iGEM 2024 — Team iit-madras

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{% block title %}Notebook{% endblock %}
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![](https://static.igem.wiki/teams/5155/mars.webp)

NOTEBOOK

* [Wet Lab](#section1)
* [Model](#section2)
* [Software](#section3)

# Wet Lab

# March

* Received 500 gm of Martian soil simulant from Prof. Sathyan Subbaiah at the Department of Mechanical Engineering, IIT Madras.

# April

## Week 1

### **Bacteria**

* Received a sample of 100 gm of calcium aluminosilicate from Astrra Chemicals, Chennai, India.
* Purchased *Pseudomonas fluorescens* from the Microbial Type Culture Collection (MTCC) of the Department of Biotechnology, Government of India.

## Week 2

### **Diatoms**

* Purchased *Phaeodactylum tricornutum* from the Culture Collection of Algae at the University of Göttingen.

## Week 3

### **Bacteria**

* We received freeze dried cultures of  *Pseudomonas fluorescens* from MTCC and revived the culture. The bacteria was grown at 30°C in a shaking incubator.
* Glycerol stocks were made and stored at -80°C.
* We plated the bacteria on LB plates with calcium aluminosilicate (CaAlSi) and observed the growth. However, we had difficulty dispersing CaAlSi evenly throughout the plate given its insoluble nature.

### **Diatoms**

* We finalized the composition of the Mann and Myers medium and the conditions required for growth and prepared the medium.
* We received the cultures of *P. tricornutum* from the Culture Collection of Algae and inoculated it in the Mann and Meyers medium as 2 separate samples - static condition and shaking incubator. We observed no growth under static conditions and decided to discard it.

## Week 4

### **Bacteria**

* We kept observing the colonies to check for solubilization zones around colonies. We could not observe solubilization zones.

### **Diatoms**

* We observed that the culture gets turbid but not brown, indicating unhealthy growth due to lack of photosynthetic pigments. So we decided to supplement the growth medium with vitamins: thiamine and cyanocobalamin to check if it rescues the pigment production.

# May

## Week 3

### **Diatoms**

* We suspected that the absence of biotin and inconsistencies with inoculation temperatures in our initial protocol was the reason we didn’t observe any diatom growth for several days.
* Due to unavailability of biotin, we tried searching for alternate growth media.

## Week 4

### **Diatoms**

* We found a paper on growth of freshwater diatoms in BG11 media that did not require biotin as an external supplement.
* We adapted the BG11 protocol by using microfiltered seawater instead of freshwater to mimic our diatom’s natural habitat and inoculated them in a 21°C incubator with a light intensity of 410 lux.
* Five days post inoculation, an OD of 0.14 was observed, aligning with a diatom growth study and confirming that the diatoms were thriving.

# June

## Week 1

### **Diatoms**

* We confirmed diatom growth at 27°C as well.

## Week 2

### **Bacteria**

* Learning from our previous experiment in April, we prepared a slurry of LB and agar with CaAlSi to spread the insoluble CaAlSi evenly across the plate.
* Despite seeing colony growth, we didn’t see a zone of clearance, hence couldn’t conclude if the bacteria were solubilizing Si.

## Week 3

### **Bacteria**

* We found a different protocol to test the bacterial solubilization of insoluble silicates in which dextrose agar was supplemented with silicates.We implemented it.
* No colony growth was observed.
* In another attempt, we melted the agar after making a slurry, then let it solidify again to see if it would spread evenly. We succeeded this time in even distribution of silicates.
* Yet again we couldn’t observe a zone of clearance and hence we had to look for better protocols.

## Week 4

### **Bacteria**

* We found a protocol which used a defined differential media - NBRISSM. We decided to swap the magnesium trisilicate suggested by the media with calcium aluminosilicate to suit the requirements of our studies.
* In order to make NBRISSM media as per the protocol, we prepared hydroxyapatite in our lab after consulting with experts from the Materials Science Lab at IIT Madras.
* We inoculated *P. fluorescens* in our modified NBRISSM media with different BCP (indicator) concentrations and placed it in a shaking incubator at 30°C and observed every 24 hours for any color change.
* We established an experimental protocol for inoculating *P. fluorescens* in the Martian soil simulant after consulting Prof. K Chandraraj, a soil microbiology expert at IIT Madras.
  We created a cell suspension of *P. fluorescens* in fresh LB and poured in martian soil simulant. The flask with inoculated soil was kept in a shaking incubator at 30°C. 1 gm of this soil was used the next day to count CFU. We noticed contamination while observing CFU.

# July

## Week 1

### **Bacteria**

* We brainstormed to refine experimental protocol for inoculating *P. fluorescens* in the Martian soil simulant to avoid any sources of contamination and we decided to autoclave all the materials again including spatulas used for taking out soil for CFU quantification.
* We redid the entire experiment of soil inoculation. We took 1 gm of soil and suspended it in sterile water. This was centrifuged and the supernatant was inoculated on LB plates every 24 hours followed by counting the colonies after 14 hours incubation for the entirety of the following week.
* We observed a color change in the silicate solubilization assay, confirming CaAlSi solubilization by bacteria.

### **Diatoms**

* We started with an experiment to compare diatom growth in presence and absence of silica. For that, we inoculated diatoms in 2 different media - minimal media with and without silicates and measured OD every 24 hours to compare the growth.
* We received the plasmids pPTbsr and pPtPuc3 from Addgene as stab cultures. After propagating the culture on appropriate antibiotics we made glycerol stocks and isolated plasmids from the culture.

## Week 2

### **Bacteria**

* After silicate solubilization was confirmed, we wanted to quantitatively estimate the amount of silica solubilized. For this we centrifuged soil after adding CaCl2 to extract soluble silicic acid.
* We then added tartaric acid, ANSA and ammonium molybdate according to the protocol and measured the absorbance after making silica standards.
* The results were inconsistent with the 0.2 gm/L silica standard showing more absorbance than the 0.8 gm/L silica standard.We decided to consult with a few professors about this and do some literature review on the same.
* In the soil growth experiment, we experimented with different volumes we could take for spread plating to see what gives the best number of colonies to count and we fixed the volume at 50 μL.

### **Diatoms**

* We continued to measure the OD of the diatoms in presence and absence of silica.
* We observed cell count and OD measurements every 24 hours in a separate subculture of the diatoms in order to establish a relationship between cell count and OD for the strain.
* As the OD results were inconsistent, we continued to discuss it with professors and got back to literature reviews to figure out the reason for such small OD values.

## Week 3

### **Bacteria**

* We continued the soil growth experiment.

### **Diatoms**

* We observed that diatoms initially grew similarly in silica-rich and silica-poor media, but showed significantly enhanced growth in the presence of silica in the stationary phase.

## Week 4

### **Diatoms**

* We reinoculated the diatoms in BG11 medium.
* The OD versus cell count calibration did not give a viable plot. We observed that this was due to variation in OD of sterile BG11 medium with time.

# August

## Week 1

### **Diatoms**

* There was no diatom growth in the reinoculated BG11 medium. We prepared the medium again and adjusted its pH to eliminate the turbidity and inoculated it.
* We designed Gibson assembly primers for 4abh and nhoA genes for acetaminophen synthesis. We received these genes from Twist Bioscience in 2023.

### **Bacteria**

* Learning from the previous mistakes we made while quantifying the soluble silicic acid, we attempted the experiment again.
* We used deionized water instead of autoclaved distilled water as the salts present in the distilled water may interfere with the experiment. Furthermore, as we were handling very small volumes, we decided to increase the volume of the reaction mixture to reduce errors. Since monomeric silicic acid is not stable, we prepared fresh standards. After making these changes, we attempted the experiment again but still got inconclusive results.

## Week 2

### **Diatoms**

* No growth was observed in the re-inoculum. To investigate potential factors affecting growth, we substituted the seawater in BG11 medium with saltwater and prepared BG11 medium with varying saltwater concentrations - 25%, 35% and inoculated them.
* We placed the order of Gibson assembly primers and received the order after 5 days. We created stock solutions for the primers.

## Week 3

### **Diatoms**

* No growth was observed in any of the inoculums. Considering that the stock culture used for inoculation might be unviable, we inoculated another flask of f/2 media from the primary culture.
* We set up a PCR reaction for amplifying the 4abh and nhoA genes. We did not receive any bands. We tried again with a higher concentration of the template DNA and received very faint bands.
* It was reasoned that the template DNA had degraded due to non-optimal storage. To test this we performed an electrophoresis on just the template DNA and received a smear, which confirmed our suspicion.

## Week 4

### **Diatoms**

* There was still no growth in any of the inoculum. We decided to supplement the f/2 media with a carbon source and prepared f/2 + glucose and f/2 + glycerol media supplemented with vitamins.
* We looked at alternate ways to get new template DNA and considered using different genes.

# September

## Week 1

### **Bacteria**

* We kept looking for alternatives and decided to use the genes available in the distribution kit to engineer *P. fluorescens*.
* We identified a genetic circuit and made preparations for using the kit for carrying out Golden Gate Assembly.

### **Diatoms**

* We inoculated f/2 + glucose, f/2 + glycerol, and BG11 media from the primary culture and incubated them at 21°C. After a few days, we shifted the cultures to a 27°C incubator.

## Week 2

### **Bacteria**

* We transformed *E. coli* DH5α cells with the parts and the destination vector using heat shock and inoculated them on the appropriate antibiotic plates.
* We grew the transformed colonies and isolated the plasmids from them. Plasmid concentration was measured using a Nanodrop spectrophotometer.
* We prepared *P. fluorescens* competent cells.

### **Diatoms**

* Growth was observed in BG11 and f/2 + glycerol media. We reinoculated fresh media with samples from both of them.

## Week 3

### **Bacteria**

* We finalized the protocol to be used for Golden Gate Assembly and procured the enzymes for it. We set up a 10 μl reaction and incubated in a thermocycler following the protocol. Following this, we transformed the assembly product into *E. coli* DH5α cells.
* Next day, we received several colonies, some of which were fluorescent (colonies with self-ligated vectors). We picked the non-fluorescing colonies and isolated our assembled plasmids.
* We decided to confirm the assembly success through restriction digestion. On running the electrophoresis, we observed the bands as expected.
* We decided to transform electrocompetent *P. fluorescens* cells with our assembled plasmids.

### **Co-Culture**

* In order to determine the salinity range of the co-culture medium for optimal bacterial growth, we prepared a setup involving soil inoculated with bacterial culture and minimal media with varying seawater concentrations.

## Week 4

### **Bacteria**

* After transforming *P. fluorescens*, we observed 6 colonies on one of the test plates, however there were 2 colonies on the negative control plate.
* To rule out faulty preparation of antibiotic plates, we made a new kanamycin plate. We picked the colonies and streaked on the new kanamycin plate.
* All the colonies from the negative control plate grew on the new plate. This prompted us to attribute the colony formation to contamination.
* We tried transformation again with new cuvette tubes and increased our precautionary measures. We received one colony on a transformant plate and no colonies on the control plate.
* We picked the colony and propagated it for further analysis.

### **Co-Culture**

* We observed good bacterial growth in all 4 salinities: 30%, 15%, 7.5% and 5%. We proceeded to set up a co-culture using minimal water in 100% seawater since it has the optimal salinity value for diatom growth.
* We quantified growth of bacteria and diatoms in the co-culture setup by measuring the OD and compared it with growth of bacteria and diatoms in separate cultures. We found that both organisms were growing better in co-culture.

# Model

# March

* **GSMM Integration**: Successfully loaded the genome scale metabolic model of the diatom into COBRApy, enabling computational metabolic modeling.
* **Literature Review and Flux Balance Analysis (FBA)**: Conducted a comprehensive review of biochemical pathways and reaction mechanisms, followed by basic FBA to evaluate the organism's growth and metabolic flux distribution.
* **GSMM Modification**: We investigated strategies for incorporating the new genes 4abh and nhoA, and the newly synthesized metabolites 4-aminophenol and acetaminophen, into the genome scale metabolic model to expand its functional capabilities.
* **FBA Validation**: Performed FBA to assess changes in flux distributions and metabolic efficiency post-introduction of the acetaminophen synthesis reactions.

# April

* **Literature Review**: Started literature review on adding side reactions to the GSMM.
* **BLAST Search and Enzyme Identification**: To incorporate the side reactions, we identified candidate enzymes in the diatom via Protein BLAST. Based on this, we added 4 side reactions to the model.
* **Reaction Addition and Flux Issues**: Incorporated a secretion reaction for acetaminophen, but encountered issues generating the correct flux. Added all the exchange and demand reactions correctly and showed the flow of metabolites.

# May

* **Model Stabilization and Feedback**: Fixed side reactions and achieved stable steady-state flux. Met with Dr. Karthik Raman, our PI and an expert in computational systems biology, for insights into the next steps. Through this, we identified an issue with the biomass reaction.
* **Model Replication and Comparison**: We integrated the acetaminophen synthesis reactions into an *E. coli* model to compare with the fluxes we were getting in the diatom.
* **Basic Dynamic Model Development**: Started working on the dynamic model for silica solubilization and built a basic network model of the silica solubilizing bacteria using SimBiology.

# June

* **GSMM Analysis**: For the GSMM we integrated multiple analysis tools, including PFBA, MOMA, ROOM, FVA, GFBA, analysis of growth medium, gene knockouts, FSEOF, and MetQuest.
* **Escher**: Identified a problem where the metabolic network plotting software Escher only displayed the *E. coli* model for any input GSMM. Explored a solution involving manual addition of reactions.
* **Parameter Optimization**: Determined rate parameters for the silica solubilization model.
* **Community Modeling**: Started looking into MICOM and its potential to understand how the co-culture of bacteria and diatoms would behave.

# July

* **GSMM Refinement**: Discovered a missing NH₃ exchange reaction in the existing GSMM and worked on addressing this gap. The addition of the reaction changed the fluxes quite a bit, proving that ammonia is an important metabolite in the diatom’s pathways.
* **Flux Analysis**: Worked on various plots to optimize biomass and acetaminophen production. Analyzed reactions using PFBA, MOMA, and ROOM approaches to generate comparative flux plots.
* **Escher**: Attempted to map key pathways involving amino acids biosynthesis (chorismate) using KEGG and Escher. Faced issues with visual clarity due to map complexity.
* **Silica Solubilization Dynamic Modeling**: Conducted a detailed analysis of silica solubilization. Documented internal and external metabolite concentrations. Examined pH variation over time at different intervals (Hour 1, Hour 10, Hour 60). Investigated how these parameters interact within the reaction network.
* **MICOM Tool Discussion**: Approached Dr. Maziya Ibrahim in the Computational Systems Biology Lab, IITM, to discuss MICOM for co-culture network modeling. Key points included adjusting growth medium bounds, cooperative tradeoff, flux generation, and optimization steps. Started modifications to GSMMs based on these insights.

# August

* **Limonene Production**: We modeled the production of limonene in Pseudomonas using constraint-based modeling and its associated analysis tools.
* **MICOM**: Completed modeling workflows and generated plots.

# September

* **Biological Interpretation of Modeling Results**: Made key inferences from the models to improve growth and product yields.

# Software

# April

* Discussed potential problem statements for a software with applications in space synthetic biology.
* Identified, based on our experience coming up with our wet lab project, that chassis selection is difficult for achieving in situ resource utilization.
* We decided to try and map functional groups present in compounds to enzymes, so that if we had a list of enzymes present in an organism, we could identify which compounds would react with these enzymes and be a part of the metabolism of the organism.
* Decided to use SMARTS and SMILES notation as possible methods of input for functional groups to our program.
* Explored RDkit , an open-source toolkit for cheminformatics, to try to convert between various formats of input.
* Implemented the RDkit library in Python to parse molecules in SMARTS and SMILES notations.

# May

* Conducted review of the EC number notation.
* For each sub-subclass, we tried to write the corresponding functional group in SMARTS notation.
* Decided to abandon this method, as there were too many functional groups to compile manually. Each sub-subclass had multiple functional groups under it, and they would each react with different metabolites.
* Ideated on other methods of obtaining a list of chassis.
* Explored various databases such as BioCyc, MetaCyc, RCSB PDB, Uniprot and KEGG.
* Manually conducted literature review of papers concerning biomining and bioleaching, to identify useful reactions that might not be present in the databases.
* Realized the need for rational scoring of chassis and ideated on structure of scoring function.

# June

* Continued to conduct literature review, and also try to find various other databases we could use for metabolite searches, such as omnicrobe.
* Literature review for physiological conditions preferred by organisms was conducted, to no avail.
* Tried to create a workflow to find the organism through a compound in BioCyc. Ran into issues, as BioCyc is not a database for organisms, and only lists organisms as possessing a pathway if there has been extensive documentation and research performed.
* KEGG tested different pathways, found compound → enzyme → gene → organism to be best.
* Performed literature review to arrive at parameters for scoring function.
* Tried to get a list of species from the identified pathway from the BioCyc website.
* Looked into a github module that integrates BioCyc’s functionality into Python.
* Improved preprocessing, implemented batching requests in KEGG, approx 2 min per compounds search.
* Explored implementation of resourcefulness and survivability parameters, arrived at score for temperature and pH.

# July

* Installed the BioCyc GitHub module in Python and tested it out.
* Tried to construct a rough workflow to find the list of organisms from an input in BioCyc.
* Looked into using BRENDA to complement KEGG results at enzyme stage.
* Set up local lookup table, implemented multiprocessing in KEGG, approx 30s-1min per compound search.
* Explored implementation of engineeribility parameter, found it hard to define and implement.
* Looked into possible inputs we could provide to the BioCyc code. BioCyc ID was the one compatible with the github library, but it eliminated the input of synonyms as entries.
* Looked into how ‘Reactions’ and ‘Pathways’ were defined, and what characteristics they had as outputs by using the github library for BioCyc.
* Tried to set up BRENDA SOAP API (and failed).
* Switched to CSV download functionality on BRENDA instead of messing about with SOAP API.
* Started Django implementation of input forms for the web tool of Astrolabe.

# August

* Implemented code to get a list of reactions from a given BioCyc ID. Also implemented code to get a list of pathways from the list of reactions.
* Integrated BRENDA and KEGG code for more comprehensive searches.
* Ideation on extracting culture media details from BacDive.
* Finished input functional version of the Django-based web tool.
* Tried to implement the library ourselves by using the requests library and directly using BioCyc URLs. There were issues due to the BioCyc database being accessible when on a paid account, so we uploaded our credentials as a requests session.
* Noticed numerous references to MediaDive via BacDive, so set up MediaDive API.
* Figured out pathways to integrate MediaDive to get culture details.
* Implemented design aesthetics in the Django web tool for Astrolabe.

# September

* Continued to implement by directly using URLs from BioCyc.
* Implemented a section of code to convert ChEBI IDs to BioCyc IDs for uniform inputs.
* Set up MediaDive code to obtain medium composition and pH.
* Integrated BacDive and MediaDive code to get culture info from MediaDive when referenced in BacDive.
* Implemented scoring function to score organism results from each database individually.
* Integrated with scoring function and other API’s to complete the software integration for the Astrolabe web tool.
* Finished implementing the BioCyc code. Started writing content for the wiki.
* Implemented a function to remove those organisms belonging to ‘Metazoa’ and ‘Embryophyta’, to eliminate multicellular organisms from the kingdoms Plantae and Animalia in the BioCyc code.
* Modularized BacDive and MediaDive code, split everything into files and set up proper libraries and pathways.
* Integrated all databases to parallelly generate organism lists to be passed to the scoring function.
* Purchased remote server for Astrolabe web tool hosting via a free duckdns domain. Configured the server and hosted the tool on the internet.

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![](https://static.igem.wiki/teams/5155/mars.webp)

ENGINEERING
SUCCESS

* [Wet Lab](#section1)
* [Model](#section2)
* [Software](#section3)

# Wet Lab

### Qualitative assays for silicate solubilization

Iteration 1

Iteration 2

|  |  |
| --- | --- |
| **Design** | Silicates in the Martian regolith are majorly in the form of anorthosite, a calcium-rich plagioclase feldspar. It is essentially a calcium aluminosilicate. Through existing literature we figured out that silicate solubilizing bacteria show differential activity on different silicate minerals [[1]](#refer). Hence, it was imperative to show that our bacteria of choice   *Pseudomonas fluorescens*  shows solubilizing activity specifically with calcium aluminosilicate. We modified the protocol used in [[1]](#refer), where they used Burnt and Rovira medium substituted with agar and 0.25% calcium aluminosilicate. Due to unavailability of the soil extract required for the Burnt and Rovira medium, we decided to use LB medium. We also decided to try a protocol using dextrose agar substituted with silicates [[2]](#refer). |
| **Build** | 0.25 grams of calcium aluminosilicate was added as a slurry to LB agar and dextrose agar prior to pouring into plates. We found a heating-cooling-heating method that helped in effectively dispersing the insoluble silicate. A 1000x diluted overnight liquid culture was used for inoculation using a spread-plating method. |
| **Test** | As suggested in the protocol, the plates were incubated for 3-5 days. After a few days we noticed some color change around the colonies in the LB plates, the area around the colonies appeared less cloudy but the appearance of conclusive clearance zones couldn’t be established with certainty. No colonies grew on dextrose agar. ![](https://static.igem.wiki/teams/5155/engineering/1.webp) |
| **Learn** | We learnt that our  *P. fluorescens* strain cannot grow on dextrose agar. Going further, we decided to choose a method that could show more conclusive evidence of silicate solubilization. |

|  |  |
| --- | --- |
| **Design** | In bacteria, both insoluble phosphate and silicate solubilization have a common mechanism - organic acid production. We decided to use a modified version of NBRISSM - a defined differential liquid media used for screening silicon solubilizers [[3]](#refer) . This media has been designed to ensure little to no interference from phosphate solubilization which ensures no false positive results. The indicator bromocresol purple (BCP) is added to obtain qualitative results. This indicator changes color from purple to yellow at pH 5.2. The pH change is due to the formation of silicic acid which makes the media change color from purple to yellow. The original composition uses magnesium trisilicate as the silicate source. However, given our context, we found it best to replace it with calcium aluminosilicate. |
| **Build** | We prepared the media as per the defined composition and replaced magnesium trisilicate with an equal amount of calcium aluminosilicate. The pH was adjusted to 7 before autoclaving. Bacterial inoculations were made at approximately 1–2 × 109 CFU ml−1. Uninoculated media served as a bacterial control whereas inoculated media without Si source acted as silica control. The culture tubes were kept at 30℃ at 250 rpm. |
| **Test** | We observed the tubes every 24 hours for 7 days and noted the color change. The color started changing after 4 days and became bright yellow by the 7th day. The color in both the control tubes remained purple, indicating a lack of solubilization in the control tubes. See our results [here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D). |
| **Learn** | The color change was purely due to bacteria’s ability to solubilize silicon. As no color change was observed in tubes containing bacteria but no silicon, the possibility of bacterial growth itself lowering the pH and thus causing a color change can be ruled out. We can conclude that  *P. fluorescens*can solubilize calcium aluminosilicate. Going further, we decided to prove its solubilizing activity on the calcium aluminosilicate present in our Martian soil simulant. |

### Growth of bacteria in Martian soil simulant

|  |  |
| --- | --- |
| **Design** | Before testing out  *P. fluorescens* ’s ability to solubilize the calcium aluminosilicate present in the Martian soil simulant (shown below), we needed to first establish a way to grow the bacteria in an otherwise non-life supporting environment. Following inoculation in soil, we needed to measure growth in the soil as well. ![](https://static.igem.wiki/teams/5155/engineering/2.webp) |
| **Build** | After discussions with Dr. K Chandraraj, an expert in soil microbiology, we decided to use a cell suspension in fresh LB media to directly pour onto the soil and observe the bacterial growth. |
| **Test** | Everyday for 10 days, 1 gm of soil was mixed with sterile water and centrifuged. The supernatant was used for counting CFU. We observed an oscillatory growth pattern over the course of 10 days.  See our results [here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D). |
| **Learn** | This result is consistent with the findings of previous work that has shown the wave-like growth pattern of metabolically active cells to be a natural phenomenon associated with the cycles of growth and death in soil microbial communities [[4]](#refer) . Thus, we have established a method to propagate *P. fluorescens*  in the Martian soil simulant in a way that mimics the natural growth patterns of bacteria in soil. |

### Quantitative assay for silicate solubilization in soil

Iteration 1

Iteration 2

|  |  |
| --- | --- |
| **Design** | After having established a way to grow the bacteria in the Martian soil simulant, it was time to test its ability to generate solubilized silicates from the simulant. We followed a colorimetric method for quantifying silicate ions in solution. This meant we needed to extract the solubilized silicates from soil before the analysis. The solubilized silicates in soil can be extracted using a CaCl2 solution [[5]](#refer). We chose this over other extractants because it was reported that CaCl2 and distilled water extracted readily soluble Si whereas other extractants either dissolved some exchangeable Si or specifically adsorbed Si.  After extraction, the obtained silicate solution is diluted 41-fold. This is done to inhibit the formation of silica polymers as monomeric silica can quickly form polymers in high concentrations [[6]](#refer). The diluted silicate ions in solution can be colorimetrically quantified using the silico-molybdate method. In acidic conditions, soluble silica reacts with molybdate ions to form a yellow complex (heteropoly acid), the intensity of which is directly proportional to the concentration of silicate ions. This complex when reduced turns to a bright blue color, the absorbance of which can be detected by a spectrophotometer at 630 nm [[7]](#refer).  Phosphate ions create an interference in this test because they also form a yellow complex with molybdate. To eliminate phosphate interference, the solution is treated with tartaric acid. |
| **Build** | We extracted silicates from the soil on which bacterial growth had been characterized. We also extracted silicates from uninoculated soil (control). The extracted solutions, the blank as well as silica standards were then subjected to addition of concentrated HCl to make the environment acidic. This was followed by addition of molybdate and tartaric acid. Finally, a reducing solution containing sodium sulfite, sodium bisulfite and ANSA (1-amino-2-naphthol-4-sulfonic acid) was added to the solution. |
| **Test** | The absorbance for all the silica standards and the test sample was measured at 630 nm. We received very erratic values for standards which made it difficult to plot a linear curve. |
| **Learn** | After going back to literature, we learnt that simply creating an acidic environment is not enough , the pH strictly needs to be maintained below 2.5. Additionally, we realized that we couldn’t use distilled water; we had to use deionized water for the reaction [[6]](#refer). |

|  |  |
| --- | --- |
| **Design** | We decided to increase our reaction volumes to minimize errors from pipetting small volumes and measure the pH before proceeding with the reaction. |
| **Build** | We used a pH paper to make sure the pH was below 2.5 and used deionized water. Additionally, we made sure no glassware was used. All the other steps were followed as done previously. |
| **Test** | We again received erratic absorbance values that couldn’t be used for plotting a linear graph. Qualitatively, the absorbance of our test sample was more than control. |
| **Learn** | On consulting Dr. Deepa Khushalani, an expert in the chemistry of silicon compounds, we were told that silicate estimation in solution can be very tricky due to the high tendency of polymer formation of monomeric silicates. Moreover, the time delay between the formation of silicates by bacteria and our quantification experiment can be a limiting factor for correct estimation as silicates quickly form polymers. We were prompted to think of other correlation establishing experiments. Therefore, going ahead we decided that co-relating an increase in diatom growth rate with bacterial presence in soil would be the best way for us to show our proof of concept. |

### Preparation of BioBricks

|  |  |
| --- | --- |
| **Design** | We decided to engineer a limonene synthesis pathway into our chassis*Pseudomonas fluorescens*. This bacteria naturally produces GPP (geranyl pyrophosphate) which can be acted upon by the enzyme 4(S)-limonene synthase to produce 4(S)-limonene. The aim of our engineering, thus, was to introduce a constitutive production of the enzyme 4(S)-limonene synthase. We chose the constitutive promoter [BBa\_J23100](https://parts.igem.org/Part%3ABBa_J23100) and the RBS [BBa\_J428038](https://parts.igem.org/Part%3ABBa_J428038) as they have been extensively used and well documented for reliable expression. Our coding sequence was 4(S)-limonene synthase and the terminator was chosen to be [BBa\_J428092](https://parts.igem.org/Part%3ABBa_J428092).  Our destination vector was [pJUMP24-1A(sfGFP)](https://www.addgene.org/126971/), a high copy number *E. coli*-*Pseudomonas* shuttle vector with an sfGFP dropout marker.   ![](https://static.igem.wiki/teams/5155/engineering/3.webp) |
| **Build** | All the BioBricks were taken from the iGEM Distribution Kit 2024’s well plates as described in the handbook. We transformed the BioBricks into competent *E. coli*DH5α cells, and grew them on appropriate antibiotic plates. We had negative control plates for each antibiotic on which untransformed bacteria were inoculated. |
| **Test** | There were colonies for all the BioBricks. The colonies containing our destination vector were green indicating the sfGFP production. We observed no colonies on control plates. ![](https://static.igem.wiki/teams/5155/engineering/4.webp) |
| **Learn** | Our transformation was successful and the colonies could be used for miniprep to isolate the plasmids. |

# Model

### Nitrate in Diatom Media

Iteration 1

Iteration 2

|  |  |
| --- | --- |
| **Design** | The initial metabolic model for *Phaeodactylum tricornutum* was designed based on a medium containing very low traces of nitrate. We assumed that this would not significantly affect the metabolic fluxes and proceeded with this assumption in our analyses, focusing on pathways related to biomass and acetaminophen production. |
| **Build** | Using this model, we generated various plots and performed a series of analyses, making several inferences about key metabolic pathways, particularly those involved in biomass synthesis and acetaminophen production. |
| **Test** | However, we found that the growth rates were not consistent with what we were observing in the wet lab. |
| **Learn** | After conducting a thorough literature review, we realized that nitrate content plays a critical role in influencing metabolic fluxes, including those central to both biomass and acetaminophen production. |

|  |  |
| --- | --- |
| **Design** | This discrepancy between the low-nitrate model and the actual conditions in wet lab experiments indicated the need for a more accurate representation of the medium. |
| **Build** | In response, we modified the model to simulate a medium with higher nitrate content, closely resembling the conditions used in wet lab experiments. |
| **Test** | After rerunning the analyses with this updated medium, we generated new plots. |
| **Learn** | This produced more accurate results and improved our understanding of the nitrate’s impact on the metabolic pathways. |

### ***Pseudomonas*****Modeling**

Iteration 1

Iteration 2

|  |  |
| --- | --- |
| **Design** | The initial metabolic model for *Phaeodactylum tricornutum* was designed based on a medium containing very low traces of nitrate. We assumed that this would not significantly affect the metabolic fluxes and proceeded with this assumption in our analyses, focusing on pathways related to biomass and acetaminophen production. |
| **Build** | Using this model, we generated various plots and performed a series of analyses, making several inferences about key metabolic pathways, particularly those involved in biomass synthesis and acetaminophen production. |
| **Test** | However, we found that the growth rates were not consistent with what we were observing in the wet lab. |
| **Learn** | After conducting a thorough literature review, we realized that nitrate content plays a critical role in influencing metabolic fluxes, including those central to both biomass and acetaminophen production. |

|  |  |
| --- | --- |
| **Design** | To model the silicate solubilization by *Pseudomonas fluorescens*, we decided to use organic acid production as an indicator of solubilization. This was done based on our literature review into the mechanisms of solubilization. |
| **Build** | We attempted to incorporate a reaction for silicate solubilization involving gluconic acid into an existing , which lacks a GSMM. The initial plan was to modify the model by adding this reaction and analyzing its effects. |
| **Test** | On testing the model, we found that the model we used was incomplete, as the developer had not included the metabolite names, only their IDs. This became evident when we attempted to add the silicate solubilization reaction and could not identify gluconic acid. |
| **Learn** | To overcome this limitation, we searched for a more complete model that included all metabolite names. |

### **Escher**

Iteration 1

Iteration 2

|  |  |
| --- | --- |
| **Design** | We aimed to visualize the modified diatom metabolic pathways by uploading the SBML file of the diatom’s GSMM to the Escher website. This tool was chosen for its ability to automatically display metabolic pathways based on the provided model, allowing us to analyze the modifications in a streamlined way. |
| **Build** | After uploading the SBML file, the website generated pathways that initially appeared accurate. However, upon closer inspection, we discovered that the pathways displayed were from *E. coli* rather than the diatom model. |
| **Test** | We attempted various troubleshooting methods to resolve the discrepancy but were unable to determine why the website defaulted to *E. coli*  pathways, despite providing the correct diatom SBML file. |
| **Learn** | As a result, we turned to a manual approach to create the required visualizations. |

|  |  |
| --- | --- |
| **Design** | We found that the Escher Python library would allow us to create our own plots. |
| **Build** | We constructed the pathways using the Escher Python library. |
| **Test** | Although this process was labor-intensive, it ultimately yielded accurate pathway visualizations. |
| **Learn** | This allowed us to make key inferences about the diatom’s metabolic modifications. |

### **SimBiology Network Development**

Iteration 1

Iteration 2

|  |  |
| --- | --- |
| **Design** | A network model for silicate solubilization was designed using organic acid production, silicon uptake, and solubilization occurred, assuming all processes happened within the cell. |
| **Build** | The initial model was built in SimBiology to simulate a single compartment where all reactions occurred. |
| **Test** | We ran tests using this model, but the silicate levels were significantly overestimated in the simulation. |
| **Learn** | Upon further literature review, we discovered that organic acids are secreted outside the cell, where the actual solubilization takes place. |

|  |  |
| --- | --- |
| **Design** | Our initial assumption of internal solubilization likely caused the misrepresentation of silicate values. |
| **Build** | To correct this, we switched to a two-compartment model that better reflects real-life conditions, distinguishing between intracellular processes and extracellular solubilization. |
| **Test** | We tested our new two-compartment model in SimBiology. |
| **Learn** | This resulted in more accurate silicate flux values. |

### **SimBiology Rate Constants**

|  |  |
| --- | --- |
| **Design** | The first model incorporated filler rates based on preliminary literature review, leading to the use of rate values that were neither well-calculated estimates nor accurately sourced from previous studies. |
| **Build** | Recognizing this limitation, we focused on gathering more reliable data and incorporated rate constants for well-established reactions, such as gluconolactone oxidation. |
| **Test** | While testing the new model, we made informed estimates of missing parameters by analyzing the model’s output graphs and data. |
| **Learn** | Using the updated model, we identified certain reactions as enzyme-driven processes, applying Michaelis-Menten kinetics to enhance the model's accuracy. These improvements led to a more robust and reliable model, with accurately defined rate constants that better reflect the biochemical processes involved. |

### **Goal of Dynamic Modeling**

|  |  |
| --- | --- |
| **Design** | Our initial project goal was to provide the wet lab with precise numerical values for the time required for silicon solubilization and the saturation point of solubilization, measured in mmol of silicic acid. |
| **Build** | We designed a model to simulate these processes based on available data and assumed we could make accurate, quantitative predictions. |
| **Test** | As we began constructing the simulation, we quickly encountered a data crunch - there was insufficient usable information available about certain pathways and processes related to silicate solubilization. This lack of data made it impossible to build a one-to-one simulation that accurately reflected real-world conditions. |
| **Learn** | In response, we shifted our focus from quantitative outputs to qualitative insights. Specifically, we designed experiments to investigate how solubilization changes with increasing pH and to determine how solubilization plateaus over time and at specific pH levels. This pivot allowed us to gather valuable qualitative insights into the solubilization process. We were able to enhance our understanding of the relationship between pH and silica solubilization, even without precise numerical data, by observing trends and plateaus in the solubilization process. This learning phase provided critical insights that can inform future experiments and model improvements. |

### **Community Modeling**

Iteration 1

Iteration 2

|  |  |
| --- | --- |
| **Design** | We designed a simulation of the bacteria and diatoms in co-culture using the MICOM framework. |
| **Build** | We constructed a community model using the same external media as the diatom’s GSMM. |
| **Test** | The initial growth media used in the community model did not yield any fluxes for biomass, resulting in a growth rate of zero. |
| **Learn** | After conducting a literature review, we discovered that both the bacteria and diatom require specific molecules like magnesium and urea in the community model’s media to support their growth. |

|  |  |
| --- | --- |
| **Design** | We identified the molecules to be added to the community media. |
| **Build** | We incorporated these essential molecules into the growth media, anticipating that this change would enhance the model's performance. |
| **Test** | Following the adjustments, we observed non zero growth and fluxes for both the diatom and the bacteria. |
| **Learn** | This indicated that the inclusion of the necessary growth factors was successful in facilitating their metabolic activities. |

# Software

### BioCyc

Iteration 1

Iteration 2

Iteration 3

|  |  |
| --- | --- |
| **Design** | We envisioned a tool that would take a ChEBI ID as input and return organisms capable of metabolizing the resource using data from BioCyc. |
| **Build** | We utilized Python's requests module and a third-party GitHub library to query BioCyc for metabolic pathways and organism data. |
| **Test** | The tool inconsistently returned results due to issues with the external library and BioCyc's access restrictions. |
| **Learn** | Relying on third-party libraries is inherently risky, and we needed a custom solution to avoid access limitations and improve reliability. |

|  |  |
| --- | --- |
| **Design** | We planned to replace the third-party library with a custom querying system and create a session-based login for BioCyc’s paid access. |
| **Build** | We developed our own Python-based solution to directly query BioCyc and established a session-based login to handle access restrictions. |
| **Test** | The custom solution consistently retrieved data and successfully returned organisms associated with the inputted ChEBI IDs. |
| **Learn** | Building a custom querying solution improved reliability, but further filtering was needed to focus on relevant chassis organisms. This also highlighted the need for proper error handling and made it clear that we had to provide a simpler to use interface than a command-line environment to widen usability for users of varying technical expertise. |

|  |  |
| --- | --- |
| **Design** | We aimed to refine the tool by filtering out non-microbial organisms, focusing on bacteria, archaea, and microbes suitable for synthetic biology. |
| **Build** | We implemented a filtering algorithm that excluded plants and animals by parsing taxonomic data for each organism. |
| **Test** | The refined tool returned only microbial chassis, improving the relevance of results for synthetic biology applications. |
| **Learn** | Improved filtering enhanced organism selection, and we recognized the opportunity to expand the tool to support product-based biomanufacturing in the future. |

### UniProt

Iteration 1

Iteration 2

Iteration 3

|  |  |
| --- | --- |
| **Design** | Started with understanding basic API querying in Python using requests. The goal was to learn how to send a GET request to UniProt's API with a specific query, retrieve JSON data, and handle basic filtering for reviewed bacterial organisms. |
| **Build** | Wrote a simple Python code to query the UniProt API using the base URL and a basic EC number or ChEBI compound index. Used requests.get() to retrieve data and json() to parse it. |
| **Test** | Run the code to check if the API request returns the desired data. With proper error handling for unsuccessful requests (e.g., wrong URLs, no internet connection). |
| **Learn** | Gained familiarity with the requests library, how to make API calls, and extract basic information from the response. |

|  |  |
| --- | --- |
| **Design** | Extended functionality to filter duplicate organisms by their scientific name, count occurrences, and sort by frequency. Aim to avoid multiple results for the same organism and focus on the most frequently appearing organisms. |
| **Build** | Implemented a Python function to filter duplicates based on scientific names, count occurrences, and store organisms in a dictionary. |
| **Test** | Confirmed that the function successfully removes duplicates, counts the number of occurrences, and sorts them by frequency. |
| **Learn** | Refined understanding of working with dictionaries and list comprehensions in Python. Developed logic to aggregate and sort data. |

|  |  |
| --- | --- |
| **Design** | Added more advanced functionality, such as exporting filtered results to a JSON file and only printing organisms with high annotation scores (≥ 3). The goal is to integrate data output for further downstream analysis. |
| **Build** | Used Python’s json library to write results to a file and filter based on annotation score. |
| **Test** | Verify that the filtered results are correctly exported as JSON and that the printed results match the annotation score condition. |
| **Learn** | Improve file handling, learn more about data serialization (JSON), and understand how to perform deeper filtering on the returned data. |

### KEGG and BRENDA

Iteration 1

Iteration 2

Iteration 3

Iteration 4

|  |  |
| --- | --- |
| **Design** | Tasked with implementing compound querying via KEGG’s REST API, a new experience for us, we had to learn how to structure queries, communicate with web APIs in Python, and retrieve relevant data for the compounds we were interested in. |
| **Build** | Using Python, we implemented the API calls and successfully set up basic compound queries, which returned data on compound-enzyme relationships. We also explored how to use SMILES notation for querying compounds to bridge communication between multiple databases. |
| **Test** | The queries worked well for a limited set of compounds. However, the pathway to translate compounds to organisms was unclear. This led to delays in getting from compound to organism. |
| **Learn** | We realized we had to take a deeper dive into KEGG’s architecture to understand how its databases interact. Additionally, SMILES notation proved to be a valuable tool for cross-database compatibility, and we would leverage this knowledge later in the project. |

|  |  |
| --- | --- |
| **Design** | Initially, we were exploring the compound → enzyme → reaction pathway → organism approach. However, navigating KEGG’s BRITE hierarchy through its API became an obstacle due to the complexity of its reaction pathway structure. |
| **Build** | We shifted our focus to a new relationship chain: compound → enzyme → gene → organism. This was more streamlined and bypassed the problematic BRITE hierarchy. We implemented this new approach using KEGG’s API and tested it on a few compounds. |
| **Test** | While the new approach worked, it was too slow. Each compound query took approximately 4 minutes, which was unacceptable for the expected 10-20 compounds per run. Query bottlenecks became apparent during the gene-to-organism step, where KEGG’s limits on API calls significantly slowed progress. |
| **Learn** | We learned that while the compound → gene → organism pathway was optimal in terms of data retrieval, we needed to optimize our queries to improve performance. We also realized that BRITE’s complex hierarchy should be avoided, especially for large-scale queries. |

|  |  |
| --- | --- |
| **Design** | Faced with long query times, we aimed to significantly speed up the process. Our plan was to optimize the API interactions by reducing redundant queries and implementing parallel processing. |
| **Build** | We first refined our queries by batching them and eliminating duplicate calls. Then,we implemented multiprocessing to allow parallel querying. We also realized that KEGG’s gene codes were prefixed with organism codes, allowing us to bypass unnecessary gene-organism queries by creating a local lookup table of organism codes. |
| **Test** | The optimizations reduced query times from 4 minutes per compound to around a minute. Local searching allowed me to quickly match genes to organisms without repeated API calls, and multiprocessing shaved off additional time. |
| **Learn** | We learned the value of preprocessing and local storage for optimization. The ability to locally reference organism codes sped up the pipeline and significantly reduced query times. While KEGG’s query limits remained an obstacle, the combination of multiprocessing and local searching allowed us to mitigate it. |

|  |  |
| --- | --- |
| **Design** | To complement KEGG, we decided to query BRENDA for additional EC numbers. However, BRENDA’s SOAP API, which was unfamiliar to us, required an entirely different architecture compared to KEGG’s REST API. We set out to learn and implement this new API architecture. |
| **Build** | We implemented the BRENDA API using the Python zeep library. However, we encountered significant issues - many documented API calls were not functioning as expected, with filtering parameters not being processed correctly server-side. Despite copying the examples directly from the documentation, several calls returned incomplete or erroneous data. |
| **Test** | After multiple failed attempts, we abandoned the problematic API calls and developed a workaround using BRENDA’s CSV download functionality. We set up Python code to automate the downloading of search results as CSV files, adjusting the search fields directly in the download URL. |
| **Learn** | We learned that while SOAP API architecture could potentially provide more structured responses, its implementation on BRENDA’s side was incomplete or flawed. The CSV download method proved much more reliable, allowing us to seamlessly extract the necessary EC numbers without further API issues. |

### Scoring Function and API Integration

Iteration 1

Iteration 2

|  |  |
| --- | --- |
| **Design** | With a list of candidate bacteria generated, We were tasked with developing a confidence score system. BacDive would be queried to check if the suggested bacteria are typically cultured in conditions similar to the user’s input. |
| **Build** | We implemented the BacDive API to retrieve temperature ranges, biosafety ratings, and (where available) media information for the bacteria. The process was straightforward since BacDive used REST architecture, but inconsistent documentation formats across bacteria entries caused frequent errors during data retrieval. |
| **Test** | While BacDive’s temperature and biosafety data were reliably accessible, media composition and pH values were not consistently provided. Additionally, media references often led to external papers, which we could not reasonably scrape within the project’s scope. |
| **Learn** | We learnt the importance of error handling in bioinformatics. The inconsistent formats in BacDive’s documentation led to broken loops in the code, which we resolved through extensive error handling. Though BacDive had limitations, it served as a crucial component for building the confidence score. |

|  |  |
| --- | --- |
| **Design** | While BacDive provided useful environmental data, many media references linked to MediaDive. We wanted to integrate MediaDive’s API into the pipeline to enhance the confidence score’s accuracy by obtaining more reliable pH and media composition data. |
| **Build** | We implemented MediaDive’s API, which had an extensive and well-documented REST interface. The code was designed to query MediaDive whenever BacDive referenced culture media from that database, pulling in more detailed media composition and pH information. |
| **Test** | The integration with MediaDive significantly improved the confidence score. The availability of additional media data allowed for a more thorough comparison between the user’s input conditions and the documented conditions for bacterial growth. |
| **Learn** | Integrating MediaDive made the confidence score system more robust, offering more precise matches between input parameters and bacterial conditions. This collaboration between BacDive and MediaDive provided a more accurate and reliable final output for the user. |

### Django Frontend Design and Integration

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

|  |  |
| --- | --- |
| **Design** | The goal was to create a user-friendly search form for querying the ChEBI database to create a streamline and self-contained user experience. This also had to be usable across browser types and versions. Implement responsive and visually appealing forms using Bootstrap as the CSS framework. |
| **Build** | Implemented the search form in the search.html template, including input fields for the search term, pH range, temperature range, and conditions using Django classes. Utilized sessionStorage to store user inputs dynamically within the script elements of the html, on client side. |
| **Test** | Conducted usability testing with different browsers and devices to ensure the search form behaved as expected. Validated that the input fields accept correct data formats and handle edge cases (e.g., negative pH). |
| **Learn** | Gained a basic familiarity of the Django framework. Obtained compatibility information of the frontend with various web browsers. |

|  |  |
| --- | --- |
| **Design** | Set out to create a boiler-plate for CHEBI API requests structure set-up. Wanted to determine the necessary data points to extract required information from the ChEBI API. |
| **Build** | Implemented the search\_chebi function in views.py in order to query the ChEBI API and return results. Parsed the XML response using BeautifulSoup and structured the data for rendering in the template. |
| **Test** | Tested various search queries to ensure the API integration returns correct and expected results. Verified that error handling works correctly for failed API requests. |
| **Learn** | Documented the performance of the API calls, noting any latency or reliability issues. Considered user feedback on the types of searches and results displayed. |

|  |  |
| --- | --- |
| **Design** | Our goal was to create a selection mechanism for users to choose which ChEBI compounds to submit for further processing and to come up with the submission workflow to gather additional parameters including pH, temperature and aerobicity. |
| **Build** | Implemented checkbox functionality for selecting compounds in the search.html template. Utilize JavaScript to manage the selected compounds and send a structured JSON object to the Django backend upon form submission. |
| **Test** | Tested the compound selection and submission process with various scenarios (no selection, single selection, multiple selections). Validated the structure of the JSON sent to the server manually and ensured it matches the expected format. |
| **Learn** | Assessed user experiences with the selection process and submission workflow. Identified possible points of confusion for the user and crafted appropriate instructions to be integrated into the webpage to enable users of varying technical familiarity to use Astrolabe. |

|  |  |
| --- | --- |
| **Design** | Planned to draft the backend processing of selected compounds, utilizing parallel processing for efficiency and define the integration points for the other APIs (UniProt, BioCyc) within the processing workflow. |
| **Build** | Implemented the process\_search\_data function in views.py to handle data processing and API calls in parallel using multiprocessing. Ensured that each API function (e.g., kegg, uniprot, biocyc) can handle the input data properly. |
| ****Test**** | Validated that the multiprocessing implementation functions correctly and that each API call returns results. Tested performance improvements compared to sequential processing. |
| **Learn** | Analyzed the efficiency gains from parallel processing and document the overall execution time for processing requests. Reviewed error handling for API calls in a multiprocessing context. |

|  |  |
| --- | --- |
| **Design** | Developed a scoring mechanism to evaluate and rank the results from the integrated APIs based on user-defined parameters (pH, temperature). Planned the presentation of results to users in a clear and informative manner. |
| **Build** | Implemented the scoring\_function in utilities.py to calculate scores based on combined results from the APIs. Created a results display section in the search.html template to show the top organisms based on the scoring results. |
| ****Test**** | Verify that the scoring function works correctly with various inputs and produces expected rankings. Ensure the results are displayed correctly and are easy to understand. |
| **Learn** | Collect user feedback on the usefulness of the presented results and the scoring mechanism. Analyze how well the results meet user expectations and make any necessary adjustments based on feedback. |

# References

[1] Vasanthi, N., Saleena, L. M., & Raj, S. A. (2016). Silica solubilization potential of certain bacterial species in the presence of different silicate minerals. Silicon, 10(2), 267–275. <https://doi.org/10.1007/s12633-016-9438-4>

[2] Sonali, J. M. I., Kavitha, R., Kumar, P. S., Rajagopal, R., Gayathri, K. V., Ghfar, A. A., & Govindaraju, S. (2021). Application of a novel nanocomposite containing micro-nutrient solubilizing bacterial strains and CeO2 nanocomposite as bio-fertilizer. Chemosphere, 286, 131800. <https://doi.org/10.1016/j.chemosphere.2021.131800>

[3] Bist, V., Niranjan, A., Ranjan, M., Lehri, A., Seem, K., & Srivastava, S. (2020). Silicon-Solubilizing media and its implication for characterization of bacteria to mitigate biotic stress. Frontiers in Plant Science, 11. <https://doi.org/10.3389/fpls.2020.00028>

[4] Semenov, A. M., Shatalov, A. A., & Semenova, E. V. (2024). Microscopic counting of the total number of bacteria and metabolically active bacteria in soil samples: their relationship and oscillation dynamics of number. Biology Bulletin Reviews, 14(2), 251–270. <https://doi.org/10.1134/s2079086424020105>

[5] Berthelsen, S., Kordorfer, G. H., CSIRO, & Universidade Federal de Uberlandia. METHODS FOR SILICON ANALYSIS IN SOIL, PLANT AND FERTILIZERS [Journal-article].

[6] UNITED STATES DEPARTMENT OF THE INTERIOR, GEOLOGICAL SURVEY, UNITED STATES GOVERNMENT PRINTING OFFICE, Books and Open-File Reports Section, U.S. Geological Survey, Fishman, M. J., Friedman, L. C., Arozarena, C. E., Horowitz, A. J., Boyle, D., Matthews, E. W., Brown, R. M., Jones, B. E., Drake, E. H., Ferryman, G. R., Fishman, M. J., Pyen, G. S., Friedman, L. C., Robinson, J., Garbarino, J., . . . Newell, N. L. (1989). METHODS FOR DETERMINATION OF INORGANIC SUBSTANCES IN WATER AND FLUVIAL SEDIMENTS (M. J. Fishman & L. C. Frledman, Eds.). UNITED STATES GOVERNMENT PRINTING OFFICE. <https://pubs.usgs.gov/twri/twri5a1/>

[7] Narayanaswamy, C., & Prakash, N. B. (2009). Calibration and categorization of plant available silicon in rice soils of South India. Journal of Plant Nutrition, 32(8), 1237–1254. <https://doi.org/10.1080/01904160903005970>

[8] Drury, L. (2003). Transformation of bacteria by electroporation. Humana Press eBooks, 249–256. [https://doi.org/10.1385/0-89603-402-x:249](https://doi.org/10.1385/0-89603-402-x%3A249)

[9] Dennis, J. J., & Sokol, P. A. (2003). Electrotransformation of *Pseudomonas*. Humana Press eBooks, 125–134. [https://doi.org/10.1385/0-89603-310-4:125](https://doi.org/10.1385/0-89603-310-4%3A125)

[10] Frick, K., Yeh, Y., Schmid-Staiger, U., & Tovar, G. E. M. (2022). Comparing three different *Phaeodactylum tricornutum* strains for the production of chrysolaminarin in flat panel airlift photobioreactors. Journal of Applied Phycology, 35(1), 11–24. <https://doi.org/10.1007/s10811-022-02893-x>

[11] Russo, G. L., Langellotti, A. L., Verardo, V., Martín-García, B., Oliviero, M., Baselice, M., Di Pierro, P., Sorrentino, A., Viscardi, S., Marileo, L., Sacchi, R., & Masi, P. (2023b). Bioconversion of Cheese Whey and Food By-Products by *Phaeodactylum tricornutum* into Fucoxanthin and n-3 Lc-PUFA through a Biorefinery Approach. Marine Drugs, 21(3), 190. <https://doi.org/10.3390/md21030190>

[12] Villanova, V., Singh, D., Pagliardini, J., Fell, D., Monnier, A. L., Finazzi, G., & Poolman, M. (2021). Boosting Biomass Quantity and Quality by Improved Mixotrophic Culture of the Diatom *Phaeodactylum tricornutum*. Frontiers in Plant Science, 12. <https://doi.org/10.3389/fpls.2021.642199>

[13] Moejes, F., Succurro, A., Popa, O., Maguire, J., & Ebenhöh, O. (2017). Dynamics of the Bacterial Community Associated with *Phaeodactylum tricornutum* Cultures. Processes, 5(4), 77. <https://doi.org/10.3390/pr5040077>

[14] Martin-Jezequel, Vet al. (2000). Silicon metabolism in diatoms: implications for growth. Journal of Phycology, 36(5), 821–840.

<https://doi.org/10.1046/j.1529-8817.2000.00019.x>

{% endblock %}

{% extends "layout.html" %} {% block title %}Experiments{% endblock %} {% block
lead %}Describe the research, experiments, and protocols you used in your iGEM
project.{% endblock %} {% block page\_content %}

![](https://static.igem.wiki/teams/5155/mars.webp)

EXPERIMENTS

* [Bacteria](#section1)
* [Diatoms](#section2)
* [Co-Culture](#section3)

Detailed protocols for all the experiments we developed can be found
at the end of this page. To better understand the rationale behind
our experimental work, please take a look
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D#section1).

# Bacteria

**Qualitative assays for silicate solubilization**

Two approaches were used to qualitatively confirm the silicate
solubilizing activity of *Pseudomonas fluorescens*:

* The first method involved growing the bacteria on solid growth
  media supplemented with 0.25% calcium aluminosilicate and
  subsequently checking for clearance zones around the colonies,
  which are indicative of solubilization [[1]](#refer)[[2]](#refer).
* The second method involved using NBRISSM, a defined differential
  liquid media for screening silicate solubilizers [[3]](#refer). Every 1
  liter of NBRISSM used in our experiment contained: 2.5 gm glucose;
  2.5 gm hydroxyapatite; 1.25 gm MgNO3; 1.25 gm
  CaCl2; 0.1 gm
  (NH4)2SO4; 0.1 gm calcium
  aluminosilicate in distilled water. We also added 0.75%
  bromocresol purple (BCP) pH indicator to reflect the presence of
  silicic acid through a change in color from purple to yellow.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Growth of bacteria in Martian soil simulant**

* The Mars Global (MGS-1) High-Fidelity Martian Dust Simulant [[4]](#refer)
  was obtained from Prof. Sathyan Subbaiah, Department of Mechanical
  Engineering, IIT Madras.
* To inoculate the soil, a 5 ml overnight culture of
  *P. fluorescens* was centrifuged to collect the cells. The
  pelleted cells were resuspended in 5 ml of fresh LB media.
* This cell suspension with fresh growth media was poured into 15 gm
  of Martian soil simulant (1:3 mass ratio) to achieve a moisture
  level of 25%.
* The flask with inoculated soil was placed in a 30℃, 250 rpm
  shaking incubator.
* Daily, over a period of 10 days, 1 gm of soil was mixed with 9 ml
  of sterile water and centrifuged. The supernatant was then
  serially diluted and plated on LB plates.
* The growth of the bacteria in soil was observed by counting the
  CFU (colony forming units) every day.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Quantitative assay for silicate solubilization in soil**

* After the bacteria was grown in soil, the solubilized silicates
  were extracted from soil using calcium chloride. 0.01 M CaCl2
  and soil in the ratio (10:1) were taken in a tube and continuously
  shaken for 1 hour [[5]](#refer).
* After shaking, the tube was centrifuged at 6000 rpm for 2 minutes.
  The supernatant was filtered and used for quantification of
  solubilized silicates using the molybdenum blue method.
* 0.25 ml of filtrate was taken in a plastic centrifuge along with
  10.5 ml of distilled water, 0.25 ml of 1:1 hydrochloric acid and
  0.5 ml of 10% ammonium molybdate.
* After 5 minutes, 0.5 ml of 20% tartaric acid was added.
* After 2 minutes, 0.5 ml of a solution of the reducing agent
  1-aminonaphthol-4-sulfonic acid (ANSA) was added. To prepare the
  ANSA solution, (a) 1.6 gm ANSA and 8.0 gm of sodium sulfite were
  added in 100 mL water, and (b) 100 gm sodium bisulfite was added
  to 800 ml water. The solutions (a) and (b) were mixed and the
  volume was made to 1 liter to obtain the final ANSA solution [[6]](#refer).
* After 5, but no later than 30 minutes following addition of ANSA,
  the absorbance was measured at 630 nm using a UV-Vis
  spectrophotometer.
* Simultaneously, silica standards of 0.2, 0.4, 0.8, and 1.2 mg
  L-1 were prepared in the same sodium sulfite matrix and
  had their absorbances measured using the spectrophotometer [[7]](#refer).

Learn more about our results and DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Preparation of BioBricks**

After locating the destination vector, pJUMP24-1A(sfGFP), and
required parts in the iGEM Distribution Kit 2024, the following
steps were performed:

* The dried down DNA in the wells was suspended in 10 μl
  demineralized water.
* 1 μl of this mixture was used to transform
  competent *E. coli* DH5α cells. Aliquots of competent cells were
  thawed on ice and then mixed with DNA. The cells were given a
  heat shock for 30 seconds at 42℃, followed by a cold shock for
  2 minutes. They were then immediately suspended in 1 ml of SOC
  (enriched media) for recovery and incubated in a 37℃ shaking
  incubator for 90-120 minutes.

| Part Name | Part Type | Vector Backbone in Distribution Kit | Antibiotic Used |
| --- | --- | --- | --- |
| BBa\_J23100 | Promoter | pSB1C5SA | Chloramphenicol 35 µg/ml |
| BBa\_J428038 | Ribosome Binding Site | pSB1C5SB | Chloramphenicol 35 µg/ml |
| 4S-limonene synthase | Coding Sequence | BBa\_J435500 | Ampicillin 100 µg/ml |
| BBa\_J428092 | Terminator | pSB1C5SD | Chloramphenicol 35 µg/ml |

* The cells were then plated on appropriate antibiotic
  plates.

* Transformed colonies were picked and inoculated in LB media
  with appropriate antibiotics.
* The overnight culture was then used to isolate plasmids
  following the QIAprep® Spin Miniprep Kit
  protocol.
* Concentrations of isolated plasmids were measured using a
  Nanodrop spectrophotometer.

Learn more about our results and DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Golden Gate Assembly**

* The assembly protocol recommended by iGEM Technology was
  used [[8]](#refer).
* We used the NEB Calculator to compute the mass of each
  BioBrick to be added to the reaction mixture in order to
  achieve 25 fmol of each BioBrick and 12.5 fmol of the
  destination vector.
* A 10 μl reaction was set up with 1 μl of BsaI, 0.5
  μl of T4 DNA ligase, 1 μl ligase buffer and the computed
  amounts of DNA. The rest of the reaction mixture consisted of
  deionised water.
* The following thermal cycling protocol was then
  followed:

|  |  |  |
| --- | --- | --- |
| 1 cycle | 37°C | 10 mins |
| 15 cycles | 37°C | 1.5 min |
| 16°C | 3 mins |
| 1 cycle | 50°C | 10 min |
| 1 cycle | 65°C | 10 min |

* The reaction mixture was then mixed at 10% volume with 50
  μl competent *E. coli* DH5α cells.
* Transformation through heat shock was carried out and the
  cells were plated on kanamycin (35 μg/ml)
  plates.
* Positive colonies were picked and propagated in antibiotic
  media. The culture was then used for plasmid isolation.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Confirmation of plasmid assembly**

* After Golden Gate Assembly and *E. coli* plasmid isolation, the plasmid was subjected to a
  single digestion by XbaI and a double digestion by XbaI and
  BstbI.
* The cloning vector without our cassette was also digested by
  XbaI.
* A 25 μl reaction was set up with 2.5 μl buffer, 0.5 ml
  of each enzyme, 500 ng of plasmid DNA and deionized
  water.
* Following digestion at 37℃ for 4 hours, we loaded the three
  digestion products in 0.8% agarose gel. The electrophoresis
  was run for 30 minutes at 100 volts. Gel was then viewed under
  UV Transilluminator.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Transformation of *P. fluorescens***

* Competent *Pseudomonas fluorescens*cells were prepared by washing the cells in late logarithmic phase with MEB (magnesium electroporation buffer). MEB contains 1 mM MgCl2 and 1 mM HEPES. For the washing step, a cell suspension in MEB was centrifuged at 2300g for 10 minutes at 4℃. The wash step was repeated thrice and all the steps were performed on ice. After all the wash steps were done, the cell pellet was resuspended in 100 μl MEB and aliquoted to make glycerol stocks [[9]](#refer)[[10]](#refer).
* Prior to electroporation, 50 μl of cell suspension aliquots (from the glycerol stock) were thawed on ice and mixed with: (a) 150 ng of DNA and (b) 1000 ng of DNA.
* Electroporation cuvettes were chilled on ice.
* The electroporator was set to 25 µF, 200 Ω, 2.5 kV/cm [[11]](#refer).
* The mixtures of cells and DNA were then transferred to cold electroporation cuvettes.
* Applying a pulse at the mentioned settings produced a time constant of 5.0 - 5.1 ms.
* Immediately, the cells were suspended in 1 ml SOC.
* This was then carefully transferred to sterile culture tubes and allowed to incubate at 30℃ and 225 rpm.
* After incubation, the cell suspension was spread on 25 μg/ml kanamycin plates and placed in a 30℃ incubator.
* Positive colonies were screened the next day.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

# Diatoms

**Culturing diatoms**

* *Phaeodactylum tricornutum*SAG 1090-1a was obtained
  from the Culture Collection of Algae at the
  Georg-August-University in Göttingen, Germany.
* Under normal conditions, the diatoms were cultivated in BG11
  medium [[12]](#refer) prepared using natural seawater, with pH
  maintained between 7 and 8. Growth conditions were maintained at
  either 27°C and 160 rpm or 21°C and 150 rpm, with a
  light dark cycle of 12h:12h and an average light intensity of
  1800 lux.
* Stressed diatoms were rescued by supplementing them with a
  carbon source. The culture was grown in f/2 medium supplemented
  with glycerol, made to a final concentration of 0.1 M [[13]](#refer).
  The pH was adjusted to 8.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Minimal media culturing**

* Growth conditions of *P. tricornutum* in a
  minimal medium were studied. This was done as an effort to make
  sure that diatoms could be grown with the least amount of
  resources given the premise of in situ resource utilization.
* The diatoms were cultivated in natural seawater supplemented
  with nitrogen and phosphorus sources [[14]](#refer).
* Growth conditions were maintained at 27°C and 160 rpm with a
  light dark cycle of 12h:12h and an average light intensity of
  1800 lux.
* Growth was quantified by measuring the optical density of the
  culture at 750 nm, in intervals of 24 hours.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Effect of silicon on diatom growth**

* *P. tricornutum* was cultured separately in minimal
  medium and minimal medium containing 30 mg/L sodium metasilicate
  nonahydrate [[14]](#refer), starting with the same initial OD of
  0.05.
* This was carried out by pelleting the diatom cells from the
  stock culture by centrifugation of equal volumes of the
  culture.
* Growth conditions were maintained at 27°C and 160 rpm.
* The experiment was carried out in duplicates and the growth was
  quantified by measuring OD at intervals of 24 hours for 15
  days.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Preparation of genes for acetaminophen synthesis**

* We sought out to demonstrate the dual biomanufacturing capabilities of our co-culture system by engineering the diatoms to synthesize acetaminophen.
* We received the *P. tricornutum*-specific plasmids pPTbsr and pPtPuc3 from Addgene as stab cultures. After propagating the culture on appropriate antibiotics we made glycerol stocks and isolated plasmids from the culture.
* We decided to insert our genes of interest for acetaminophen synthesis - 4abh and nhoA in the multiple cloning site flanked by the fcpA promoter (a constitutive promoter) and terminator.
* Primers were designed for Gibson assembly of the 2 gene sequences.
* For PCR, a 10 μl reaction was set up with 2 μl buffer, 0.2 μl dNTPs, 0.1 μl of DNA polymerase, 0.5 μl each of forward and reverse strand primers, 50 ng template DNA and the rest of the reaction was deionized water.
* The annealing temperature was found to be 61℃.
* The PCR product was then loaded on a 0.8% agarose gel and subjected to electrophoresis with 100 volts for 30 mins.

Learn more about our results and DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

# Co-Culture

**Optimizing salinity for co-culture**

* The diatoms usually grow at higher salinities than the bacteria.
  We performed this experiment to test the salinity tolerance of
  the bacteria.
* *P. fluorescens* was inoculated in 3 gm samples of
  Martian soil simulant enriched with LB media.
* 10 ml of the diatom’s minimal media with varying
  salinities was then added above the soil. Four cultures were
  made with salinities 30%, 15%, 7.5% and 5%. This was achieved by
  preparing the media in appropriate dilutions of seawater.
* Growth conditions were maintained at 30°C in shaking
  conditions.
* Growth was determined by measuring CFU in 0.1 gm samples of the
  soil simulant from the culture.

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

**Testing the co-culture**

* The interaction between *P. fluorescens*and *P. tricornutum* was studied by designing
  a co-culture setup with conditions optimized according to growth
  conditions of both the organisms.
* Four cultures were studied, which included one co-culture and
  three controls: bacterial culture, diatom culture and sterile
  media.
* The culturing media consisted of Martian soil simulant along
  with the minimal media prepared in pure seawater.
* For the co-culture, 3 gm of the soil simulant, enriched with 1
  ml of LB media containing *P. fluorescens* that
  was grown overnight, was added to a conical flask.
* Along with this, 15 ml of minimal media, with diatom cells in
  the exponential phase, was added to the flask.
* Growth conditions were maintained at 27°C and 160 rpm.
* Growth was quantified by measuring the OD of the cultures at 600
  nm for bacteria and 750 nm for diatoms.

![ ](https://static.igem.wiki/teams/5155/experiment/exp.webp)

See our results
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

Learn more about our DBTL cycles
[here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D).

All the protocols that we developed for our experiments can be
found here:

# References

[1] Vasanthi, N., Saleena, L. M., & Raj, S. A. (2016). Silica solubilization potential of certain bacterial species in the presence of different silicate minerals. Silicon, 10(2), 267–275.
<https://doi.org/10.1007/s12633-016-9438-4>.

[2] Sonali, J. M. I., Kavitha, R., Kumar, P. S., et al. (2021). Application of a novel nanocomposite containing micro-nutrient solubilizing bacterial strains and CeO2 nanocomposite as bio-fertilizer. Chemosphere, 286, 131800.
<https://doi.org/10.1016/j.chemosphere.2021.131800>.

[3] Bist, V., Niranjan, A., Ranjan, M., Lehri, A., Seem, K., & Srivastava, S. (2020). Silicon-Solubilizing media and its implication for characterization of bacteria to mitigate biotic stress. Frontiers in Plant Science, 11.
<https://doi.org/10.3389/fpls.2020.00028>.

[4] Mars Global (MGS-1) High-Fidelity Martian Dust Simulant. Space Resource Technologies.
<https://spaceresourcetech.com/products/mgs-1-mars-global-simulant>.

[5] Berthelsen, S., & Kordorfer, G. H. (2011). Methods for Silicon Analysis in Soil, Plant and Fertilizers.
<https://www.semanticscholar.org/paper/METHODS-FOR-SILICON-ANALYSIS-IN-SOIL-%2C-PLANT-AND-Berthelsen-Kordorfer/984011f3ce446e42a441cdacc462b2ba4db1481e>.

[6] Korndörfer, G. H., Snyder, G. H., Ulloa, M., Powell, G., & Datnoff, L. E. (2001). Calibration of Soil and Plant Silicon Analysis for Rice Production. Journal of Plant Nutrition, 24(7), 1071–1084.
<https://doi.org/10.1081/pln-100103804>.

[7] Narayanaswamy, C., & Prakash, N. B. (2009). Calibration and categorization of plant available silicon in rice soils of South India. Journal of Plant Nutrition, 32(8), 1237–1254.
<https://doi.org/10.1080/01904160903005970>.

[8] Golden Gate Assembly - Bennett Lab Wiki - Rice University Campus Wiki.
[https://wiki.rice.edu/confluence/display/BIODESIGN/Golden+Gate+Assembly](https://wiki.rice.edu/confluence/display/BIODESIGN/Golden%2BGate%2BAssembly).

[9] Dennis, J. J., & Sokol, P. A. (2003). Electrotransformation of Pseudomonas. Humana Press eBooks, 125–134.
[https://doi.org/10.1385/0-89603-310-4:125](https://doi.org/10.1385/0-89603-310-4%3A125).

[10] Coleman Lab. (2021). Preparation of electrocompetent cells of Pseudomonas or E. coli.
<https://www.coleman-lab.org/wp-content/uploads/2021/04/Preparation-of-electrocompetent-cells-of-Pseudomonas-or-E.coli-and-electroporation.pdf>.

[11] Drury, L. (2003). Transformation of bacteria by electroporation. Humana Press eBooks, 249–256.
[https://doi.org/10.1385/0-89603-402-x:249](https://doi.org/10.1385/0-89603-402-x%3A249).

[12] Russo, G. L., Langellotti, A. L., Verardo, V., et al. (2023). Bioconversion of Cheese Whey and Food By-Products by Phaeodactylum tricornutum into Fucoxanthin and n-3 Lc-PUFA through a Biorefinery Approach. Marine Drugs, 21(3), 190.
<https://doi.org/10.3390/md21030190>.

[13] Villanova, V., Singh, D., Pagliardini, J., et al. (2021). Boosting Biomass Quantity and Quality by Improved Mixotrophic Culture of the Diatom Phaeodactylum tricornutum. Frontiers in Plant Science, 12.
<https://doi.org/10.3389/fpls.2021.642199>.

[14] Moejes, F., Succurro, A., Popa, O., Maguire, J., & Ebenhöh, O. (2017). Dynamics of the Bacterial Community Associated with Phaeodactylum tricornutum Cultures. Processes, 5(4), 77.
<https://doi.org/10.3390/pr5040077>.

{% endblock %}

{% extends "layout.html" %} {% block title %}Results{% endblock %} {% block lead
%}You can describe the results of your project and your future plans here.{%
endblock %} {% block page\_content %}

![](https://static.igem.wiki/teams/5155/mars.webp)

RESULTS

* [Bacteria](#section1)
* [Diatoms](#section2)
* [Co-Culture](#section3)

# Bacteria

### Qualitative assay for silicate solubilization

The change in color of the experiment tube and no change in color in the control tubes, suggests pH change due to bacteria’s silicate solubilization activity. This proves that *P. fluorescens* can solubilize silicates from calcium aluminosilicate, the major silicate mineral in Martian soil.

![](https://static.igem.wiki/teams/5155/result/1.png)
![](https://static.igem.wiki/teams/5155/result/2.png)
![](https://static.igem.wiki/teams/5155/result/3.png)

### Growth of bacteria in Martian soil simulant

The bacterial growth dynamics of *P. fluorescens* in the Martian soil simulant show a wave-like pattern indicating cycles of growth and death. This behavior has also been [observed](https://doi.org/10.1134/s2079086424020105) in Earth-based soils.

![](https://static.igem.wiki/teams/5155/result/4.png)

### Golden Gate Assembly

The presence of all non-fluorescing colonies on the low CFU plate and the presence of several non-fluorescing colonies on the high CFU plate confirms successful assembly.

![](https://static.igem.wiki/teams/5155/result/5.png)
![](https://static.igem.wiki/teams/5155/result/6.png)

### Confirmation of plasmid assembly

Bands of expected size were observed, confirming successful Golden Gate Assembly.

![](https://static.igem.wiki/teams/5155/result/7.png)

### Transformation of *P. fluorescens*

*P. fluorescens* was successfully transformed with our limonene synthesis construct.

![](https://static.igem.wiki/teams/5155/result/81.webp)

# Diatoms

### Culturing diatoms

*P. tricornutum* showed optimum growth when cultured in
nutrient media prepared in seawater, with an approximate salinity of
30-35%. The diatoms showed slower growth at 27°C compared to
21°C with the exponential phase typically occurring between days
3 and 5 at 21°C, and between days 5 and 7 at 27°C.

Under stressed conditions the diatom growth was shown to be rescued
by providing an external carbon source. When cultured along with
bacteria, the bacteria can act as the carbon source for the diatoms
and enhance growth as well as increase survival under stressed
conditions.

![](https://static.igem.wiki/teams/5155/result/9.png)

![](https://static.igem.wiki/teams/5155/result/10.png)

### Minimal media culturing

The diatoms show a typical growth curve with exponential phase
occurring between days 5 and 8, suggesting that
*P. tricornutum* can be grown in a minimal medium containing
only nitrogen and phosphorus sources.

![](https://static.igem.wiki/teams/5155/result/11.png)

### Effect of silicon on diatom growth

![](https://static.igem.wiki/teams/5155/result/12.png)

The plot shows that the presence of silicate in the media leads to
higher overall growth of *P. tricornutum*, suggesting that
the diatoms can take up solubilized silica from the co-culture
environment for cell wall formation, resulting in growth enhancement
in the presence of solubilized silica.

# Co-Culture

### Optimizing salinity for co-culture

The plates incubated with samples from all four cultures showed
dense colony formation. This shows that the salinity of media used
in the co-culture setup does not hinder the growth of
*P. fluorescens*.

### Testing the co-culture

![](https://static.igem.wiki/teams/5155/result/13.webp)

The bacteria and diatoms in the co-culture containing the Martian soil simulant show synchronous growth, with a wave-like pattern corresponding to growth and death cycles of the bacteria in soil, implying a co-dependence of the two species for resources.

![](https://static.igem.wiki/teams/5155/result/14.webp)

![](https://static.igem.wiki/teams/5155/result/15.webp)

*P. fluorescens* and *P. tricornutum* show a higher average growth in the co-culture environment as opposed to when grown separately. These results indicate the establishment of a mutualistic relation between *P. fluorescens* and *P. tricornutum* in the co-culture environment.

Through our experimental work, we were able to conclusively prove the following:

* *Pseudomonas fluorescens* can solubilize silicates present in Martian soil.
* *Phaeodactylum tricornutum* can be cultured in minimal media, making it ideal for use in ISRU applications.
* *Phaeodactylum tricornutum* grows better in the presence of solubilized silicates, in the form of silicic acid.
* *Pseudomonas fluorescens* can be engineered to biomanufacture a compound of human interest.
* Both organisms show better growth in co-culture in the presence of Martian soil, indicating that they exchange nutrients and silicates.

{% endblock %}