers struggled to make efficient progress before platforms for hardware and software design were created, synthetic biologists today struggle in finding an efficient method for testing their biomechanisms. Inspired by the platforms created to help computer engineers, our platform will allow synthetic biologists to design novel synthetic biological mechanisms more efficiently rather than conducting manual, large-scale experiments for each iteration. Our “bio-computation” platform is meant to serve as a precursor to scaled up “bio-factories” for any process that can be achieved through synthetic biology. For example, if a bacterial consortium were designed to efficiently produce some sort of chemical or drug, the system would be able to be prototyped using our platform. Once the system works on our platform, the scientists could move on to the next stage: upscaling. Similarly to how computer engineers could design a novel hardware system on an FPGA much more efficiently than soldering together individual logic gates, synthetic biologists could design a novel synthetic biological mechanism with our platform much more efficiently than doing each experiment by hand.

Our project

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To combat the problems of nutrient competition and ineffcient experimentation processes, our project aims to develop a platform for quickly screening and discovering synthetic microbial communities that perform a particular biological task. By overcoming such obstacles, the project paves the way for more rapid and cost-effective development of novel pathways and synthetic organisms. Start-ups can more quickly deliver results and are more likely to break into the chemical processing industry.

The platform will house different microbial species in individual PDMS biochips that maintain cells with complementary roles at their log (exponential growth) phase while providing a constant supply of media and removal of cellular excess. To facilitate communication among biochips, we have designed a multi-layer motherboard consisting of a control, silicone, and flow layer.

Importantly, the architecture of the biochips within the motherboard allows efficient interspecies communication without any detrimental interaction. A dual-syringe pumping mechanism will control the flow of media to the multilayer microfluidic motherboard.

By having a continuous flow mechanism, the platform will only require a media refill once the entire supply is used up, and the experiment can last for days at a time. The flow within the chip is controlled by solenoids that the user can customize via an application that accompanies the solenoid mechanism.

We have implemented a “plug and play” design into our platform that allows companies to quickly optimize their biological pathway. Different biochips will have different organisms with varying levels of abundance. In this way, the user can replace organisms or increase/decrease their abundance and metabolite concentrations in the different sections of a metabolic pathway without having to alter the entire microfluidic device. Instead of having to spend the time and money to restart experiments and redo lengthy procedures, our platform can be used to change aspects of the pathway one at a time and screen options in a high-throughput manner.

Sources

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