DESCRIPTION

The Payload Problem

The prospect of long-term space missions, particularly those involving human exploration and colonization of celestial bodies like Mars, face significant logistical challenges. The primary constraint on the duration and sustainability of long-term space missions is the burden of transporting massive quantities of resources from Earth, which is both expensive and cumbersome, significantly increasing the complexity and cost of space exploration [1].


The SynBio Solution

To address this constraint and enable sustainable human presence beyond Earth, the concept of In Situ Resource Utilization (ISRU) holds great promise. The idea is to make use of resources on other celestial bodies to produce materials necessary for human use. Biological techniques are an innovative avenue to achieve ISRU. Synthetic biology approaches can transform both in situ destination planet resources into practical products while consisting of less mass than conventional abiotic means [2].

Our project aims to establish a proof-of-concept of conducting In Situ Resource Utilization in outer space. We use synthetic biology to develop methods to use the silicates (SiO2)n in Martian regolith, either directly or indirectly, as a means to produce important chemical compounds.


cosmobiome

We present our project, cosmobiome - a CO-culture with Silicon MObilization for BIOmanufacturing using Martian rEgolith.

Various bacterial species have been shown to possess the ability to solubilize silicates into aqueous silicic acid Si(OH)4 [3]. Unicellular aquatic algae called diatoms can take this up and metabolize it back into silica, in order to produce a protective outer covering that is necessary for their survival [4]. When these diatoms die, they shed their siliceous covering. This silica, along with the dead diatoms’ cells, can act as a substrate for further bacterial growth.

Both these organisms can be genetically engineered to produce compounds of interest. Thus, a co-culture of silicate-solubilizing bacteria and diatoms can form a closed-loop system for and silica recycling, from which chemicals of human interest can be obtained in a dual biomanufacturing setup.

Diatoms are photosynthetic and can produce their own food, as long as carbon dioxide and sunlight are present. Increasing CO2 concentration supply to diatoms leads to increased growth and biomass production [5]. This allows us to utilize the CO2 atmosphere of Mars. In nature, algae cohabitate with other aquatic creatures, creating a balanced underwater ecosystem with plenty of nutrients such as nitrates, phosphates, and silicates [6].

Silicon is an important element for diatoms. Soluble silicic acid in the external aqueous environment enters the diatom through active or passive transport, depending on its concentration, and is moved into silica deposition vesicles (SDV). Here, the synthesis of the frustule or outer cell wall takes place [7].

The frustule is an integral part of the cell division process in diatoms. Before a diatom can divide, it must synthesize a new outer cell wall for its daughter cells, which is essential for their survival [8].

Having been well characterized and studied for its genetic engineering, the diatom Phaeodactylum tricornutum serves as an excellent chassis for introduction of new genes in order to produce compounds of interest [9]. By utilizing the anthranilate present in the naturally-occurring chorismate pathway of diatoms, and introducing the necessary genes as suggested by Menezes et al., we can synthesize acetaminophen. This pharmaceutical can be used by humans who may commit to long term space expeditions or plan to settle on Mars. The possibility of this biomanufacturing has been explored in our model here.

A major silicate present in the Martian regolith is anorthosite, a crystal form of calcium aluminosilicate. The bacterium Pseudomonas fluorescens has been shown to solubilize this form of silicate [3]. Pseudomonas species have also been shown to exist in symbiotic relations with the diatom P. tricornutum in natural habitats [6]. Thus, P. fluorescens was a perfect choice of the silicate-solubilizing bacteria for cosmobiome.

To demonstrate the biomanufacturing capabilities of our co-culture system and to show its compatibility with the iGEM Standard Registry of Parts, we engineer P. fluorescens to produce the compound 4(S)-limonene using parts available in the iGEM Distribution Kit 2024. This compound has applications across multiple industries, including the food industry where it is used as a flavoring agent, and in pharmaceuticals to aid in the penetration of medicinal creams and ointments [10]. By introducing 4(S)-limonene synthase into P. fluorescens, the bacterium can convert its naturally occurring geranyl diphosphate into limonene.

Learn more about our experiments here.


Modeling

Mathematical modeling provides a way to quantitatively represent biological systems, offering insights into their complex behaviors. The overall goal of our modeling is to explore the mechanisms underlying metabolite synthesis in our engineered organisms through constraint-based modeling, to simulate the silicate solubilization process through dynamic modeling, and to analyze the interactions in a co-culture of Pseudomonas and Phaeodactylum using community modeling.

For constraint-based modeling, we used COBRApy [11] to apply linear constraints, based on stoichiometric coefficients, to reactions within their respective metabolic networks in order to analyze the diatom and bacterial systems. Additionally, MATLAB’s SimBiology [12] allowed us to construct a dynamic model of the silicate solubilization process, capturing its behavior over time. We analyzed the interactions between the two organisms in co-culture using the MICOM framework [13], applying linear constraints to study their combined metabolic behavior. Mathematical modeling in this context allows us to predict and better understand these biological processes in silico.

To complement our wet lab experiments, we utilized our modeling to generate quantitative insights that support and guide our research. Analyzing our modeling results allows us to identify critical pathways and track the flow of metabolites [14], offering a deeper understanding of the metabolic processes at play. By simulating the processes involved in silicate solubilization, we can predict how the system will behave under different conditions. Also, by building a community model, we can predict key metabolites and optimize media composition. As a result of our three modeling strategies, we can ensure that our experimental efforts are more targeted and efficient, increasing the likelihood of successful outcomes while reducing trial-and-error in the lab.

Learn more about our modeling here.


Astrolabe: A Software for Space SynBio

Chassis selection is one of the first challenges to be tackled to achieve successful implementation of a SynBio project. With our focus on In Situ Resource Utilisation (ISRU), the importance of choosing to use organisms that can utilize a wide variety of minerals and other resources available cannot be overstated.

However, the choice of chassis is usually biased towards laboratory workhorses such as E. coli, rather than being tailored to resource availability. This increases the engineering required to produce desirable compounds from existing resources available on a host planet.

We present Astrolabe - A Space bioTechnology RecOmmendation aLgorithm for Applications in Biomanufacturing Extraterrestrially. This first-of-its-kind software will provide users with a ranked list of suitable chassis organisms based on the available environmental resources and conditions on host planets, maximizing resource utilization while minimizing the engineering required.

Learn more about our software here.


References

[1]  Menezes, A. A., Montague, M. G., Cumbers, J., Hogan, J. A., & Arkin, A. P. (2015). Grand challenges in space synthetic biology. Journal of the Royal Society Interface, 12(113), 20150803.  https://doi.org/10.1098/rsif.2015.0803 .  

[2]  Menezes, A. A., et al. (2015). Towards synthetic biological approaches to resource utilization on space missions. Journal of the Royal Society Interface, 12(102), 20140715.  https://doi.org/10.1098/rsif.2014.0715.

[3]  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 .  

[4]  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 .  

[5]  Sethi, D., Butler, T. O., Shuhaili, F., & Vaidyanathan, S. (2020). Diatoms for carbon sequestration and Bio-Based manufacturing. Biology, 9(8), 217.  https://doi.org/10.3390/biology9080217 .  

[6]  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 .  

[7]  Cassarino, L., Curnow, P., & Hendry, K. R. (2021). A biomimetic peptide has no effect on the isotopic fractionation during in vitro silica precipitation. Scientific Reports, 11(1).  https://doi.org/10.1038/s41598-021-88881-6 .  

[8]  Javaheri, N., Dries, R., & Kaandorp, J. (2014). Understanding the Sub-Cellular dynamics of silicon transportation and synthesis in diatoms using Population-Level data and computational optimization. PLoS Computational Biology, 10(6), e1003687.  https://doi.org/10.1371/journal.pcbi.1003687 .  

[9]  Huang, W., & Daboussi, F. (2017). Genetic and metabolic engineering in diatoms. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 372(1728), 20160411.  https://doi.org/10.1098/rstb.2016.0411 .  

[10]  Masyita, A., Sari, R. M., Astuti, A. D., Yasir, B., Rumata, N. R., Emran, T. B., Nainu, F., & Simal-Gandara, J. (2022). Terpenes and terpenoids as main bioactive compounds of essential oils, their roles in human health and potential application as natural food preservatives. Food Chemistry X, 13, 100217.  https://doi.org/10.1016/j.fochx.2022.100217 .  

[11]  Ebrahim, A., Lerman, J. A., Palsson, B. O., & Hyduke, D. R. (2013). COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Systems Biology, 7(1).  https://doi.org/10.1186/1752-0509-7-74 .  

[12]  SimBiology.  https://www.mathworks.com/products/simbiology.html .  

[13]  Diener, C., Gibbons, S. M., & Resendis-Antonio, O. (2020). MICOM: Metagenome-Scale modeling to infer metabolic interactions in the gut microbiota. mSystems, 5(1).  https://doi.org/10.1128/msystems.00606-19 .  

[14]  Passi, A., Tibocha-Bonilla, J. D., Kumar, M., Tec-Campos, D., Zengler, K., & Zuniga, C. (2021). Genome-Scale metabolic modeling enables In-Depth understanding of big data. Metabolites, 12(1), 14.  https://doi.org/10.3390/metabo12010014 .