Unraveling the Mysteries of Long-COVID

We had a fantastic talk from Dr. Andrea (Andi) Levine, an Assistant Professor of Medicine in the division of Pulmonary & Critical Care Medicine at the University of Maryland School of Medicine. She discussed the current definition of Long-COVID syndrome, what we know about who gets it, why they do, and what we can do to try to both treat and prevent this syndrome.

Long-COVID comes by many names, but refers to symptoms that linger 1-3 months after initial infection, according to the CDC and WHO respectively. We are still very much in this pandemic; Dr. Levine highlighted that at-home testing likely led to the underreporting of overall cases. She highlighted that Long-COVID is its own pandemic and could lead to a mass deterioration event.

Long-COVID symptoms impact almost all organ systems and 80% of patients report at least one symptom that persists long-term. Roughly 50% of patients have ongoing symptoms after 1 month, 5 months, and up to 1 year. The majority of Long-COVID patients were female, obese or with underlying conditions, and around the 50-year age mark. The more symptoms you had earlier on made you more likely to experience Long-COVID symptoms.

The likelihood of a patient acquiring Long-COVID is related to their initial disease severity. A study in The Lancet found that even patients who were less sick initially still reported Long-COVID symptoms. However, that same study alluded that the sicker you were, the more likely you are to experience Long-COVID. The 2-dose vaccination series diminished the likelihood of experiencing persistent illness, but not entirely. Dr. Levine stressed the importance of getting boosters and mask-wearing. Getting vaccinated after COVID-19 infection and long-term symptoms have presented themselves may lead to remission of these symptoms as well. Different COVID variants triggered different persistence in whether patients experienced Long-COVID.

Dr. Levine discussed how the virus can persist in “viral reservoirs” within the body (i.e. the virus can be cleared in a nasal sample, but still present itself in the stool for weeks on end). She stressed again that all organ systems in patients reflect ongoing COVID-19 virus, meaning that no organ tissue is spared. The virus can cross the blood-brain barrier and continue to replicate. In these viral reservoirs (particularly the brain), the virus was seen to mutate from what it was at the time of initial infection. Significant areas of the brain maintained SARS-CoV-2 RNA and structural brain abnormalities could have led to neurological symptoms. COVID-19 infection also caused a prolonged inflammatory state in patients, leading to Long-COVID symptoms. Autoimmunity unmasking by COVID-19 infection is also a probable explanation.

Dr. Levine touched on the mental and social effects of Long-COVID. She discussed how many fear that their experiences are not real and all in their head. Long-COVID correlated with mental health diagnoses and the inability to work. Unfortunately, there are still a lot of unknowns on how inpatient and outpatient therapies impact Long-COVID. She highlighted we, as a society, are at a turning point and must begin to focus on persistent COVID symptoms in addition to the initial infections themselves.


Microbes in Space


The Microbes in Space seminar, co-sponsored by the Maryland Branch of the American Society for Microbiology, highlighted projects conducted in space relating to microbiology. Seminar attendees were joined by Dr. Jennifer Kerr of Notre Dame of Maryland University to present on some key studies happening in space and her own lab’s research.

Kerr first highlighted the many hazards spaceflight has on the human body. These dangers include radiation which damages DNA and lack of gravity which leads to mineral and bone loss. Therefore it is quite astonishing how certain microorganisms can withstand these obstacles, in particular tardigrades. Also known as water bears or moss piglets, tardigrades are tiny and cute invertebrates which prefer to live in water. They are well known for cryptobiosis, Latin for “hidden life”. Cryptobiosis is when there are no signs of metabolic activity, but the organism is still alive. In this state these animals only maintain 0.01% of normal metabolic activity which lets them handle extreme environments. Tardigrades shrivel up and go into what is known as the tun state during this dormancy period. There are also variations of cryptobiosis that tardigrades partake in. For instance, tardigrades in the tun state could survive 125 years without water (anhydrobiosis).

Tardigrade space research began in 2007 with the NASA Foton-M3 Mission studying radiation’s effects on these tiny critters. In a 2019 study, tardigrades in the tun state accidentally crash landed on the moon! Now, is it likely that there is a colony of tardigrades on the moon since there is no water on the moon and those tardigrades arrived in the tun state? Well, a 2021 study investigated tardigrade survival in such high-speed crashes. It was found that moss piglets could survive the impact, but not the shock pressure withstood, so it is unlikely that there is a tardigrade colony on the moon.

Aside from cute water bears, there are also more general microbiome studies happening in space. Specifically, these studies depict how contact surfaces around the International Space Station (ISS) have changed in response to the astronauts who come and go on the Space Station. The microbiomes of crewmembers may influence the microbial composition of ISS habitable surfaces. This is important in managing disease control and preventing contaminants from breaking certain hardware on the ISS. It was found that an astronaut’s microbiome contributes to roughly 55% of the environmental surface microbiome. These findings were not startling, yet it was importantly confirmed that the majority of these were safe and typical bacteria that already exists on the skin. However some were classified as opportunistic pathogens. Opportunistic pathogens have the potential to cause disease, but are unlikely to do so when kept in check by other bacteria or if the person’s immune system is properly functioning. Furthermore, this microbiome snapshot was maintained for a few weeks even after the particular astronaut had left. However this micro-diversity encountered turnover when a new astronaut arrived at the space station and was in constant interplay.

Not only did the microbiomes on ISS surfaces change, even the astronauts who lived there had notable shifts in their own microbial environments. In particular there were 347 bacterial species identified. This varied based on sample sites from the saliva, ears, skin, and nostrils. There were 12 top genera with the highest relative abundance identified across these astronaut samples. Mainly differences were seen in skin samples when astronauts were in flight to and from the Space Station. In the mouth, there were some key, but minimal changes. For instance, saliva had the largest change in composition of bacteria but relative abundance (which is the overall number of bacterial species) stayed the same. One potentially concerning finding with the saliva organisms was that some of them sampled displayed antimicrobial resistant gene markers. The reasoning is unknown, but further research is ongoing.

The NASA Biomedical Engineering for Exploration Space Tech (BEEST) lab is researching health care for exploration. Their goal is to train astronauts in non-invasive treatment of dental cavities. Seminar attendees were also shown a light-hearted video on how astronauts brush their teeth in space. Something as simple as brushing their teeth is even more important in space. There has never been an astronaut who is a dentist, so having preventative care and training is important. Astronauts are even taught techniques up to tooth extraction.

A healthy microbiome is known to be in eubiosis while an unbalanced one is in dysbiosis. For example, the reason that too much sugar leads to tooth decay is because bacteria in the mouth feed on this excess sugar. It causes them to grow more and create more acid. This excess acid leads to a pH shift in which creates a habitable environment for more hardy bacteria to in turn create more acid. This acid also destroys tooth enamel. This leads to cavities and even more body-based diseases. For instance, the dangerous bacteria from the mouth can move through blood to other bodily systems and clog arteries. Maintaining oral hygiene is of utmost priority in space.

This human-microbial research ties back to Dr. Jennifer Kerr’s research at Notre Dame. She is an oral microbiologist and studies teeth in space. Her work centers around Streptococcus mutans. Her lab hopes to help astronauts identify a cavity and use a handheld microwave device to kill the bacteria. Astonighly when this gadget is held to the mouth for only a minute, 99% of the S. mutans are killed. However, in the case of a cavity, demineralization still remains. In order to remineralize it, the astronaut’s body needs to be given the appropriate starting material and the pH has to come back to neutral. This research is still ongoing, but it could have profound impacts, not only on human health in space but even on Earth. According to the Global Burden of Disease Study “oral diseases affect close to 3.5 billion people worldwide” which is why this research and its findings will be so consequential to the world of dentistry and the science and medical communities as a whole.

Co-sponsored by the Maryland Branch of the American Society for Microbiology.


Digital Equity in Baltimore – Building a Shared Future Together

Last fall, with the promise of historic federal and state investments for community recovery, among other important initiatives, Baltimore City announced its Digital Equity Framework – a plan to permanently close its digital divide within the next eight years. Free public Wi-Fi in outdoor community gathering places has been announced as part of the plan, as has connecting our Rec Centers. The grand vision is a municipally owned fiber infrastructure serving all locations in the city. But this ambitious goal won’t be met by technology solutions alone.

Chris Ritzo presented to us about how the city is beginning this work and we discussed the Internet, Wi-Fi, and the power of human networks and community based solutions to combat inequities.

The seminar began with some background information and definitions. The Digital Divide is the gap between those with access to engage online and it disproportionately affects minorities and prevents equal technological access Digital Equity, on the other hand, differs from equality in that it acknowledges systemic barriers in place to hinder others. Lastly Digital Inclusion aims to involve all communities even those most disadvantaged. In order to achieve Digital Inclusion, there are five pillars which must be fulfilled. These include: affordable internet services, access to digital training, quality tech support, access to internet devices, and participation collaboration in the internet sphere. With online school and the overall shift online due to the COVID-19 pandemic, the Divide Divide problem has been further exacerbated and solutions for it become even more pressing.

Throughout the seminar there was active discussion from participants. A question was raised by one attendee on why internet within the City is worse than outside of it, even from the same provider. Another proposed this is because it is harder to lay underground lines in the City. Furthermore, Chris spoke to the disconnect between marketing and engineering sides at companies which also contributes to these problems. Another person highlighted the recent news of a sexual assault that occurred in the Facebook Metaverse. The victim had received responses along the lines of “if you don’t like it then don’t join”. Chris mentioned how this current event ties back to the definition of Digital Inclusion and how moderating community norms is important in addition to creating these novel tools. He also suggested a book, Behind the Screen by Sarah Roberts, identifying the problematic issues of social media. Around the ongoing purchase of Twitter by Elon Musk, Ritzo highlighted Twitter and other social media networks’ claims to support free speech, but how they can never be truly utopian since the judgment lies within the corporation with an end goal of data mining and advertising to its users.

The seminar moved on to discuss technical aspects of getting access to the Internet. For instance, a router helps us connect wired/wirelessly to a laptop onto the Internet. This router also protects us and our data to some extent via firewall. First Mile is the idea of democratizing internet setup. In this concept, individuals can also set up Wi-Fi services, not just big corporations. The seminar also touched upon running speed tests. Chris said speed is important, but not the only thing that matters. It was recommended to survey nearby Wi-Fi channels, buy an extender, and understand the channel overlaps of your current network and where it should be. There were also online sites provided to see how crowded the channels are in your neighborhood. In the same vein, conduct basic latency tests, in particular latency under load also known as bufferbloat, to determine where your internet stands as it buffers in the case there is too much data.

Overall, data mechanics surrounding internet accessibility maintains a key driver in creating a community based solution to this problem. Science and technology will benefit greatly when there is contribution and inclusion in which there is equitable and diverse representation across the Internet.


Go with Your Guts!

Go with your guts, and the billions of bacteria that are in them!

Test your own personal microbiome (or your pet’s) – 

In this class you will be able to investigate your own gut microbiome, or the microbes of your pets, roommates, or family members. Our microbiome (the microbes living in and on our bodies) are believed to have profound effects on our immune systems, health, and susceptibility to disease. Find out what you can learn about your own health and wellness. In this class you’ll perform lab work to isolate the microbes present, sequence and identify those microbes, and then learn what the results mean for YOU personally.

In this 3 Saturday class you’ll be able to design your own experiment to compare any two samples! What samples do you want to compare? How many gut microbes you share with your dog? What about with another human? Does your gut microbiome change with your diet? What if you ate pizza for two weeks straight? (We do not endorse pizza as a sole source of nutrition!)

Isolate and process DNA from your samples. Use the polymerase chain reaction (PCR) to amplify the DNA of the microbes. Determine the different species of in your two samples. For those interested in programming and computational biology, the entire class will learn how to analyze the sequencing data and perform comparative analysis to uncover further information (don’t worry, this class is appropriate for beginners!).

Everyone gets two biological samples to test, here are some experimental suggestions. If none of these interest you, just ask us if you have another idea.

Some experimental suggestions:

1)      Test your microbiome vs. your roommate (or friend, or family member)’s microbiome *

2)      Test your microbiome vs. your pet **

3)      Test your microbiome before and after a lifestyle change such as a change in diet, exercise or sleep habits

4)      Test your pet microbiome before and after a change in your pet’s lifestyle

* Mandatory: you must get their consent

**Well, at least ask?



Computational Modeling with R

In the pursuit of knowledge in any field, a well-crafted  introduction is a key element for eventual mastery of the subject matter. Unfortunately, when it comes to learning a new computer language, this element is missing from all too many first encounters.  A failed initial coding experience, bedeviled by cryptic error messages for which no help is at hand, and perhaps accompanied by doubts that any useful application can be mastered in the near term, may be all that it takes to dash a beginner’s hopes and engender resistance to ever trying again. Happily for all concerned, BUGSS’s recent three-day course “Computational Modeling in Biology” followed a trajectory designed to ensure a successful learning experience. Led by Johns Hopkins Ph.D. candidate Wangui Mbuguiro and offered on three consecutive Saturdays, the course was structured as an introduction to building mathematical models in the R programming language for the analysis of biological data. In keeping with this design, the focus was not on an exhaustive study of R and all of its resources, but rather on how to employ some of the most powerful features of this versatile language to accomplish common tasks in biological research.

Week 1: Deterministic Modeling

In the opening session March 2, after guiding class members through the installation of R and the RStudio integrated development environment on their laptops, Ms. Mbuguiro presented an introduction to deterministic modeling. Each model considered was a mathematical explanation of a biological process of interest. “Deterministic” means that the output of the model depends solely on the precise values and conditions used as input for the model, and not on any variables that may have a random or other probabilistic distribution.  The class recreated deterministic models – expressed in R – for drug delivery via nanoparticles and for bacteria grown in culture. We also explored fitting functions to our data – that is, optimizing a model to best account for our data – using the least squares method. Rather than writing code from scratch, we began by modifying short segments of existing code provided in the development environment. This enabled us to avoid trivial mistakes and permitted the focus to remain on gaining experience using mathematical concepts expressed in R to study a biological system.

Week 2: Modeling growth rates

On the second Saturday, March 9, we explored using R for a common laboratory task: calculating the varying rate of change over time for a biological process involving a material of interest, and using the results to obtain a close estimate of the quantity of that material which is present at various time intervals.  The varying rates of change are values of an ordinary differential equation.  Using these values to obtain numerical approximations of the concentration of the material of interest at different  points in time can be especially useful when direct analytic evaluation is difficult. In particular, the class focused on modeling the growth rate of bacterial colonies.  We started with the essential relationship between the concentration of bacterial cells present and the growth rate of that cell concentration when there are no external constraints, such as limits on food supply. By calculating the rate of change in cell growth over very small changes in time using a species-specific growth rate constant and the concentration at the start of the time period, we were able to estimate the cell concentration at each incremental time point. When plotted, these cell concentration  calculations formed a smooth curve that revealed exponential growth over the time sequence.
Next we altered the model to be more reflective of real-world conditions. Relying on an equation developed by Nobel-Prize-winning French biochemist Jacques Monod, we created in R the code to calculate and chart both bacterial colony growth and the depletion of nutritive media over time. As in the previous model, the colony growth rate is dependent on the concentration of cells present at a given moment in time, but in this model the natural increase in cell concentration is tempered by the gradual depletion of the cells’ nutritional medium, or substrate, at a variable rate which also depends on the concentration of cells present. We again used R’s plotting package to graphically display the cell concentration N and for the substrate concentration S over time.

Week 3: Sensitivity Analysis

On the third Saturday, March 16, we conducted a sensitivity analysis on the Monod model of cell concentration increase and substrate depletion. Creating code in R for changing – one at a time –each relevant initial condition of the system and each rate-governing parameter, we explored the effect on the final outputs – cell concentration and substrate concentration – of a 10% change in each of the initial conditions and parameters. Looking at the effect of a 10% increase in the growth constant, we learned how to get a close estimate of the time required to reach half of the maximum cell concentration. As an illustration of how easily R can accommodate new functions to meet special needs, Ms. Mbuguiro wrote a “helper function” to find the position number within a sequence of time values of the particular value associated with a cell concentration that had reached 50% of the maximum. The calculation of all output changes driven by an incremental change of one input parameter is called a univariate sensitivity analysis.  Extending our exploration of R for standard statistical manipulations, we normalized the outputs of the entire sensitivity analysis – that is, we converted the change in each output from an absolute measure into a measure that is relative to the 10% change of the growth constant. As a last step, the class wrote the R code to create a graphical representation of the normalized output for multiple univariate sensitivity analyses, showing the effect of 10% changes in various parameters, considered one at a time, upon properties associated with cell concentration (N) and with substrate concentration (S). These properties include: Cmax, the maximum cell or substrate concentration; Thmax, the time required to arrive at one half of Cmax; and the Area under Curve (AUC), a measure of concentration over time that can be used to calculate average concentration during the time period. The area under the curve for the substrate concentration is often used in drug development research as a measure of “exposure” to the substrate. As an aid to visualization, the class made use of another R “helper function” contributed by Ms. Mbuguiro called output_calculator2, which works in concert with other R functions to produce the final output.
Although each Saturday session was four hours long, the time passed quickly as we alternated between discussion of applications and the production of error-free code. The classes were further enriched by discussion of work by Birgit Schoeberl[1] and Iraj Hosseini[2] demonstrating how synthetic biology techniques such as model optimization and sensitivity analysis can be used to design and implement drug therapies to treat cancer and HIV, respectively.  We also benefited from review of a textbook chapter by Raina Maier[3] that explains the utility of the Monod model and the mathematical tools of synthetic biology for the large-scale production of microbial products including antibiotics, yeast, and alcohol. By the end of the last day, we realized that we had been equipped with a powerful tool for setting up and running our own models. Yet we also knew that we had barely scratched the surface of the potential for using R to gain insight into biological data. We departed with gratitude for the collective learning experience, and eager to learn more. – Mark V.

About the Instructor

Wangui Mbuguiro is a Ph.D. candidate in the Biomedical Engineering Program at Johns Hopkins. Her research and passions center on engineering tools to better understand and treat menstrual disorders as part of the Computational Design of Therapeutics Lab. Outside of lab, Wangui enjoys encouraging scientific inquisition as an instructor and mentor at the Baltimore Underground Science Space, as well as building opportunities and community for underrepresented students in STEM at Johns Hopkins. Lastly, Wangui is a MIT alumna (B.S., Bioengineering, 2017), National Science Foundation Fellow, and friendly neighborhood scientist. You can connect with her on twitter (@WanguiMbuguiro) or LinkedIn.
[1] Schoeberl, B. et al., Systems biology driving drug development: from design to the clinical testing of the anti-ErbB3 antibody seribantumab (MM-121). npj Systems Biology and Applications (2017) 3, 16034; doi:10.1038/npjsba.2016.34; published online 5 January 2017. [2] Hosseini, I. and Mac Gabhann, F., Mechanistic Models Predict Efficacy of CCR5-Deficient Stem Cell Transplants in HIV Patient Populations. CPT Pharmacometrics Syst. Pharmacol. (2016) 5, 82–90; doi:10.1002/psp4.12059; published online 16 February 2016. [3] Maier, R.M., Bacterial Growth. In Environmental Microbiology (Maier, R.M., Pepper, I.L., and  Gerba, C.P., eds., 2nd ed., Academic Press, 2009), Ch. 3, p. 37-54. (

City Nature Challenge

CNC is a platform that brings together nature enthusiasts from around the globe.

Have you ever wondered-

What is that peculiar bug chilling out on my plant called? I planted tomatoes, but not this other plant. What is that? A colorful bird loves to stop by my bird feeder. I wonder what my new friend’s name is? If you answered yes to any (or all) of these questions, CNC is your chance to find some answers and to contribute to science while doing that.

Want more information?

Visit the City Nature Challenge site


SWEET Science: Responsible Bioengineering for Amateurs and Educators

A plant geneticist’s discussion on alternative methods of bioengineering.

Sebastion Cocioba discussed using sugars as a means of selection in molecular cloning and plant genetic engineering, removing antibiotics and herbicides from the equation entirely.

A plant geneticist’s discussion on alternative methods of bioengineering, Sebastion Cocioba discussed using sugars as a means of selection in molecular cloning and plant genetic engineering, removing antibiotics and herbicides from the equation entirely. He is a plant biotechnology researcher with a focus on the production of commercially and industrially valuable plant species. He is an owner of New York Botanics, LLC, a plant biotech R&D laboratory with a specialization in orchid micropropagation, a founder of Binomica Labs (, and a leader in the open science movement.

Follow him at:
Join the Petalsmiths Plant Engineering Research Party on Facebook:

Cocioba spoke to us from a converted bedroom turned microbiology lab space and discussed his informal thesis dissertation. Like many of our talks, Cocioba began his discussion on antibiotics. Antibiotics are chemicals that prevent bacterial growth. We can isolate and harvest these compounds to cure diseases. However, antibiotic dosage is key. Through diluted exposure, bacteria gain resistance and antibiotic therapies become antiquated. Additionally, there are many ways this resistance takes place, such as transduction (bacteriophage viral infection), conjugation (bacterial sex), and transformation (free-floating DNA pickup from the environment). These horizontal gene transfer methods are all ways that antibiotic resistance spreads aside from normal cell lineage passage.

It’s not all bad news though. We can use these genetic transfer methods to our advantage. For instance, transformation is the foundation for molecular cloning. Bacterial DNA comes in the form of circular fragments known as plasmids. Naturally occurring plasmids can be used to artificially recreate traits. Plasmids with genes of interest are put into the environment and uptaken by bacteria. The code of the plasmid then hijacks the bacterial machinery to produce our desired protein. However, there is never a 100% chance of plasmid uptake by the bacteria. We don’t want to move forward with an experiment without knowing that our bacteria has the desired result we are looking for, so how do we confirm this? This is where the initial discussion about antibiotics comes into play. Adding antibiotic resistance genes to these plasmids in addition to the gene of interest can be used to screen and confirm gene uptake. The media is laced with antibiotics. Any bacterial colonies that grow in the presence of antibiotics are the ones with successful plasmid uptake. We can harvest these cells for further analysis.

Coming from a home lab, Cocioba aimed to create a way to select for gene uptake without using antibiotics. Out of pure fortune, he landed on sugar gene research. Interestingly, lab strains of Escherichia coli cannot break down sucrose on their own. Cocioba developed a way to take advantage of this metabolization inability as a selection marker. He gave E. coli in his experiments the metabolic component to digest sucrose via a plasmid. Thus in theory when bacteria survive on sucrose media, it is because the cell is consuming the sugar. This means plasmid uptake was successful and the gene of interest is present as well. Cocioba replaced the antibiotic resistance gene on a plasmid with the gene to aid in sucrose breakdown, and it worked! He developed a way to screen for plasmid uptake into bacteria without using antibiotics.

In the past, archaea were thought to be the same as bacteria. Although both are single-celled organisms, archaea fall under a different category of life. However, we can still translate between the two. Haloarchaea is an extremophile, meaning it lives in extreme conditions. They cannot survive without high salt levels. Additionally, haloarchaea is incapable of metabolizing sucrose entirely. Cocioba applied the same bacterial screening solution to archaea. He encouraged plasmid uptake into haloarchaea, so that the specimen metabolizes sucrose in extremely salty environments. This means that our sample is pure due to the high salinity (no autoclave needed). They also use different machinery which hinders them from pathogenic transfer in a normal environment. Due to their intolerance for low saline environments, these cells will literally explode down the drain and allow for safe disposal. This is a great solution for DIY bio!

He then switched gears to discuss Agrobacterium tumefaciens, a soil bacteria normally found all over plants. He compared A. tumefaciens to a shark in the water. Whenever the bacteria sense “plant blood”, it springs into action. The bacterium locates the wound site and injects itself into the plant cell. Making its way to the nucleus, this bacteria begins to genetically engineer the plant. A. tumefaciens works in two ways. It programs the plant to create a carbohydrate that only the bacteria can use and to build a fortress around the bacteria protecting itself. A. tumefaciens infection can be pinpointed by the presence of “plant tumors”. Notably, genetically modified A. tumefaciens can input their DNA into a plant this way as a form of plant genetic engineering.

This brings us from the microscopic to the macroscopic scale ending with plants. Many plant cells can regenerate via shoots (somatic embryogenesis). But how do we screen out the transgenics that have our gene of interest from the ones that don’t after A. tumefaciens infection? We can apply the sugar-bacteria solution from before here for plants too. Mannose is a sugar that plants naturally cannot metabolize, but if they were given a supplemental gene, they can. We have the sugars for selection and the mechanism to get them into plants; now we just have to test it. Plasmid molecular cloning with the gene of interest is done in E. coli and Haloarchaea. Then these are transformed into A. tumefaciens. Swapping the sugar genes in between to ultimately prepare for plant infection. Further refinements to the process speed things along. Ruby betalain from beets is used as a potent pigment to differentiate transgenic plant tissue (bright red) from normal variations (lime green). Vanilla and other pantry staple spices agitate the plant cells to encourage A. tumefaciens infection. This whole process is organic but still transgenic and revolves around sugar all the way down.


Molecular Biology Bootcamp: Building a Kill Switch in Bacteria

This class taught the fundamental techniques of molecular biology (PCR, restriction digest, ligation, and transformation) by cloning a regulated version of the holin gene which can then be activated to destroy bacteria.

Check out more information about the course at this link!


Molecular Biology Bootcamp: Building a Kill Switch in Bacteria

This class taught the fundamental techniques of molecular biology (PCR, restriction digest, ligation, and transformation) by cloning a regulated version of the holin gene which can then be activated to destroy bacteria.

Week 1

This week we performed PCR and gel electrophoresis and discussed what kill switches are.


Week 2

This week we performed Gibson Assembly and bacterial transformation and discussed bacterial toxin/antitoxin systems.


Counting Shrimp with Sonar

Ever wonder where shrimp come from? Shrimp farming is harder than you might think! Agriculture and aquaculture farmers need to understand how many plants and animals they are growing on land or in the water to make decisions on their farms. For aqua-farmers, counting shrimp is a major challenge because their animals are grown in murky water and the farmers are blindfolded to how many shrimp they have. Minnowtech aims to help the farmers by counting their shrimp using sonar and doing the math behind their behavior, providing aqua-farmers with the information they need to manage their farms efficiently. In this seminar, Dr. Suzan Shahrestani of Minnowtech tells us how shrimp and other seafood get to your dinner plate, and how Minnowtech is striving to make that process easier for farmers.

Aquaculture is a fairly new field in farming. Dr. Shahrestani touched on the different types of aqua-farming from oysters, salmon, yellowtail, tilapia, and even seaweed as well as the sophisticated engineering solutions developed for each. Minnowtech aims to make aquaculture more efficient and sustainable through technology integration.

Dr. Shahrestani pointed out that other types of meat are inefficient in comparison to fish. This is because other forms of meat like chicken, pork, and beef require much more feed to just grow. Cows, in particular, eat the most. They also release the most energy before ending up on our plates. Ultimately, hamburgers are more expensive to the environment than fish sticks due to the increased methane output and feed required to raise the same amount of meat. Furthermore, fish use less energy by being buoyant in water as opposed to land animals which are weighed down further by gravity.

Energy efficiency isn’t the only benefit of aquaculture. For instance, aquaculture allows further growth in less space (i.e. vertical farming). However, aquaculture presents its own problems by being in the water. It is harder to calculate crop quantity in the water. In general, science on land is easier rather than in the water. Electronics and data collection in marine environments is a tricky area to navigate, but thanks to naval research (i.e. sonar radar) these technologies can be repurposed for commercial uses.

The challenge with shrimp farming is that hundreds of thousands of animals grow in turbid water. There is no way to see below the surface even with cameras to judge the quality and yield of their crop. Minnowtech’s solution is to use sonar devices to see into the murky water. The company’s initial deployment and testing started in Hawaii and the team has taken trips around the world to apply their solution to real-life situations. The majority of shrimp farming happens at backyard farms in Southeast Asia and Central America. Due to the small-scale nature of these farms, Minnowtech’s work is even more important and impactful for the lives of these farmers.

Dr. Shahrestani concluded her talk by touching on her dissertation research in which she studied counting jellyfish. Through the IMET Ratcliffe Environmental Entrepreneur Fellowship (REEF) program she transitioned her dissertation work to an aquaculture industry-level startup and co-founded Minnowtech. Countless prototypes ultimately led to Minnowtech’s BRS-1 which is now on the market and you can check out here: