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Floating Photovoltaics (FPVs): Impacts on Algal Growth in Reservoir Systems

By Benjamin Narwold, Environmental Science and Management major ’23

Author’s Note: I wrote this review paper to learn more about the environmental impacts of floating photovoltaics (FPVs) because this topic directly applies to my work as an undergraduate researcher position with the Global Ecology and Sustainability Lab at UC Davis. I wanted to focus specifically on the impacts of FPV on algae because of the biological implications of disturbing ecologically important photosynthesizers in reservoirs. I want readers to develop an understanding of FPVs as a climate change mitigation solution, how these systems may disturb algae, and the uncertainties in whether expected and observed changes in algae growth are beneficial or detrimental to the aquatic environment.

ABSTRACT

Floating photovoltaics (FPVs) are typical photovoltaics mounted on plastic pontoon floats and deployed on man-made water bodies. If FPVs are developed to cover 27% of the surface area of US reservoirs, they would provide 10% of the electricity in the US. Freshwater reservoirs are host to vulnerable ecosystems; therefore, understanding the water quality impacts of FPVs is necessary for sustainable development. This review aimed to fingerprint the impacts of FPVs on reservoir aquatic ecology in terms of algal growth and identify the uncertainties in FPV-induced algae reduction to present our current understanding of the environmental impacts of reservoir-based FPVs. The UC Davis Library database was searched for papers from peer-reviewed journals published from 2018 to 2022 that covered “floating photovoltaics”, “algae reduction”, and “environmental impacts”. A consistent result across studies was that FPVs reduce algal growth by reducing the sunlight entering the host waterbody, and this can disrupt phytoplankton dynamics and have cascading effects on the broader ecosystem. Modeling and experimental approaches found that 40% coverage of the reservoir by FPVs is optimal for energy production while maintaining the necessary algae levels to support the local ecosystem. The lack of research on the ideal percent coverage of FPVs to reduce algal growth but not disrupt ecosystem dynamics emphasizes the need for future research that addresses FPV disturbance of local microclimates, algae response to reduced sunlight, and the corresponding cascading impacts on other organisms dependent on the products of algal photosynthesis.

Keywords: floating photovoltaics, algae reduction, environmental impacts, water and ecology management, energy and water nexus

Caption: Floating photovoltaic (FPV) system in Altamonte Springs, Florida. One of four sites monitored by the Global Ecology and Sustainability Lab for water quality impacts of FPV.

INTRODUCTION

Climate change is a global problem of increasing intensity and poses challenges to food, water, and energy security. Global climate models predict a 2-4°C increase in global temperatures from now until 2100, which will degrade human health and threaten ecosystems [1]. Renewable energy is a critical component of reducing anthropogenic greenhouse gas emissions, and the widespread transition away from fossil fuels is becoming increasingly feasible with new technologies. One of these new renewable energy systems is floating photovoltaics (FPVs), standard photovoltaic (solar panel) modules mounted to a polyethylene pontoon float system, positioned off the water’s surface, and anchored to the bottom or shore of the host waterbody [2]. FPVs represent an intriguing and novel renewable energy solution because they can be deployed on human-constructed water bodies and improve land-use efficiency. Ground-mounted solar projects compete for land against agricultural and urbanization interests, whereas many artificial and semi-natural water bodies, such as wastewater discharge pools, have no conflicting human interests [3]. FPV development thus presents an opportunity to sustainably increase solar energy production without interfering with agricultural and urban development, which will continue to expand as world populations increase. In addition to optimizing land use, FPVs can produce up to 22% more power than conventional solar due to evaporative cooling [4]. The solar panels are located just above the water’s surface, so the local water evaporation contributes to a reduction in solar panel temperature, thus increasing efficiency. Generating electricity using FPVs is intended to augment solar power generation capacity and supply more renewable energy to the grid for households and industry.

Among the most abundantly available space to develop this pivotal land-use optimization and climate change mitigation solution are reservoirs, lakes formed from damming a river for water storage and hydropower production. A GIS analysis found that covering 27% of the surface area of reservoirs in the United States with FPVs would generate enough electricity to meet 9.6% (2116 Gigawatts) of the country’s 2016 energy demands [4]. But reservoirs and similar bodies of water nevertheless represent vulnerable freshwater ecosystems, so developing an understanding of the water quality and species impacts of FPVs represents the primary hurdle to informing sustainable development of these systems.

FPVs reduce the amount of sunlight reaching the surface of their host waterbody, which reduces the amount of evaporative water loss and results in significant changes to algae growth [5]. Several studies have found that FPVs alter phytoplankton dynamics and can have cascading effects on the other organisms in the ecosystem [6–8]. A key agent of uncertainty surrounding reservoir FPVs is determining the equilibrium range of algal growth needed to support reservoir food webs. In some reservoir systems, we see strong summertime algal blooms. An algal bloom is a rapid increase in or overaccumulation of an algal population that can result in oxygen-depleted waterbodies called “dead zones,” where the algae eventually die and decompose [9]. FPV-induced shading can counter harmful algal blooms, providing environmental benefits to augment renewable energy generation. Alternatively, in reservoirs that do not have problematic algal blooms, adding an FPV system may reduce healthy algal populations and cause adverse rippling effects to other species in the ecosystem. Developing an understanding of what percent of the total water surface area of the reservoir covered by FPVs is enough to reduce algal growth and bloom potential but not too large to disrupt ecosystem dynamics will require further research. Specifically, assessing the disturbance of local microclimates caused by FPVs, algae response to reduced sunlight conditions, and the impact on other aquatic species dependent on the ecosystem functioning provided by algae. Due to climate change, we predict an increase in temperature and shifting precipitation patterns; therefore, it is important to contextualize the water quality impacts of FPV and its influence on algae, given this variability.

Figure 1. Impact of FPVs on algal in reservoir ecosystems. FPV-induced shading can provide additional environmental benefits in reservoirs with algal blooms and may cause adverse effects in healthy reservoirs.

Methods

This review surveys what we know regarding the impacts of FPVs on algal growth in reservoir systems. The UC Davis Library database was searched for papers from peer-reviewed journals using the following keywords: “floating photovoltaics,” “algae reduction,” and “environmental impacts.” I looked at experiments on reservoir-based FPVs from 2018-2022 to analyze plot scale impacts on algal growth, quantified with chlorophyll-a monitoring data, and assessed global-scale changes in algal growth from a climate change perspective, with consideration of FPV materials and design. Although a study on crystalline solar cells incorporated in this review is from 2016 and falls outside the 5-year range of focus, it represents a necessary juxtaposition to the semitransparent polymer cell technology. Overall, I analyzed the methods and results of site-specific, laboratory, and global-scale studies to fingerprint the current state of knowledge on the impacts of FPVs on algae and algal blooms to inform reservoir management.

Algal Growth and FPV Coverage Scenarios

Algae are responsible for producing oxygen in the waterbody, and the impact of FPVs on algae growth depends on the percentage of the waterbody covered by the FPV and is measured by looking at chlorophyll-a (ch-a) differences. Ch-a, a pigment present in all photosynthetically active algae, is often used as a proxy measurement to assess algal growth dynamics within a waterbody [10]. Ch-a is measured using optical sensors and wavelengths of light, so it is an indirect measurement of algal concentration. FPVs reduce the amount of sunlight reaching the surface of their host waterbody and disrupt phytoplankton dynamics. Hass et al. (2020) and Wang et al. (2022) investigated different FPV coverage scenarios and used ch-a as a proxy for algal growth. Hass et al. used the ELCOM-CAED model to evaluate ten different FPV coverage scenarios, and Wang et al. simulated 40% coverage relative to 0% coverage control ponds using black polyethylene weaving nets as a proxy for an FPV array. Both the model output and experiment-based approach settled on 40% FPV coverage as an equilibrium development target [7, 11]. The results of these studies show continuity; however, Hass et al. did not consider the difference in absorption wavelength range for different microalgal taxa, and Wang et al. did not use actual solar panels in their experimental design. Additionally, Andini et al. (2022) investigated the difference in algae between 0% and 100% coverage at Mahoni Lake in Indonesia by experimenting with mesocosms, isolated systems that mimic real-world environmental conditions while allowing control for biological composition by taking samples at the same water depths. These researchers found that 100% FPV coverage reduced ch-a between 0 and 1.25 mg/L, average temperature between 0 and 2.5℃, dissolved oxygen between 0 and 1.5 mg/L, and electrical conductivity categorically in the waterbody. However, the researchers only considered directly measured water quality variables and did not assess the long-term trophic consequences of 100% FPV coverage [6]. Clearly, the study was designed to show the polarity between 0% and 100% coverage in terms of several water quality parameters; however, realistic intermediate FPV coverages incorporated into both Hass et al. and Wang et al. were absent from this study. Given these compiled results, future research can continue to work toward the broader question of determining what percent FPV coverage can be applied to a reservoir to maximize energy production and minimize environmental disturbance.

Algal Blooms and Mitigation Potential

Algal blooms are a product of high productivity conditions that favor rapid algae growth, and the shading provided by FPV systems could mitigate the intensity and negative impacts of summertime algal blooms. High productivity conditions include high water temperature, intense sunlight, and abundant nutrients such as nitrogen and phosphorus. The first two variables can be controlled by FPV coverage. In a study of the global change in phytoplankton blooms since the 1980s, Ho et al. (2019) found that most of the 71 large lakes sampled saw an increase in peak summertime bloom intensity over the past three decades, and the lakes that showed improvement in bloom conditions experienced little to no warming. Temperature, precipitation, and fertilizer inputs were the considered variables, and this study could not find significant correspondence of blooms to any of these variables exclusively [12]. This insignificant result suggests a diversity of causal agents on a per-lake basis. Thus, conducting site-specific studies and monitoring these water quality variables will help establish algal bloom causation and the relative intensity of the confounding variables and, therefore, whether FPV coverage would be an effective mitigation agent. If the algae in a reservoir are linked to less-controllable variables like carbon dioxide concentration in the water or nutrient loading from agricultural runoff, FPV-shading will have a negligible effect on algae [6, 7]. Such considerations are critical to informing the potential environmental co-benefits of an FPV installation.

FPV Solar Cell Design

The properties of solar cells within the photovoltaic panels themselves are instrumental in determining what wavelengths of light interact with the surface of the host waterbody under the panels. Crystalline silicon solar cells absorb radiation wavelengths from 300-1300 nm and have a thick active layer of about 300 µm, responsible for high photon absorption [13]. These properties result in opaque solar panels that do not allow photons to travel through the panel and interact with the waterbody. Conversely, semitransparent polymer solar cells (ST-PSCs) represent an alternative material and technological approach, and algae growth can be regulated by engineering the panels to provide specific transmission windows and light intensities. Zhang et al., 2020 found that the growth rate for the algal genus Chlorella was minimized under the opaque treatment; however, the changes in photosynthetic efficiencies did not significantly affect the growth rate of Chlorella during the 24-hour experimentation window. While the researchers were able to show the variability in the number of photons penetrating the panels from 300-1000 nm across three treatments of different layering of material within the ST-PSCs, they were unable to yield a significant result in their study [5]. These results have limited scope because this study was conducted in a lab and did not assess real-sized PV panels in the field; however, it highlights how algae species may prefer different light wavelengths for photosynthesis that may be discontinuous with the wavelengths an FPV system best controls. Therefore, it is vital to coordinate solar panel material design in order to reflect and absorb the primary wavelengths that support algal photosynthesis. The viability of prioritizing this component of FPV is uncertain; however, new materials and technologies are being developed and utilized, and this relationship must be considered as we work to maximize FPV coverage in reservoir systems with minimal ecological complications (Figure 2).

Figure 2. Relationship between FPV transparency, light profiles entering the waterbody and interacting with algae, and FPV coverage optimization. Solar cell design influences light transmission, and photosynthetic rates in algae vary with light wavelength and intensity, providing site-specific design opportunities.

CONCLUSION

FPVs are relatively untapped climate change mitigation solutions and can potentially reduce algae, benefitting water quality in freshwater ecosystems and reservoirs that suffer from strong summertime algal blooms. Algae are critical primary producers in reservoir ecosystems; therefore, areas for future research include microalgae response to the reduced sunlight conditions created by FPVs and the ecological role of algal taxa within the reservoir ecosystem. Further laboratory studies of solar panel designs in this context are needed. Future research on FPVs and water quality must also account for climate change, shifting baselines, and environmental variables. From a reservoir management viewpoint, this includes studying whether reservoirs have lower nutrient loading and whether the algae can be managed with FPV arrays, fingerprinting the inter-reservoir variability to determine where we should spatially place FPV arrays and localize impacts, and further modeling the relationship between warming and algal blooms to understand the long-term effectiveness of FPV-based algae management. Climate change will continue to operate in the background, and energy security issues will intensify. Our understanding of the environmental impacts of FPVs is currently limited to the point where we cannot safely approve and construct these systems on most reservoirs; therefore, future studies are needed to incorporate this modern technology into the global renewable energy portfolio.

REFERENCES

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  2. Energy Sector Management Assistance Program, Solar Energy Research Institute of Singapore. 2019. Where Sun Meets Water: Floating Solar Handbook for Practitioners. Washington, DC (USA): World Bank.
  3. Cagle AE, Armstrong A, Exley G, Grodsky SM, Macknick J, Sherwin J, Hernandez RR. 2020. The Land Sparing, Water Surface Use Efficiency, and Water Surface Transformation of Floating Photovoltaic Solar Energy Installations. Sustainability [Internet]. 12(19):8154. doi:10.3390/su12198154
  4. Spencer RS, Macknick J, Aznar A, Warren A, Reese MO. 2019. Floating Photovoltaic Systems: Assessing the Technical Potential of Photovoltaic Systems on Man-Made Water Bodies in the Continental United States. Environ Sci Technol [Internet]. 53(3):1680–1689. doi:10.1021/acs.est.8b04735
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First steps in the development of small-scale 3D printed hydrogel bioreactors for protein production in space travel

By Maya Mysore, Laura Ballou, Anna Rita Moukarzel, Alex Cherry, David Duronslet, Lisette Werba, Nathan Tran, Hannah Mosheim, Stephen Curry, Simon Coelho

Advisors: Kantharakorn Macharoen, Matthew McNulty, Andrew Yao, and Dr. McDonald, Dr. Nandi, and Dr. Facciotti

 

Author’s Note: My name is Maya Mysore, and I am a team lead on the BioInnovation Group’s Plant Bioprinter project. The BioInnovation Group is a student organization that creates research and leadership opportunities for undergraduates. The Bioprinter project is one of these opportunities.

I joined the BioInnovation group (BIG) in the winter quarter of 2019, as a freshman looking for ways to get involved on campus. I knew I liked research; I had been working in another lab. However, I was looking to explore different aspects of research. I heard about BIG through some friends in my major and went to an information session. There, I tried to join the more tech-based microfluidics project; however, my previous lab experience with cell culture convinced the lead for the Bioprinter project to get me involved in their work. I spent the next couple quarters investigating how to trap viruses in hydrogel. In Fall 2019, I was offered the role of lead. I was shocked, surprised, and a little out of my depth– after all, I had practically joined the project by accident! But I took on the role, excited about the leadership opportunity and the freedom. Now over a year into being project lead, I am planning to transition into the organization’s leadership. However, as a swan song to my time in charge, I wanted to compile all the hard work those involved with the project have accomplished. This paper is a celebration of the work of tens of student researchers over a period of several years. Hopefully, this paper will be the first of many for the Bioprinter project and the BioInnovation Group.

 

Abstract 

As human space exploration expands to include potential settlement on the Moon and Mars, the ability to build shelter, manufacture food, produce medicine, and create other necessities in space will become increasingly important. Currently, the high cost and size constraints of sending payloads into space challenges us to think beyond the traditional manufacturing and agricultural tool-kit. Engineers have proposed that additive manufacturing, particularly 3D printing, is a solution to lower the payload costs and to enable the manufacturing of a variety of products in situ. This study focuses on 3D printing engineered biological cells for the production of biologics (e.g. pharmaceuticals that are living or derived from a biological source). We describe in-progress work to design, build, and test a small and affordable 3D bioprinter capable of printing 3D structured hydrogels that can carry living cells. We provide a general overview of the project, our progress in converting a low-cost and compact 3D printer from printing plastics to printing hydrogels, and preliminary work testing the compatibility of bioink formulations with genetically engineered rice cells that produce and secrete the enzyme butyrylcholinesterase. 

 

Background

As humans continue to explore space and potentially settle in distant locations such as the Moon, Mars, and beyond, it will become increasingly necessary to build shelter, create food, and develop medicine while in space. However, the major costs (roughly $20,000/kg) and size constraints of sending payloads into space create challenges for such long-duration space travel beyond low Earth orbit [1-4]. Challenges include the manufacturing of food, shelter, and even medicine. 3D printing has been proposed as a cost-effective method for addressing some of these challenges, as it might allow the opportunity to ship only the printer to remote sites and to source the majority of the printing materials from the settlement location [5]. 

Biological systems may also play a large role in this approach. Microorganisms have been envisioned to help construct habitats through biocementation, a process that uses microorganisms to solidify inorganic matter into 3D structures [6-8]. Plants and microbes together are proposed as possible tools for the creation of sustainable ecosystems that recycle and detoxify waste and produce food [9-11]. A purported advantage of biological systems is that they can self-replicate, as each organism carries the full set of genetic instructions to create copies of itself. This means that biological systems could be delivered as light-weight “seeds”, i.e. self-replicating units that can be shipped in small and light quantities and grown to larger quantities upon permanent settlement at remote bases. 

We and others envision that the 3D printing of engineered living systems (e.g bioprinting) may prove useful for the manufacturing of biologicals; this includes pharmaceuticals of or derived from a biological source [12]. In this context, the engineered living system serves as an on-demand expandable factory for the production of the biological while the 3D printer serves to produce custom-made culturing and purification hardware that can be produced in the geometries required for specific cells and production sizes. We were interested in exploring this concept and better understanding the challenges associated with the proposed process of drug production through bioprinting. In order to do this, we needed a bioprinter. Depending on their feature sets, commercial bioprinters can cost anywhere between $10,000 and $200,000, which was well outside our budget.  Therefore, as a first step, we sought to design, build, and test a low-cost and compact bioprinter that we could later customize and use to explore novel design ideas.

FExisting modalities of bioprinting were considered and four main existing modalities of 3D bioprinting were considered: inkjet, pressure-assisted, laser-assisted, and stereolithography. For a detailed review on this subject, see Li et. al [13]. The major factors that were considered in the selection of a printer were types of usable bioinks, potential for good cell viability, cost, and complexity of the system (e.g. ease with which it can be modified). Inkjet-based bioprinting uses computer controls to drop small drops of bioink onto a surface. This type of printing maintains high short-tem cell viability and is widely available at low cost. However, it is limited in printing materials and creates high thermal and mechanical stress on cells which risks damage to cells and may affect long-term viability. Pressure-assisted bioprinters extrude bioink continuously onto a surface. While the extrusion process is slower and can lower cell viabilities immediately after printing (ranging from 40-80%, compared to 90% for inkjet printing), it allows use of a greater variety of materials and incorporates cells directly into the bioink. Laser-assisted bioprinters use a laser to irradiate a bioink such that the droplets adhere to the desired surface. This method of bioprinting is very precise and results in the highest cell viability; however, it is the most expensive, time-consuming, and has the highest risk of metal contamination. Finally, stereolithography printing uses illumination of a light-sensitive polymer to solidify 3D shapes. This method is fast, cost-effective, and has high final cell viabilities, but it is primarily limited by the need for a light-sensitive bioink, many of which are not biocompatible.  

We chose to build a pressure-assisted bioprinter primarily due to practical factors: (a) the availability of low cost and compact fused deposition modeling (FDM) printers that could be used as chassis, theoretically enabling a “simple” swap of printing nozzles and pumps while taking advantage of the existing build platforms and 3D control systems; (b) the easy access to safe and low cost of compatible bioinks, and (c) the ability to incorporate cells directly into the bioink for prototyping. 

This paper describes the progress of our project in developing a functional bioprinter. In addition, we describe the chemical assays used to evaluate engineered rice cell viability within hydrogels and these cells’ cell’s ability in gels to produce the pharmacologically-relevant enzyme Butrylcholinesterase (BChE), which is a complex human serine hydrolase enzyme that provides protection against organophosphorus poisoning from toxic agents such as sarin.

Figure 1. This diagram demonstrates the model methodology for seeding the cells into the hydrogel, printing out the cell-gel complex, and extracting the protein of interest from this system.

 

Methods

Printer selection, modification and testing

Selection of chassis

We sought to find a low-cost and compact FDM printer system that could be reasonably modified to extrude bioink rather than plastic filament. We ultimately selected the Monoprice MP Select Mini 3D Printer V2 because of its high availability, low cost ($250), and relative ease of modification. An accurate open source 3D computer-aided design (CAD) model  (https://www.thingiverse.com/thing:2681912) of this printer was already available, making it easier to design new features for this specific unit. 

Construction of an bioink extruder 

To start converting the 3D plastic printer into a bioprinter, the printer’s original extrusion mechanism was replaced with a standard syringe/syringe-pump mechanism typical of bioprinters [14]. 

Incorporating the syringe-based bioink extruder required the design and construction of the entire extrusion system. An interchangeable mount was designed to hold the 10 mL syringe on the printer access, as seen in Figures 2b and 2c. In Figure 2b, the interchangeable mount design is shown with a trapezoidal connection piece, allowing the mount to swap between holding the 3D printer plastic extruder and the bioprinter syringe extruder system. 

Figure 2. a) Inside of the 3D printer after all electrical components and panels were removed b) 3D printed interchangeable mount used to exchange the plastic extruder and the syringe extruder. c) The hydraulic extrusion system as connected to the bioprinter  d) The hydraulic extrusion tubing system

The 10 mL syringe was connected to a hydraulic pumping system through a plastic tube. The hydraulic system is controlled using a Nema 17 Bipolar 40 mm Stepper Motor connected to an 8 mm threaded rod, forming a linear actuation mechanism. Connected to the rod is a 60 mL syringe plunger which is pushed through a 60 mL syringe. A liquid is placed in the 60 mL syringe and the bioink is placed in the 10 mL syringe also with a plunger sitting on top of the syringe. When the motor turns on, this liquid is pushed from the 60 mL syringe through the tubing and into the 10 mL syringe. This system pushes the plunger through the 10 mL syringe and extrudes the bioink onto the printing surface.

A T fitting made from 6 mm brass tubes was attached to the middle of the tubing system in order to remove air bubbles from the tube, as shown in Figure 2d. 

Integration of hydraulic motors with chassis

To power the motor for the syringe extruder, the electrical components needed to be rebuilt. With this in mind, an Arduino Mega 2560 was connected with the HiLetGo RAMPS 1.4 control panel and the A4988 stepper motor driver boards using the wiring setup diagrammed in Figure 3. 

 Figure 3. This diagram shows the wiring for the 3D printer using the Arduino.

The Z-axis switch was then repositioned and mounted to the printer chassis directly under the print head, as seen in Figure 2c. 

Firmware

For the firmware, Marlin was selected because it is open sourced and easily modified with the Arduino IDE. After the firmware and electronics were set up, a G code file was needed to determine the print pattern. Cura was used to develop the file due to its compatibility with the Monoprice 3D printer. The Cura profile used with the bioprinter tests followed a cylindrical shape with a square-shaped infill grid. With this information established, the Cura profile was exported as G Code. In the printer design, an SD card is required to flash the firmware and upload the G code to the bioprinter. With the firmware and G code loaded onto the SD card, the bioprinter could be set up to run test prints with the bioink. The final cost spent to make the bioprinter came out to $375. Further information on the process of building the bioprinter can be found at https://www.instructables.com/Low-Cost-Bioprinter/.

 

Hydrogels

Hydrogels are porous water-based polymers that have many valuable uses, especially in fields such as drug delivery and tissue engineering. Here, we use hydrogels for their ability to selectively trap materials on a size basis, as this is what allows us to trap cells and release the protein of interest. Our hydrogel protocol was adapted from Seidel et al., 2017. Briefly, the hydrogel mixture contained agarose (0.2275% w/v), alginate (2.52% w/v), methyl cellulose (3% w/v), and sucrose (3% w/v).  Agarose, alginate, and sucrose were mixed into deionized water at room temperature until dissolved. This mixture and the methyl cellulose powder were then autoclaved in separate containers for 20 minutes at 121 C. Upon completion of the autoclave cycle, methyl cellulose was mixed into the gel. The mixture was then left for 12 to 24 hours in a 2-8 C fridge to allow swelling to occur [15]. After this, the gel was ready to be seeded.

 

Seeding and Crosslinking the Gels

Transgenic rice cells were supplied by the McDonald-Nandi lab. The cells were genetically modified with the addition of a human BChE gene optimized for rice cell compatibility and cloned into the RAmy3D expression system for transformation into A. tumefaciens to allow insertion into rice cells [16]. This allowed the engineered cells to produce the pharmacologically-relevant BChE protein. The provided cell suspensions were mixed thoroughly via pipetting to obtain even distribution of cells. This suspension was then added directly to the hydrogel in a 50% volume split of cell suspension and gel and gently mixed to distribute cells evenly. To crosslink the gels and create solid structures for later use, a 0.1 M calcium chloride solution was prepared. The hydrogel was loaded into a syringe and deposited into weight boats containing enough CaCl2 solution to half-cover the extruded hydrogel. The hydrogel would then cure in the solution for at least 5 minutes or until the shape solidified. Upon completion of curing, the hydrogel could be removed and used for experiments.

 

Tetrazolium Chloride Viability Assay on Hydrogels

The TTC (2,3,5-triphenyltetrazolium chloride) assay is a method for testing cell viability. TTC is turned red from a colorless solution in the presence of metabolizing cells, allowing for quantification of cell viability. When used with defined standards and run on a spectrometer, it can be used to monitor cell survival over time.

Preparation of the TTC solution involved mixing 0.4% w/v TTC in 0.05 M sodium phosphate buffer, pH 7.5. Once the TTC solution was prepared, the TTC assay was performed. 

5-6 mL of 0.05 M sodium phosphate buffer was added to a 15 mL Falcon tube with cured gel to submerge the cured gel entirely. The gel remained in the solution for 15 minutes. Then the Ellman buffer was removed from the tube and 500 μL of TTC were added to the tube with gel while mixing slightly. This tube was stored in a dark area for 24 hours. 

If the gel was not cured, roughly 5 mL of gelled cells were first centrifuged in a 15 mL conical tube at 4500 g for 20 minutes. The supernatant was removed and 1 mL of Ellman buffer was added and mixed. The sample was centrifuged again at 4500 g for 15 minutes, the supernatant was removed, and 500 μL of TTC solution were added to the gel-cell mix. This sample was stored for 24 hours in a dark area.

After the 24 hours period ended, the sample-TTC mix was centrifuged at 4500 g for 15min. The supernatant was removed and the gel-cell mix was washed with 1 mL deionized water. The mixture was re-centrifuged at 4500 g for 10 minutes. The supernatant was removed again and 1 mL of 95% ethanol was added to the gel-cell mix. The sample was transferred to a microcentrifuge tube and placed in a 60C water bath for approximately 10 minutes. The sample is then centrifuged at 21.1 g for 15 minutes to recover the final supernatant. The supernatant was then run on a colorimeter or Tecan and the absorbance value was read at 485 nm. Beer’s law was then used to determine concentration from this value. 

 

Seeded Cell-Ellman BChE Concentration Assay

The Ellman assay was used to measure BChE concentration for a sample at a given time point. This assay uses the kinetics of a color changing reaction to quantify the amount of BChE in solution. When in the presence of specific substrates, BChE turns a colorless solution yellow; the peak rate of this reaction can be determined and used to calculate BChE mass in a sample.

After cells were seeded into a hydrogel complex with a disc shape approximately 7 cm in diameter and 1 cm thick, the complex was suspended in 40 mL sucrose-free nutrient broth (NB-S). 

The flask was then covered with a cloth filter and placed in the shaking incubator (37C, 5% CO2, 80 rpm). 50 μL media samples were collected from the flask daily over 14 days and the Ellman assay was run directly following collection of each of these samples. 

The Ellman assay protocol was based on the Cerasoli lab protocol, which was adapted from Ellman et al., 1961 [17]. To perform the Ellman assay, a 20 mm stock solution of 5, 5’ – dithiobis-(2–nitrobenzoic acid) (DTNB) was prepared. A 75mM stock solution of S-Butyrylthiocholine (BTCh) iodide was also prepared. 

Immediately prior to performing the Ellman assay, the Ellman substrate was prepared. 60 μL of DTNB and 30 μL of BTCh were added to the phosphate buffer in the falcon tube.  The tube was temporarily stored in ice with light protection. 

Then the Ellman assay was performed. In a 96-well plate, 50 μL of sample containing BChE was were diluted into 0.1 M phosphate buffer, pH 7.4, to ensure the generated? outputted slope readings (mOD/min) would fall in the range of 200-1000 when read for 3-5 minutes at 25 C. This dilution was done by estimating the approximate BChE concentration and estimating the mOD/min based on the expected value. 150 μL of Ellman substrate was added to each sample containing well. The optical density of the sample was immediately read at a wavelength of 405 nm for a total of 300 s (5 min) after the measurement was started. 

After collecting data from the assay, Beer’s law was used to determine the concentration of product formed. From that value, we could estimate the mass of functional BChE in the total volume of the sample collected [18]. 

 

Results

TTC-Gel compatibility 

To measure in-gel cell viability, we evaluated the use of the tetrazolium chloride (TTC) assay. This assay measures metabolic activity in live cells by reducing tetrazolium chloride to red formazan through the process of cell metabolism. Effectively, it provides an indication of how well the cells survive over time. Our team modified the assay for use in gels by including extra Ellman buffer and centrifugation steps to provide more opportunity for cells in the gel to be washed. 

Figure 4. This figure shows the results of the TTC assay run on the transgenic rice cells in suspension. The leftmost tube is a positive control showing the TTC assay done on cell aggregates in suspension (i.e. without gel) that have been centrifuged into a pellet after the assay was performed. The middle and rightmost tubes are cells suspended in a hydrogel; the TTC assay was performed on this combination of cells in gels. In each tube, the cells have been stained red from the assay, indicating the presence of metabolic activity. These samples can go on to be washed and suspended in ethanol to obtain a viability data value. 

To qualitatively assess how different factors like cell distribution and crosslinking might influence the results of the TTC assay, we performed additional variations of the assay. We first visually examined whether cell homogeneity was impacted by the gel. Then, we performed the TTC assay on E. coli cells alone as a positive control. After that, we tested the effects of non-crosslinked and crosslinked gel to ensure neither condition would prevent the use of the assay. E. coli was used for these tests due to our group’s ability to access it more regularly and grow it more easily than the genetically modified rice cells from Dr. McDonald’s lab. All of these tests together allowed us to determine that cell survival could indeed be measured within the gel, allowing us to monitor culture health over time. This will be critical in future use of the model, allowing us to determine ways to improve cell health and protein output by providing a metric for us to test against.

Homogeneous mixing of biological sample

To determine later TTC accuracy, the first key issue to address was homogeneity of cells in a hydrogel. This would determine whether sectioned off samples of cell-gel complexes would be representative of a whole sample. To ensure that the gel mixing protocol yielded a homogeneous suspension of the cells, we first tested our procedure by mixing E. coli expressing a transgenic green fluorescent protein (GFP) and imaged the suspension under UV light. We expect E. coli to distribute homogeneously in the gel similarly to the transgenic rice cells. This mixture was observed (Figure 5a) and confirmed by visual inspection of a homogeneous mix.

Test of TTC assay with bacterial suspension

To ensure that the TTC assay in later tests would be effective with E. coli, we first tested the TTC assay on an E. coli suspension as a positive control for later tests. We ran the modified TTC assay protocol described in Methods, and observed a color change in the solution. The resultant red solution (Figure 5b) matches the literature expectations for the output of this assay on living cells and indicates the assay is effective for E. coli.

Test of TTC assay with bacteria seeded in hydrogel

After confirming the TTC assay was effective with E. coli, it became important to determine how the presence of gel would affect the assay. We suspended the E. coli cells in the hydrogel and ran the modified TTC assay. The results seen in Figure 4c show the suspension turning red, which visually indicates the presence of cell metabolic activity and the effectiveness of the TTC assay.

Test of TTC assay with bacteria seeded in a crosslinked hydrogel

Upon determining the gel did not qualitatively affect the output of the TTC assay, it became necessary to determine whether crosslinking the gel had any effect on the effectiveness of the TTC assay. We reran the same experiment as the non-crosslinking hydrogel experiment, with the only change being the crosslinking process and the different first wash step. We found that the result of the TTC assay appears to be unaffected by the presence of the crosslinked out layer, as the solution turns red in the same way it does for the positive control and the non-crosslinked gel (Figure 5d).

These experiments allowed us to qualitatively determine whether the TTC assay could be an effective measure of cell viability. They also demonstrated that the introduction of a crosslinked hydrogel will not have visible impacts on measuring cell viability.

Figure 5. Qualitative TTC assays were run on E. coli with the pMax plasmid to test homogeneity within the gel and the effectiveness of the TTC assay in different hydrogel conditions. 5a shows the bacteria mixed homogeneously within the hydrogel, which is visible in the fluorescence that is present homogeneously through the sample. 5b shows the ethanol suspension output for a TTC assay run on a pMax E. coli culture, providing a control for later experiments and showing that the TTC assay is effective for E. coli. The left image is the control and the right image is the test condition. The control is run in the same conditions as the test, except the cells are placed in a 60C water bath for ten minutes prior to adding TTC in order to kill them. 5c shows the output prior to ethanol suspension for a TTC assay on E. coli pMax cells that were suspended in an uncured hydrogel. The left tube is the control and the right tube is the experimental condition. The red color visible in the right tube shows that the presence of the hydrogel does not prevent use of the TTC assay. 5d shows the ethanol suspension output for a TTC assay run on E. coli pMax cells that had been suspended in a cured hydrogel. The left image is the control and the right image is the experimental condition. The red color of the suspension indicates the TTC assay remained effective even with the addition of the crosslinked outer layer of the gel. Throughout this figure, variation in intensity of the redness of the samples is related to variations in time spent in suspension of the TTC solution, with redder samples correlating to longer time.

 

Initial Attempts at Measuring BChE Production 

Our second major goal was to determine whether BChE could be collected from our model system (as seen in figure 1). This would allow us to determine if our model system was an effective way to collect our protein of interest for future space travel applications, as well as confirm that our test for BChE quantity would be effective in this system. To test this, our team ran the seeded cell-Ellman assay as described in methods to assess the amount of BChE that was escaping into the media. We first prepared a hydrogel, mixed the transgenic rice cells in, and cured it into a disc shape roughly 7 cm in diameter and 1 cm in height. We then suspended this cured cell-gel complex in NB-S media to stimulate BChE production, and we kept this mix in a spinning incubator to ensure aeration and adequate diffusion of materials in and out of the gel. Media samples were collected over the course of 14 days and were run with the Ellman assay for BChE detection on a spectrometer. The Ellman assay uses the enzyme kinetics of a color-changing reaction between BChE and a substrate to quantify the amount of BChE present in a sample at a given time point.

It is important to note that this test was intended as a trial run of the system in order to ensure that the assay works and that useful data is being collected. In addition, we sought to assess if BChE could escape from the gel at all. Therefore, no negative control was run and only one run of data was collected (shown in the figure below.) As a result, we cannot conclusively state anything about the data. However, the data does show a trend worth noting for future experimentation. The This is that active BChE concentration in the media increased for the first roughly 100 hours, after which the values dropped off. At the time point marked in figure 5, 96 hours, we see the maximal BChE present. If the unusually low value seen at the roughly 120 hour time point is considered erroneous (which we suspect), the data suggests increased production of BChE over the first 4 days of culture followed by a slow decay thereafter with production ending at around day 8. This provides an early quantitative estimate of the time-dependence of BChE production in this model system. This experiment is a first attempt and will be repeated with various parameter variations in the future. 

Figure 6. This plot shows the approximate active concentration of BChE released into the media for various time points over 14 days. Each sample was a 50 μL amount of media pulled from the small scale system model. This figure shows a burst in production of active BChE until the 96 hour time point (denoted with a dashed red line), after which the values drop off. The data point at t=120 hours is most likely an outlier resulting from this data being for one set of samples from one test condition.

 

Preliminary Bioprinter Testing

The process of building and testing the bioprinter was done in parallel with the TTC and Ellman assay testing. Detailed bioprinter testing has not been performed; however, initial testing of the printer showed its ability to print hydrogel into pre-programmed patterns. The grid pattern seen in Figure 7 was printed into a petri dish containing CaCl2 curing solution. The print shows excess hydrogel accumulation near the edges where the printhead briefly paused and reversed direction. In the center of the print, the lines in the grid averaged 1.25mm +/- 0.4 in width. Further testing and refinement is currently in process.

Figure 7. This shows a test print from our modified 3D printer using the hydrogel described in the methods section and cured in standard CaCl2 curing solution. This structure is described as a lattice shape and will be the primary pattern for future prints.

 

Discussion

In these experiments we determined that the TTC assay was effective in hydrogels, the Ellman assay showed the ability of protein to be detected from solution, and the bioprinter was able to create the desired lattice shape for later use.

 

Printer Performance

Our experiments to date have demonstrated our ability to convert a low-cost and compact FDM printer into a preliminarily functional bioprinter. The conversion of the original chassis required the modification of the printhead support, the development of a syringe-based hydraulic pump, and the modification of electronic and software control systems. Preliminary prints indicate that the printer can successfully deposit a programmed pattern with feature sizes in the range of 1.5mm. Existing conventional commercial bioprinters can achieve resolutions of 100-200µm, (some even claim filament diameters as low as 3µm), suggesting that we have room to improve the resolution of our system [19]. In addition to improving the resolution of the prints, we want to explore alternate methods for delivering the CaCl2 curing solution during alginate filament deposition to minimize user interaction and allow complete processing inside a biosafety cabinet; this should allow us to increase sterility during printing and print quality.

 

Cell Viability

Since it is known that pressure-assisted printing may negatively impact cell viability during printing, a key concern was the resulting cell viability of the system. As a result, our general goal for this phase of the project was to test whether a pressure-assisted bioprinter system could maintain cell viability after extrusion. We adapted the TTC assay for this purpose and tested our protocol to determine the effect of bioink and extrusion on cell viability under conditions mimicking those experienced during bioprinting.

Generally, the TTC assay demonstrated the ability of the assay to cellular viability in the crosslinked hydrogel, despite the unknown nature of how crosslinking affects pore size. Despite this success, the TTC assay remains largely qualitative as it is challenging to get quantitative measurements of cell viability when cells are embedded in a gel. This is further complicated by factors like the heterogeneous distribution of cells (or cellular aggregates) in the gel (see figure 4, rightmost sample). If homogeneity is not maintained, we need to design assays that take into account heterogeneous distribution of cells in the gel.  In future experiments, we seek to determine whether samples from a large complex of cells in a hydrogel will provide a representative sample. 

In later experiments, additional key variables that may potentially affect viability will be tested. These variables include media composition, culture duration, environmental conditions such as temperature, gel architecture, and the additional variables associated with the printer extrusion process (e.g. pressure, needle pore diameter, etc.). Determining how these specific factors affect viability will allow us to modify the printer design to minimize the drop in cell viability upon extrusion.

 

Protein production

Having confirmed the effectiveness of the TTC assay in the hydrogel, we moved forward to analyzing BChE production and its diffusion into the media. The assay we adopted allowed us to develop a standard method for data collection that can be used to analyze how various factors impact the cells’ ability to produce BChE. Figure 6, for example, shows that we can measure BChE production and diffusion out of the gel, and that under our preliminary experimental conditions, production peaks at 96 hours and then falls over the next 150 hours. While encouraging, this experiment needs to be repeated with many more samples and replicates to obtain a more reliable assessment of measurement error associated with the assay. Despite needing to replicate the experiment, we are confident that this preliminary experiment answered the core question of whether such a large protein – 85 kDa monomers and 4 units in quaternary form, with a total size of 574 monomers [20] – can effectively diffuse out of the hydrogel and avoid denaturation long enough to be collected and purified

In addition to replication, future experiments should be explored to further improve protein escape from the hydrogel. These tests could increase the mixing speed to use centrifugal force to free proteins from the gel, increase pore size to create more physical space for protein escape, or print the 3-dimensional lattice structure to increase surface area and allow greater escape. Other relevant variables whose impact on BChE production should be tested include media composition and media changing schedules, culture duration, environmental conditions, gel architecture, and growth temperature. In our initial experiments, plant cells were grown in a shaking incubator at 37C to mimic the environment of protein production in mammalian hosts.  However, this growth condition may have stressed the plant cells for which growth at 27C is more typical [16, 21]. This may explain the trend shown in figure 6, where die-off occurs after 96 hours. 

Finally, in our current studies, the presence of sucrose in gel formulation (which inhibits BChE production) may have adversely impacted the amount of protein produced. While we expected that overlaying a relatively large volume of sucrose-free media would effectively dilute the sucrose to low levels, the presence of sucrose in the initial formulation could have nevertheless impacted the cells’ initial states and therefore protein production. A followup experiment that more stringently controls for the presence of sucrose in the gel than in the studies described above seems warranted.

 

Conclusion

In this work, we successfully modified an off-the-shelf pressure-assisted 3D printer into a working bioprinter. In addition, we established that BChE producing rice cells are biocompatible with the different bioink gel formulations and that our assays for testing cell viability and protein production are effective when analyzing the cells within the gel. Having shown that we can print gel, assess cell survival, produce BChE, and quantify its abundance, we next seek to optimize both printer function and the measurement assays for cell viability and protein concentration in ways that provide more quantitative data and more refined control over printed structures. Eventually, we expect that such advances will allow us to optimize protein production itself and ultimately develop a bioprinter suitable for protein production during space travel or in other remote locations.

 

Acknowledgements

Thank you to the Molecular Prototyping and BioInnovation Lab for the lab space, the BioInnovation Group for the administrative, scientific, safety, and monetary support, the McDonald-Nandi lab for materials and mentorship, and all past, present and future members of the Bioprinter team for contributing to these experiments. 

 

References:

  1. Joshi SC, Sheikh AA. 2015. 3D printing in aerospace and its long-term sustainability. Virtual Phy Prototy [Internet]. 10(4):175–185. doi.org/10.1080/17452759.2015.1111519
  2. Hodkinson PD, Anderton RA., Posselt BN, Fong KJ. 2017. An overview of space medicine. Br J Anaesth [Internet]. 119(suppl_1):i143–i153. doi.org/10.1093/bja/aex336
  3. Aglietti, GS. 2020. Current Challenges and Opportunities for Space Technologies. Front Space Tech [Internet]. 1:1. doi.org/10.3389/frspt.2020.00001
  4. Coopersmith J. 2011. The cost of reaching orbit: Ground-based launch systems. Space Policy  [Internet]. 27(2):77–80. doi.org/10.1016/j.spacepol.2011.03.001
  5. Ghidini T. 2018. Regenerative medicine and 3D bioprinting for human space exploration and planet colonisation. J Thorac Dis [Internet]. 10(Suppl 20):S2363–S2375. doi.org/10.21037/jtd.2018.03.19
  6. Gleaton J, Lai Z, Xiao R, Chen Q, Zheng Y. 2019. Microalga-induced biocementation of martian regolith simulant: Effects of biogrouting methods and calcium sources. Constr Build Mater [Internet]. 229:116885. doi.org/10.1016/j.conbuildmat.2019.116885
  7. Pilehvar S, Arnho M, Pamies R, Valentini L, Kjøniksen AL. 2020. Utilization of urea as an accessible superplasticizer on the moon for lunar geopolymer mixtures. J Clean Prod [Internet]. 247:119177. doi.org/10.1016/j.jclepro.2019.119177
  8. Kumar A, Dikshit R, Gupta N, Jain A, Dey A, Nandi A., … Rajendra A. 2020. Bacterial Growth Induced Biocementation Technology, ‘Space-Brick’ – A Proposal for Experiment at Microgravity and Planetary Environments. BioRxiv  [Internet]. 2020.01.22.914853. doi.org/10.1101/2020.01.22.914853
  9. Lopez JV, Peixoto RS, Rosado AS. 2019. Inevitable future: space colonization beyond Earth with microbes first. FEMS Microbiol Ecology [Internet]. 95(10). doi.org/10.1093/femsec/fiz127
  10. Bornemann G, Waßer K, Tonat T, Moeller R, Bohmeier M, Hauslage J. 2015. Natural microbial populations in a water-based biowaste management system for space life support. Life Sci Space Res [Internet]. 7:39–52. doi.org/10.1016/j.lssr.2015.09.002
  11. Bokulich NA, Lewis ZT., Boundy-Mills K, Mills DA. 2016. A new perspective on microbial landscapes within food production. Curr Opin Biotechnol [Internet]. 37:182–189. doi.org/10.1016/j.copbio.2015.12.008
  12. Varma A, Gemeda HB, McNulty MJ, McDonald KA, Nandi S, Knipe JM. 2021. Bioprinting transgenic plant cells for production of a recombinant biodefense agent. BioRxiv  [Internet]. 2021.02.01.429263. doi.org/10.1101/2021.02.01.429263
  13.  Li J, Chen M, Fan X, Zhou H. 2016. Recent advances in bioprinting techniques: approaches, applications and future prospects. J Transl Med [Internet]. 14:271. doi.org/10.1186/s12967-016-1028-0
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  16. Corbin JM, Hashimoto BI, Karuppanan K, Kyser ZR, Wu L, Roberts BA, … Nandi S. 2016. Semicontinuous Bioreactor Production of Recombinant Butyrylcholinesterase in Transgenic Rice Cell Suspension Cultures. Front Plant Sci [Internet]. 7:412. doi.org/10.3389/fpls.2016.00412
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  19. Gu Z, Fu K, Lin H, He Y. 2020. Development of 3D bioprinting: From printing methods to biomedical applications. Asian J Pharm Sci [Internet]. 15(5):529–557. doi.org/10.1016/j.ajps.2019.11.003
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  21. Macharoen K, McDonald KA, Nandi S. 2020. Simplified bioreactor processes for recombinant butyrylcholinesterase production in transgenic rice cell suspension cultures. Biochem Eng J [Internet]. 163:107751. doi.org/10.1016/j.bej.2020.107751

Fold@Home: Aid in COVID-19 Research from Home

Image via Folding@Home

By Nathan Levinzon, Neurobiology, Physiology, and Behavior ‘23

Author’s Note: The purpose of this article is to introduce and inform the UC Davis scientific community of Folding@Home; a distributed computing project that allows individuals and researchers to donate computing resources from their computers towards COVID-19 research.

Keywords: COVID-19, Folding@Home, Distributed Computing

 

Reports of localized viral pneumonia cases in the Chinese city of Wuhan began in December 2019, initially amounting to little concern for humanity. Since then, the world has drastically changed as a result of COVID-19. As of September 16, 2020, almost thirty million cases of COVID-19 have been confirmed across the globe, claiming the lives of close to one million individuals. In California alone, there have been seven hundred seventy thousand cases with close to twenty three thousand deaths as of September 28th [1]. Millions of individuals are currently under a government-mandated shelter-in-place order, forcing the lives of many to come to a standstill. In a statement made by UC Davis Chancellor May in March of this year, “much of [UC Davis’] research is ramping down, but when it comes to the coronavirus, our efforts continue apace” [2]. One such effort taking place at UC Davis is called Folding@Home (FAH), and it allows researchers to study the mechanisms of COVID-19 with nothing but a computer.

FAH originated as a project to study how protein structures interact with their environment. Currently, some proteins of particular interest to FAH are the constituents of the virus that causes COVID-19. Like other coronaviruses, SARS-CoV-2 has four types of proteins: the spike, envelope, membrane, and nucleocapsid proteins. Many copies of the spike protein protrude from the surface of the virus, where they wait to encounter Angiotensin-Converting Enzyme 2 (ACE2) on the surface of human cells [3]. In order to develop therapeutic antibodies or small molecules for the treatment of COVID-19, scientists need to better understand the structure of the viral spike protein and how it binds to the human enzymes required for viral entry into the cells.  Before the spike proteins on SARS-CoV-2 can function, they must first take on a particular structure, known as a ‘conformation’, through a process known as “protein folding.” As a result of the many factors that impact protein folding, like electrostatic interaction, especially the electrostatic interactions between amino acids and their environment,  research into therapeutics against COVID-19 first necessitates intensive computation in order to resolve protein structure [4]. Only after the proteins of SARS-CoV-2 are understood can the hunt for a cure begin.

FAH is able to study the complex phenomena of protein folding thanks to the computational power of distributed computing. A distributed computing project is a piece of software that allows volunteers to “donate” computing time from the Central Processing Units (CPUs) and Graphics Processing Units (GPUs) located in their personal computers towards solving problems that require significant computing power, like protein folding. In essence, FAH uses a personal computer’s computational resources while the computer is idle to perform calculations involving protein folding. This donated computing power is what forms the “nodes” within a greater cluster of other computers in a process known as “cluster computing.” FAH uses the cluster’s resources to run complex biophysical computer simulations in order to understand the complexities and outcomes of protein folding [5]. In this way, FAH brings together citizen scientists who volunteer to run simulations of protein dynamics on their personal computers. 

Studying protein folding via distributed computing has humble beginnings but has grown into a technology that has the potential to research even the most elusive proteins. First, established protein conformations are used by FAH as starting points for a set of simulation trajectories through a technique called ‘adaptive sampling.’ The theory behind adaptive sampling goes as follows: If a protein folds through the states A to B to C, researchers can calculate the length of the transition time between A and C by simulating the A to B transition and the B to C transition [6]. First, a computer simulates the initial conditions of a protein many times to determine the sample space of protein conformations. As the simulations discover more conformations, a Markov state model (MSM) is created and used to find the most dominant of protein conformations. The MSM represents a master equation framework: meaning that, in theory, the complete dynamics of a protein can be described using a single MSM [7]. The MSM method significantly increases the efficiency of simulation as it avoids unnecessary computation and allows for the statistical aggregation of short, independent simulation trajectories [8]. The amount of time it takes to construct an MSM is inversely proportional to the number of parallel simulations running, i.e., the number of CPUs and GPUs available [9]. At the end of computation, an aggregate final model of all the sample states is generated. This final model is able to illustrate folding events and dynamics of the protein, which researchers can use to study and discover potential binding sites for novel therapeutic compounds.

The power of FAH’s distributed computing in the hunt for a cure to COVID-19 grows with each computer on FAH’s network.ch citizen scientist who donates the power of their idle computer. Currently, pharmaceutical research in COVID-19 has been hindered by the fact that there are no obvious drug binding sites on the surface of the SARS-CoV-2 virus. This makes developing therapeutic remedies for COVID-19 a long, expensive process of ‘check and guess.’ However, there is promise: in the past, FAH’s simulations have captured motions in the proteins of the Ebola virus that create a potentially druggable site not otherwise observable[10]. Using the same methodology as they did for Ebola, FAH has now found similar events in the spike protein of SARS-CoV-2 and hopes to use this result and future results to one day produce a life-saving treatment for COVID-19. By downloading Folding@Home and selecting to contribute to “Any Disease”, anyone can help provide FAH-affiliated researchers with the computational power required to tackle this worldwide epidemic. For more information, refer to https://foldingathome.org/start-folding/.

 

References

  1. Smith, M., White, J., Collins, K., McCann, A., & Wu, J. (2020, June 28). Tracking Every Coronavirus Case in the U.S.: Full Map. The New York Times. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html
  2. May, G. S. (2020, March 20). Update on Our Response to COVID-19. UC Davis Leadership. https://leadership.ucdavis.edu/news/messages/chancellor-messages/update-on-our-response-to-covid19-march-20
  3. Astuti, I., & Ysrafil. (2020). Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): An overview of viral structure and host response. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. https://doi.org/10.1016/j.dsx.2020.04.020
  4. Sarah Everts. (2017, July 31). Protein folding: Much more intricate than we thought | July 31, 2017 Issue – Vol. 95 Issue 31 | Chemical & Engineering News. Cen.Acs.Org. https://cen.acs.org/articles/95/i31/Protein-folding-Much-intricate-thought.html
  5. About – Folding@home. (n.d.). Folding@Home. Retrieved June 28, 2020, from https://foldingathome.org/about/
  6. Bowman, G. R., Voelz, V. A., & Pande, V. S. (2011). Taming the complexity of protein folding. Current Opinion in Structural Biology, 21(1), 4–11. https://doi.org/10.1016/j.sbi.2010.10.006
  7. Husic, B. E., & Pande, V. S. (2018). Markov State Models: From an Art to a Science. Journal of the American Chemical Society, 140(7), 2386–2396. https://doi.org/10.1021/jacs.7b12191
  8. Sengupta, U., Carballo-Pacheco, M., & Strodel, B. (2019). Automated Markov state models for molecular dynamics simulations of aggregation and self-assembly. The Journal of Chemical Physics, 150(11), 115101. https://doi.org/10.1063/1.5083915
  9. Stone, J. E., Phillips, J. C., Freddolino, P. L., Hardy, D. J., Trabuco, L. G., & Schulten, K. (2007). Accelerating molecular modeling applications with graphics processors. Journal of Computational Chemistry, 28(16), 2618–2640. https://doi.org/10.1002/jcc.20829
  10. Cruz, M. A., Frederick, T. E., Singh, S., Vithani, N., Zimmerman, M. I., Porter, J. R., Moeder, K. E., Amarasinghe, G. K., & Bowman, G. R. (2020). Discovery of a cryptic allosteric site in Ebola’s ‘undruggable’ VP35 protein using simulations and experiments. https://doi.org/10.1101/2020.02.09.940510

Applications of Machine Learning in Precision Medicine

By Aditi Goyal, Statistics, Genetics and Genomics, ‘22

Author’s Note: I wrote about this topic after being introduced to the idea through a speaker series. I think the applications of modern day computer science, genetics and statistics creates a fascinating crossroads between these academic fields, and the applications are simply astounding.

 

Next Generation Sequencing (NGS) has revolutionized the field of clinical genomics and diagnostic genetic tests. Now that sequencing technologies can be easily accessed and results can be obtained relatively quickly, several scientists and companies are relying on this technology to learn more about genetic variation. There is just one problem: magnitude. NGS and other genome sequencing methods generate data sets in the size of billions. As a result, simple pairwise comparisons of genetic data that have served scientists well in the past, cannot be applied in a meaningful manner to these data sets [1]. Consequently, in efforts to make sense of these data sets, artificial intelligence (AI), also known as deep learning or machine learning, has introduced itself to the biological sciences. Using AI, and its adaptive nature, scientists can design algorithms aimed to identify meaningful patterns within genomes and to highlight key variations. Ideally, with a large enough learning data set, and with a powerful enough computer, AI will be able to pick out significant genetic variations like markers for different types of cancer, multi-gene mutations that contribute to complex diseases like diabetes, and essentially provide geneticists with the information they need to eradicate these diseases, before they manifest in the patient. 

The formal definition for AI is simply “the capability of a machine to imitate intelligent human behavior” [2]. But what exactly does that imply? The key feature of AI is simply that it is able to make decisions, much like a human would, based on previous knowledge and the results from past decisions. AI algorithms are designed to take in information, generate patterns from that information, and apply it to new data, about which we know very little about. Using its adaptive strategies, AI is able to “learn as it goes,” by fine-tuning its decision-making process with every new piece of data provided to it, eventually making it the ultimate decision-making tool. While this may sound highly futuristic, AI has been used for several years in applications throughout our daily lives from the self-driving cars being tested in the Silicon Valley, to the voice recognition program available on every smartphone today. Most chess fans will remember the iconic “Deep Blue vs Kasparov” match, where Carnegie Mellon students developed an IBM supercomputer using a basic AI algorithm designed to compete against the reigning chess champion of the world [3]. Back then, in 1997, this algorithm was revolutionary, as it was one of the major signs that AI was on par with human intelligence. [4]. Obviously, there is no question that AI has immense potential to be applied in the field of genomics. 

Before we can begin to understand what AI can do, it is important to understand how AI works. Generally speaking, there are two ways AI algorithms are developed: supervised and unsupervised learning. The key difference between the two groups is that in supervised learning, the data sets we provide to AI to “learn” are data sets that we have already analyzed and understand. In other words, we already know what the output will be, before providing it to AI [5]. The goal, therefore, is for the AI algorithm to generate an output as close to our prior knowledge as possible. Eventually, by using larger and more complex data sets, the algorithm will have modified itself enough to the point where it does the job of the data scientist, but is capable of doing so on a much larger scale. Unsupervised learning, on the other hand, does not have a set output predefined. So, in a sense, the user is learning along with the algorithm. This technique is useful when we want to find patterns or define clusters within our data set without predefining what those patterns or clusters will be. For the purposes of genomic studies, scientists use unsupervised learning patterns to analyze their data sets. This is beneficial over supervised learning, since the gigantic data sets produced by omics studies are difficult to fully understand.

Some of the clearest applications of AI in biology are in cancer biology, especially for diagnosing cancer [6].AI has outperformed expert pathologists and dermatologists in diagnosing metastatic breast cancer, melanoma, and several eye diseases. AI also contributes to innovations in liquid biopsies and pharmacogenomics, which will revolutionize cancer screening and monitoring, and improve the prediction of adverse events and patient outcomes” [7]. By providing a data set of genomic or transcriptomic information, we can develop an AI program that is designed to identify key variations within the data. The problem lies, primarily, in providing the initial data set. 

In the 21st century, an era of data hacks and privacy breaches, the general public is not keen to release their private information, especially when this information contains everything about their medical history. Because of this, “Research has suffered for lack of data scale, scope, and depth, including insufficient ethnic and gender diversity, datasets that lack environment and lifestyle data, and snapshots-in-time versus longitudinal data. Artificial intelligence is starved for data that reflects population diversity and real-world information” [8]. The ultimate goal of using AI is to identify markers and genetic patterns that can be used to treat or diagnose a genetic disease. However, until we have data that accurately represents the patient, this cannot be achieved. A study in 2016 showed that 80% of participants of Genome Wide Association Study (GWAS) were of European descent [9]. At first glance, the impacts of this may not be so clear. But when a disease such as sickle cell anemia is considered, the disparity becomes more relevant. Sickle cell anemia is a condition where red blood cells are not disk-shaped, as they are in most individuals, but rather in the shape of a sickle, which reduces their surface area, which in turn reduces their ability to carry oxygen around the body. This is a condition that disproportionately affects people of African descent, so it is not reasonable to expect to be able to find a genetic marker or cure for this disease when the data set does not accurately reflect this population.

Another key issue is privacy laws. While it is important to note that any genomic data released to a federal agency such as the NIH for research purposes will be de-identified, meaning that the patient will be made anonymous, studies have shown that people can be re-identified using their genomic data, the remaining identifiers still attached to their genome, and the availability of genealogical data and public records [10]. Additionally, once your data is obtained, policies like the Genetic Information Nondiscrimination Act do protect you in some ways, but these pieces of legislation are not all-encompassing, and still leave the window open for some forms of genetic discrimination, such as school admissions. The agencies conducting research have the infrastructure to store and protect patient data, but in the era of data leaks and security breaches, there are some serious concerns that need to be addressed.

Ultimately, AI in genomics could transform the world within a matter of days, allowing  Modern biology, defined by the innovation of NGS technologies, has redefined what is possible. Every day, scientists all around the world generate data sets larger than ever before, making a system to understand them all the more necessary. AI could be the solution, but before any scientific revolution happens, it is vital that the legislation protecting citizens and their private medical information be updated to reflect the technology of the times. Our next challenge as a society in the 21st century is not developing the cure for cancer or discovering new secrets about the history of human evolution, but rather it is developing a system that will support and ensure the protection of all people involved in this groundbreaking journey for the decades to come.

 

References

  1. https://www.nature.com/articles/s41576-019-0122-6
  2. https://www.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/
  3. https://en.chessbase.com/post/kasparov-on-the-future-of-artificial-intelligence
  4. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.278.5274&rep=rep1&type=pdf#page=41
  5. https://www.nature.com/articles/s41746-019-0191-0
  6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373233/
  7. https://www.genengnews.com/insights/looking-ahead-to-2030/
  8. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089703/
  9. https://www.genome.gov/about-genomics/policy-issues/Privacy

Robot-Assisted Surgeries

By Neha Madugala, Cognitive Science, ‘22

Author’s Note:

I came across an article detailing the future of surgery. What initially seems like science fiction may be becoming a reality as more and more surgeries are being administered by robots. Through my research, however, I found that robot-assisted surgeries may have the initial appeal of lowering human error, but there are still various issues that must be resolved before they can fully take over in the surgical room. 

 

Robot-assisted surgeries boast the potential of shorter recovery time, less pain and blood, and fewer scars and infections. They have been on the market for a little less than twenty years, and have been used in cancer procedures for about the past fifteen years. While the FDA has approved these devices for other procedures, robot-assisted surgeries have not officially been approved for cancer treatments. Regardless, surgeons have been and continue to perform robot-assisted surgery for cancer-related procedures due to their benefits and increased efficiency. 

Robot-assisted surgeries mainly contrast from traditional surgeries because they can be performed  through small cuts in the patient’s body. As a result, they are minimally invasive. There are three robotic arms, allowing for multiple angles for improved accuracy, which perform the incisions. According to The New York Times, the robotic arms are controlled by a computer and software that replicates the operating surgeon’s movements. This occurs as the operating surgeon performs the movements while looking at a magnified and high-definition screen of the surgical site captured by a camera attached to the robot. While the device requires limited retraining for surgeons, as of now, there is only one company actually offering this device. Interestingly, the device requires less precision and attention by the surgeon due to the magnification and the actual incisions being performed by the robot. 

In 2000, the FDA approved for one of the first robot-assisted systems to be brought to the market. The system, called the da Vinci Surgical System promised to improve the efficiency and effectiveness of medical surgeries, not just cancer-related surgeries. In order to bring the system to the market quickly, the robotic surgery system went through “premarket notification,” allowing the company to skip the rigorous safety and efficacy trials. Essentially, “premarket notification” is supposed to ensure that a device is safe and this notation helps quicken a device’s journey to the market. The FDA said that this decision was based only on short-term data and a spokesperson stated that the decision was made “based on evaluation of the device as a surgical tool and did not include evaluation of outcomes related to the treatment of cancer.” The device promises more successful surgeries with limited retraining and a smooth transition from a humancentric to robot-assisted surgery. These prospects posed limited risks and the evident benefit of improving the success rate of these surgeries; as a result, the device was approved without a thorough and holistic evaluation. 

While this system has only been approved for some urological and gynecological procedures, these devices are used for a vast array of other unapproved procedures. The FDA can assess the safety of these devices for certain procedures, but they cannot prevent these systems from being used in unapproved settings in the medical field. As a result, medical professionals may still use these systems for procedures that have not been approved by the FDA.

At the beginning of March, the FDA released a statement reminding the public that robot-assisted surgeries have not been approved for mastectomy or cancer-related surgeries, two procedures for which the device is frequently used. Dr. Terri Cornelison, who works for the FDA’s Center for Devices and Radiological Health, has stated,  “We are warning patients and providers that the use of robotically-assisted surgical devices for any cancer-related surgery has not been granted marketing authorization by the agency. The survival benefits to patients when compared to traditional surgery have not been established.” The FDA has claimed that there is no supporting evidence that robot-assisted surgeries are better than traditional surgeries and they have further claimed that robot-assisted surgeries result in more problems for patients receiving treatment for cervical cancer. Cornelison further states, “We want doctors and patients to be aware of the lack of evidence of safety and effectiveness for these uses so they can make better informed decisions about their cancer treatment and care.”

The FDA cited two studies that warn against the danger of robot-assisted surgery. Both studies were published by the New England Journal of Medicine. Both studies analyzed the difference between robot-assisted and traditional procedures for cervical cancer in women. The first study found that women who received surgery with robotic methods faced four times as many cancer recurrences and six times as many deaths. It should be noted that the procedure – radical hysterectomy – is considered to be a relatively safe procedure when performed correctly that can cure patients of cervical cancer. Furthermore, in the second study, 9.1% of the sample group died after minimally invasive surgeries, or in other words robot-assisted surgeries, and 5.3% died in open surgeries, which involve no robotic mechanisms. 

It is not clear why robot-assisted surgeries have had worse results for cervical cancers. Dr. Pedro T. Ramirez, a surgical researcher at the Anderson Cancer Center in Houston, believes that these results may be due to the device or because carbon dioxide, which is used to provide a working and viewing space for the surgeon, may increase the spread of cancer during the procedure.  

These findings by the FDA encourage patients to question their medical professionals about what type of procedures they will receive and to know the facts about different methods for surgery. In order to ensure that they receive the best care, it is important that patients have a say in the procedure they will receive by accurately weighing the risks and benefits. While the FDA cannot stop the use of these tools in the medical field, increased interest and probing of the mechanics of these systems are helping raise awareness about what is actually happening in the operation room. 

Looking Deeper into Life: How a Nobel Prize Winner Advanced Microscopy

By Madison Dougherty, Biochemistry and Molecular Biology ‘18

 

Author’s Note:

“I was encouraged to attend and review Nobel Prize winner Eric Betzig’s lectures on campus, and I am extremely glad that I did. As a Biochemistry and Molecular Biology major, I did not think that I would find microscopy very interesting, but after listening to Betzig talk about his developments in the field, I felt a new sense of appreciation for microscopy, and even for telescopes and space. If you are interested in astronomy, physics, chemistry, biology, or all of the above, I highly encourage you to watch and absorb the wealth of information that he has to share with the scientific community. Full video presentations of Betzig’s lectures can be found on the CBS Storer Lectureship website: http://biology.ucdavis.edu/seminars-and-events/storer-endowment/past-lectures/2016-2017.html

 

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Stem Cells: Important Yet Controversial

By: Lauren Forsell, Biological Sciences ’16 and Parnya Baradaran, Computer Science Engineering, ’16

Author’s Note: 

“Parnya and I collaborated on this piece for a Science and Religion: The Case of Galileo seminar assignment. This assignment was inspired by the seminar’s focus on religious controversies surrounding scientific advancements, theories, and concepts. Another main reason why we wrote this piece is because of our backgrounds. Parnya, a computer engineer major, and myself, a biology major, both attended Catholic high schools. We enjoyed writing this piece because analyzing science and technology in the face of religious teachings and practice is something we will have to consider in our future careers. We chose to analyze abortion because it is one of the most popular and controversial science vs religion topics today. After reading this piece, we would like our readers to understand that while science can heal and cure, it can also offend and upset religious groups. As college students studying science, it is our job to develop our own opinions, while respecting those whose beliefs differ from our own.”

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Data Reproducibility: The Chink in Science’s Armor

By Christopher Fiscus, Biotechnology, 2015

Science is an additive discipline in which each novel contribution builds upon the breadth of existing scientific knowledge and acts as a launch pad from which to pursue further study.  The scientific community is currently in the midst of a crisis: many studies are not reproducible, meaning that results cannot be adequately verified by other scientists.  According to estimates, approximately 75-90% of preclinical studies published in high-impact journals, such as Science and Nature, cannot be replicated (Begley and Ioannidis 2015).  This lack of reproducibility undermines science as a vehicle for human progress as it means that new research avenues are being pursued based on presumptive hypotheses and unverifiable findings.  The result is a widespread waste of resources, a loss of public trust in the scientific establishment, and a reduced applicability of science as a tool to better the quality of human life.  Potential solutions to this crisis include improving researcher training, employing more rigorous peer review, and increasing the transparency of scientific literature.      (more…)

Mapping neurons through online gaming

By: Jenny Cade, Biochemistry & Molecular Biology ‘15

One of the biggest challenges in neuroscience today is mapping the wiring of the nervous system. Looking at the spatial arrangement of neural networks can tell us a lot about how information is relayed, but accurate 3D mapping of neurons is an enormously challenging task, even with the aid of computer analysis. One group of researchers at MIT has harnessed the power of crowdsourcing to tackle this problem.

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New Method Increases Supply of Embryonic Stem Cells

By: Varsha Prasad, Genetics ’15

A study to employ a new method of generating human embryonic stem cells without destroying any human embryos is currently being conducted by an international research team led by Karl Tryggvason, Professor Medical Chemistry at Karolinska Institutet and a Professor at Duke-NUS Graduate Medical School in Singapore.

The researchers developed a method in which embryonic stem cells can be obtained from a single cell of an eight-cell embryo, which can then be refrozen and placed in the woman’s uterus.  This prevents the need to destroy human embryos in the process.  The idea is that the embryo can survive a single cell removal. (more…)