Presented by Team AFCM-Egypt 2020
Ahmed Adel Rezk¹, Ahmed Gamal Mattar¹,Ahmed Wael¹,Saif Wahba¹, Mohamed A. Mekki¹,Aly Mahmoud Morsy¹,Marwan Samir¹,Moetaz Sherif Metawea¹,Mohamed Tahoun¹,Raed Ouerfelli¹,Taycir Ben Ammar¹,Ahmed Emad Eldin¹,Alaa Ehab¹,Eyad Ashraf¹, Mohammad Tarek², Hana M. Abdelzaher², Mahmoud AbdelGawad², Ziad Nagy ², Mohammed Salah², Mostafa El Nakib², Mohamed Osama², Yasser Elbedewy², Ahmed Hamdy², Mohamed Gamal², Ahmed Elshewikhy², Assem Ismael², Ayman Shawky³ ,Wagida Anwar³ ,
¹iGEM Student Team Member, ²iGEM Team Mentor, ³iGEM Team PIs.
Abstract
Triple negative breast cancer is one of the most aggressive breast cancer subtypes. It is characterized by a generally poor prognosis and strong resistance to traditional therapies. This season, we focused on designing a novel immunotherapeutic approach involving DNA-launched RNA Replicons (DREP). We utilized our hotspot detection tool, “Custommune”, to generate a list of candidate neo-epitopes from clinically prioritized neoantigens thus reducing the need for extensive sequencing approaches. After in silico validation of our generated neo-epitope predictions, we devised a DREP-based platform to effectively deliver our multi-neo-epitope vaccine. The platform utilizes the inherent self-amplification ability of RNA-replicons to ensure enhanced neo-antigen uptake and presentation leading to the mounting of efficient cellular and humoral immune responses against TNBC. Embedded with optimized subgenomic regulation and linker-peptides alongside glycine-alanine repeats (GAr), a miRNA-based immune evasion mechanism and an OFF-switch, our platform is predicted to be highly safe and efficient through experimentally-driven mathematical simulations.
Team:AFCM-Egypt/Poster
Neo-epitope discovery for DNA-launched RNA replicons: paving the way to efficient breast cancer vaccination
Hotspot Neoantigen Retrieval
Inspiration:
Hundered of thousands of neoantigens appear on the surface of cells on growth
Main problem: To develop a new drug fits all of these it's impossible, due to high cost
Our Focus: Main concern is triple negative breast cancer, we have chosen highly specific and the most expressed epitopes on (TNBC) cells
Data retrieval: So we used The Gene expression omnibus (GEO) We found 21 datasets listed in figure() After that we collected data from the TCGA about neoantigens.
Method: Then we could to filter these data more specifically according to logfc & t value Finally using the final 3 datasets we tried to find intersections between them by genevenn, and also from literature (CD79A,CDH1,NCOA1,PDE4DIP,MYC,JAK1,NCOA2,PICALM,TRRAP,ATRX,MSH6,
PRKAR1A,FLI1,MAP2K2,MET,SPEN,TFE3,ARNT,SMARCB1,IKZF1,NR4A3,RUNX1,
AR, NTRK1,RBM10,MSH2,MUC1, Lck, PSA, PAP, MRP3 PTHrP, HNRPL, WHSC2,
SART3,CypB, UBE2V, EGFR)
TNBC Neoantigens Retrieval Process: T-cell epitope prediction is a big challenge due to high MHC polymorphism we identified a set of promising TNBC neoantigens ready. For each epitope examined in TCGA revealing usual sites of mutation in specific TNBC cases We worked on a group of 173 TNBC cases that have been classified into Four groups, BL1, BL2, M and LAR.
Prediction of Candidate epitopes was done then graded according to multiple HLA alleles: Custommune provided a final list of epitopes and built a Consensus sequence for B-cell epitopes prediction.
Main problem: To develop a new drug fits all of these it's impossible, due to high cost
Our Focus: Main concern is triple negative breast cancer, we have chosen highly specific and the most expressed epitopes on (TNBC) cells
Data retrieval: So we used The Gene expression omnibus (GEO) We found 21 datasets listed in figure() After that we collected data from the TCGA about neoantigens.
Method: Then we could to filter these data more specifically according to logfc & t value Finally using the final 3 datasets we tried to find intersections between them by genevenn, and also from literature (CD79A,CDH1,NCOA1,PDE4DIP,MYC,JAK1,NCOA2,PICALM,TRRAP,ATRX,MSH6,
PRKAR1A,FLI1,MAP2K2,MET,SPEN,TFE3,ARNT,SMARCB1,IKZF1,NR4A3,RUNX1,
AR, NTRK1,RBM10,MSH2,MUC1, Lck, PSA, PAP, MRP3 PTHrP, HNRPL, WHSC2,
SART3,CypB, UBE2V, EGFR)
TNBC Neoantigens Retrieval Process: T-cell epitope prediction is a big challenge due to high MHC polymorphism we identified a set of promising TNBC neoantigens ready. For each epitope examined in TCGA revealing usual sites of mutation in specific TNBC cases We worked on a group of 173 TNBC cases that have been classified into Four groups, BL1, BL2, M and LAR.
Prediction of Candidate epitopes was done then graded according to multiple HLA alleles: Custommune provided a final list of epitopes and built a Consensus sequence for B-cell epitopes prediction.
Neoepitope Discovery and Computational Validation
Using bioinformatics tools and after mining the literature we could retrieve some elected epitope to help in vaccine formulation and assessment.
Generally speaking there are 2 types of vaccine: for those who elicit humoral response we use T helper and B cell epitope servers in addition to HLA-II database. However, for those eliciting cellular response we use HLA-I.
IDENTIFICATION OF MHC I & MHC II EPITOPES AND THEIR PREDICTION USING CUSTOMMUNE
After submission of the mutated DNA sequence as Fasta file into Custommune, it predicts the T-cell epitope and ranks them according to their custoscore and an epitope scoring method, called the IC50, in addition to other special filtration and scoring parameters, as shown in the equation below:
S = 10000 * 〖"(IC50)" 〗^(-1) - DFIRE + EscapeM *500 + CScore *1000 + LocationScore * 500 – SDaffinities + DOverlap *500
Custommune calculates C-score, as well as analyzing the reported escape mutation for each peptide. Then, custommune calculates D-Overlap. Eventually, it estimates the Standard-Deviation affinity and docking of each epitope to a set of HLA-alleles by Affinity robustness that we call the D-Fire score.
Figure: Early phase II clinical study testing 19‐peptide cancer vaccine monotherapy on 14 advanced metastatic triple‐negative breast cancer (mTNBC) patients. DOI 10.1111/cas.14510
Figure: Experimental correlation and Validation of MUC1 epitopes immunogenicity
IDENTIFICATION OF MHC I & MHC II EPITOPES AND THEIR PREDICTION USING CUSTOMMUNE
After submission of the mutated DNA sequence as Fasta file into Custommune, it predicts the T-cell epitope and ranks them according to their custoscore and an epitope scoring method, called the IC50, in addition to other special filtration and scoring parameters, as shown in the equation below:
S = 10000 * 〖"(IC50)" 〗^(-1) - DFIRE + EscapeM *500 + CScore *1000 + LocationScore * 500 – SDaffinities + DOverlap *500
Custommune calculates C-score, as well as analyzing the reported escape mutation for each peptide. Then, custommune calculates D-Overlap. Eventually, it estimates the Standard-Deviation affinity and docking of each epitope to a set of HLA-alleles by Affinity robustness that we call the D-Fire score.
Figure: Early phase II clinical study testing 19‐peptide cancer vaccine monotherapy on 14 advanced metastatic triple‐negative breast cancer (mTNBC) patients. DOI 10.1111/cas.14510
Figure: Experimental correlation and Validation of MUC1 epitopes immunogenicity
Prediction of T and B-cell epitopes
Actually, Anticipating B-cell antigenicity is vital and imperative in our vaccine planning and circuit design. Be that as it may, it is confounded to anticipate B-cell epitopes by computational tools. Furthermore, characterizing a T-cell epitope could bring about the ID of a B-cell epitope, since B-cell epitopes have appeared to colocalize with T-helper epitopes. Utilizing in-silico procedures and B-cell epitope forecast instruments, we had the option to get excellent b-cell epitopes from IEDB.
Custommune analyzes the DNA sequence and provides us Consensus sequence that is then inserted as an input into IEDB, thereby using its Random forest algorithm to predict linear peptides for B cells then assessing them to be added to our circuit.
Figure: Predicted Cytotoxic T-Cell epitopes of the 17 TNBC antigens.
Figure: Predicted Helper T-Cell epitopes of the 17 TNBC antigens.
Custommune analyzes the DNA sequence and provides us Consensus sequence that is then inserted as an input into IEDB, thereby using its Random forest algorithm to predict linear peptides for B cells then assessing them to be added to our circuit.
Figure: Predicted Cytotoxic T-Cell epitopes of the 17 TNBC antigens.
Figure: Predicted Helper T-Cell epitopes of the 17 TNBC antigens.
DNA-launched replicon design and optimization
Our DNA-Launched Replicon has its main bulk formed of 4 non structural proteins proteins by which we can get self-amplification of a downstream construct which would highly increase the expression of our vaccine all of which was even further improved by adding of selective mutations that increased its efficacy even more, followed by parts and adjuvants that would increase our selected vaccine expression even more followed by parts BBa_K3504023 & BBa_K3504007 (Off-switch & Gly-ala repeats accordingly) designed to help ensure our vaccine safe reach into the cell, and to reap the maximum benefit of it we optimized many of our parts such as part BBa_K3504018 & BBa_K3504006 which are (P2a cleavage site & Mi-Rna FF6 accordingly) First with p2a cleavage site separating the reporter gene from the vaccine and furthermore increasing its cleavage ability by adding 3xflag & v5tag upstream to it and the second by choosing the best mirna from multiple synthetic mirnas to enhance our off switch and get the highest efficacy overall.
Figure: Final circuit Design.
Figure: Final Sbol Design for the circuit.
References:
DiAndreth, B., Wauford, N., Hu, E., Palacios, S., & Weiss, R. (2019). PERSIST: A programmable RNA regulation platform using CRISPR endoRNases. bioRxiv.
Wagner, T. E., Becraft, J. R., Bodner, K., Teague, B., Zhang, X., Woo, A., ... & Sanders, N. N. (2018). Small-molecule-based regulation of RNA-delivered circuits in mammalian cells. Nature chemical biology, 14(11), 1043-1050.
Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic acids research, 31(13), 3429-3431.
Figure: Final circuit Design.
Figure: Final Sbol Design for the circuit.
References:
DiAndreth, B., Wauford, N., Hu, E., Palacios, S., & Weiss, R. (2019). PERSIST: A programmable RNA regulation platform using CRISPR endoRNases. bioRxiv.
Wagner, T. E., Becraft, J. R., Bodner, K., Teague, B., Zhang, X., Woo, A., ... & Sanders, N. N. (2018). Small-molecule-based regulation of RNA-delivered circuits in mammalian cells. Nature chemical biology, 14(11), 1043-1050.
Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic acids research, 31(13), 3429-3431.
Safety Improvements
In order to improve the safety of our proposed circuit:
We decided to add two further protective mechanisms where the first guards our vaccine from being recognized and attacked by Cytotoxic lymphocytes which would cause the destruction of the cells expressing our vaccine. The way to do that is adding glycine-alanine repeats which were proven to prevent this by the part BBa_K3504007.This protects the circuit from the innate immunity.
The second defending mechanism was crafting an OFF-Switch founded on a modular NOR gate which relies on two miRNAs as its inputs. The first is mir-126, that also aids in the protection of the vector from the recognition and attack issue so if transfected to APCs as DCs it would activate the switch causing the arrest of the replication in these cells while preserving it in other cells. The second input was the synthetic miRNA FF4 where it is administered to simply stop the replication and act as a safety switch. With their binding sites being the part BBa_K3504008 (Mir-126 binding site) and the BBa_K3504019 (FF4 Binding site). References:
DiAndreth, B., Wauford, N., Hu, E., Palacios, S., & Weiss, R. (2019). PERSIST: A programmable RNA regulation platform using CRISPR endoRNases. bioRxiv.
Wagner, T. E., Becraft, J. R., Bodner, K., Teague, B., Zhang, X., Woo, A., ... & Sanders, N. N. (2018). Small-molecule-based regulation of RNA-delivered circuits in mammalian cells. Nature chemical biology, 14(11), 1043-1050.
Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic acids research, 31(13), 3429-3431.
We decided to add two further protective mechanisms where the first guards our vaccine from being recognized and attacked by Cytotoxic lymphocytes which would cause the destruction of the cells expressing our vaccine. The way to do that is adding glycine-alanine repeats which were proven to prevent this by the part BBa_K3504007.This protects the circuit from the innate immunity.
The second defending mechanism was crafting an OFF-Switch founded on a modular NOR gate which relies on two miRNAs as its inputs. The first is mir-126, that also aids in the protection of the vector from the recognition and attack issue so if transfected to APCs as DCs it would activate the switch causing the arrest of the replication in these cells while preserving it in other cells. The second input was the synthetic miRNA FF4 where it is administered to simply stop the replication and act as a safety switch. With their binding sites being the part BBa_K3504008 (Mir-126 binding site) and the BBa_K3504019 (FF4 Binding site). References:
DiAndreth, B., Wauford, N., Hu, E., Palacios, S., & Weiss, R. (2019). PERSIST: A programmable RNA regulation platform using CRISPR endoRNases. bioRxiv.
Wagner, T. E., Becraft, J. R., Bodner, K., Teague, B., Zhang, X., Woo, A., ... & Sanders, N. N. (2018). Small-molecule-based regulation of RNA-delivered circuits in mammalian cells. Nature chemical biology, 14(11), 1043-1050.
Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic acids research, 31(13), 3429-3431.
Proposed Assembly
Assembling two variants of our vaccine, the first one to be made using Equine Encephalitis Virus (EEV) Non Structural Protein replicon parts and the second using Semliki Forest Virus (SFV) parts with minor differences in design.
Using Golden Gate assembly cloning system in the assembly of EEV plasmids while using Simple restriction-enzyme ligation system in SFV Plasmids.
The circuit is separated into 8 fragments.
The 8th and final fragment contained reporter genes and enhanced p2a+gsg. And at designing our reporter gene, we found that its function could be enhanced by adding p2a cleavage site separating the vaccine from the reporter gfp. Furthermore its cleavage ability increased by adding 3xflag & v5tag upstream of it.
In order to select the best and most efficient one between multiple design variations, multiple plasmid designs are assembled to experimentally & computationally compare between them as we were lacking lab availability due to Covid-19 pandemic.
References:
DiAndreth, B., Wauford, N., Hu, E., Palacios, S., & Weiss, R. (2019). PERSIST: A programmable RNA regulation platform using CRISPR endoRNases. bioRxiv.
Wagner, T. E., Becraft, J. R., Bodner, K., Teague, B., Zhang, X., Woo, A., ... & Sanders, N. N. (2018). Small-molecule-based regulation of RNA-delivered circuits in mammalian cells. Nature chemical biology, 14(11), 1043-1050.
Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic acids research, 31(13), 3429-3431.
.
The first fragment contains Origin of replication and the AmpR and promoter.
The second fragment contains CMV promoter , FF4 binding site & mir126 binding site.
The 3rd to 6th fragments contained NSP1,NSP2,NSP3,NSP4 in that order with every fragment containing only one NSP.
The 7th fragment contained SubGenomic promoter, B-defensin, Badri and most importantly the chosen epitopes to our vaccine.
The second fragment contains CMV promoter , FF4 binding site & mir126 binding site.
The 3rd to 6th fragments contained NSP1,NSP2,NSP3,NSP4 in that order with every fragment containing only one NSP.
The 7th fragment contained SubGenomic promoter, B-defensin, Badri and most importantly the chosen epitopes to our vaccine.
The 8th and final fragment contained reporter genes and enhanced p2a+gsg. And at designing our reporter gene, we found that its function could be enhanced by adding p2a cleavage site separating the vaccine from the reporter gfp. Furthermore its cleavage ability increased by adding 3xflag & v5tag upstream of it.
In order to select the best and most efficient one between multiple design variations, multiple plasmid designs are assembled to experimentally & computationally compare between them as we were lacking lab availability due to Covid-19 pandemic.
References:
DiAndreth, B., Wauford, N., Hu, E., Palacios, S., & Weiss, R. (2019). PERSIST: A programmable RNA regulation platform using CRISPR endoRNases. bioRxiv.
Wagner, T. E., Becraft, J. R., Bodner, K., Teague, B., Zhang, X., Woo, A., ... & Sanders, N. N. (2018). Small-molecule-based regulation of RNA-delivered circuits in mammalian cells. Nature chemical biology, 14(11), 1043-1050.
Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic acids research, 31(13), 3429-3431.
.
DREP-based Vaccine Replication
To simulate the efficacy of DREP-based Vaccine, we compared between plus-strand RNA molecules of traditional DNA vaccine replication vs plus-strand RNA molecules of DREP-based Vaccine within VMS. using populations of numbers (in the cytoplasm) of plus-strand viral RNA molecules (RPC), translation complexes (TC), viral polyprotein molecules (P), the enzyme NS5B and associated viral proteins needed for RNA synthesis (EC), the numbers of plus-strand RNA within the VMS ( after self-amplification) (RP), dsRNA within the VMS (RD), viral polymerase complexes within the VMS (E), the numbers of plus-strand RNA and dsRNA replicative intermediate complexes ( RIP and RID) respectively.
Plus-strand RNA (Traditional Vaccine)
References:
-Dahari, H., Ribeiro, R. M., Rice, C. M., & Perelson, A. S. (2007). Mathematical modeling of subgenomic hepatitis C virus replication in Huh-7 cells. Journal of virology, 81(2), 750–760. https://doi.org/10.1128/JVI.01304-06
-Ali, N., K. D. Tardif, and A. Siddiqui. 2002. Cell-free replication of the hepatitis C virus subgenomic replicon. J. Virol. 76:12001-12007.
Plus-strand RNA (Traditional Vaccine)
Plus-strand RNA (based on self-replicating Replicons)
Promoter optimization
References:
-Dahari, H., Ribeiro, R. M., Rice, C. M., & Perelson, A. S. (2007). Mathematical modeling of subgenomic hepatitis C virus replication in Huh-7 cells. Journal of virology, 81(2), 750–760. https://doi.org/10.1128/JVI.01304-06
-Ali, N., K. D. Tardif, and A. Siddiqui. 2002. Cell-free replication of the hepatitis C virus subgenomic replicon. J. Virol. 76:12001-12007.
Immune Evasion System
We have been recommended by Dr noreen wauford from MIT to construct an immune evasion system to protect our replicon from immune attack So, we constructed it via addition of Gly/Ala repeats upstream to our 3 vaccine versions to be protected from CTls attacks We tested it via these differential equations.
This allows the user to ligate up to six DNA parts together in a one-pot reaction, cutting down the time it takes to build large circuits dramatically (shown below).
The simulation showed a higher efficacy with using this system as it delays the time needed for degradation by about 30 days after addition of Gly/Ala repeats as illustrated in these graphs.
References:
-Mishchenko, E. L., Bezmaternykh, K. D., Likhoshvai, V. A., Ratushny, A. V., Khlebodarova, T. M., YU. SOURNINA, N. A. T. A. L. I. A., ... & Kolchanov, N. A. (2007). Mathematical model for suppression of subgenomic hepatitis C virus RNA replication in cell culture. Journal of bioinformatics and computational biology, 5(02b), 593-609
This allows the user to ligate up to six DNA parts together in a one-pot reaction, cutting down the time it takes to build large circuits dramatically (shown below).
The simulation showed a higher efficacy with using this system as it delays the time needed for degradation by about 30 days after addition of Gly/Ala repeats as illustrated in these graphs.
References:
-Mishchenko, E. L., Bezmaternykh, K. D., Likhoshvai, V. A., Ratushny, A. V., Khlebodarova, T. M., YU. SOURNINA, N. A. T. A. L. I. A., ... & Kolchanov, N. A. (2007). Mathematical model for suppression of subgenomic hepatitis C virus RNA replication in cell culture. Journal of bioinformatics and computational biology, 5(02b), 593-609
Immune and Anti-cancer Response
In order to simulate the anti-cancer response of our DREP-based vaccine, we performed this model to follow the dynamic of tumor size T (t) .
Simulation
In conclusion, it’s found that DREP-based vaccine elicits stronger anticancer response vs traditional DNA vaccine as shown in the graph above by decreasing tumor size.
Immune Response
We have already simulated immune response against the three versions of multi-epitope vaccine, yet, we wanted to simulate variation in immune response against DREP based delivery vs traditional DNA vaccine delivery via T helper and DCs response.
References:
-Bianca, C., Chiacchio, F., Pappalardo, F., & Pennisi, M. (2012). Mathematical modeling of the immune system recognition to mammary carcinoma antigen. BMC bioinformatics, 13 Suppl 17(Suppl 17), S21. https://doi.org/10.1186/1471-2105-13-S17-S21
-Pappalardo F, Mastriani E, Lollini PL, Motta S. Genetic Algorithm against Cancer. Lecture Notes in Computer Science. 2006;3849:223–228. doi: 10.1007/11676935_27.
Simulation
In conclusion, it’s found that DREP-based vaccine elicits stronger anticancer response vs traditional DNA vaccine as shown in the graph above by decreasing tumor size.
Immune Response
We have already simulated immune response against the three versions of multi-epitope vaccine, yet, we wanted to simulate variation in immune response against DREP based delivery vs traditional DNA vaccine delivery via T helper and DCs response.
References:
-Bianca, C., Chiacchio, F., Pappalardo, F., & Pennisi, M. (2012). Mathematical modeling of the immune system recognition to mammary carcinoma antigen. BMC bioinformatics, 13 Suppl 17(Suppl 17), S21. https://doi.org/10.1186/1471-2105-13-S17-S21
-Pappalardo F, Mastriani E, Lollini PL, Motta S. Genetic Algorithm against Cancer. Lecture Notes in Computer Science. 2006;3849:223–228. doi: 10.1007/11676935_27.
Integrated Human Practices
Throughout the year, we touched base with different experts at different time points of our project and incorporated their feedback into our design. Right when we were creating a preliminary design for the ON switch of our vaccine, we got fortunate to meet with Dr. Wauford during the MIT meetup collaboration. She explained to us the importance of studying the cellular clearance of the replicon vector issue of immunity evasion that we were missing out on and informed us that the circuit could completely break even before the replication process starts. As a result, we started using her PERSIST to design an OFF modular switch that contains a NOR gate as well as GLY/ALA repeats which allowed our circuit to evade innate immunty responses by controlling its inhibitors.
Among the merits we had through attending the MIT iGEM global meetup we got the chance to meet Dr. Chatterjee. We contacted him asking about project and he pointed out some issues that needed improvement and re-assessment, including: mathematical modeling of vaccine design, experimental designand finding new ways of enhancing delivery.
Among the merits we had through attending the MIT iGEM global meetup we got the chance to meet Dr. Chatterjee. We contacted him asking about project and he pointed out some issues that needed improvement and re-assessment, including: mathematical modeling of vaccine design, experimental designand finding new ways of enhancing delivery.
Human Practices
Human practices has always been an integral part of our iGEM projects, this year was no different. At the beginning of the season, we had in mind various activities that required gatherings, but when COVID-19 hit the world, we had to quickly switch our agenda. We relied on our meetings with public health experts to identify the problem we wanted to tackle. Afterwards, we decided that we wanted to pay attention to two major axes; the psychological aspect of BC and spreading awareness. We met with cancer survivors and prominent media figures to learn more about the experience of being a cancer patient and the role the media plays in spreading awareness.
We incorporated info from these meetings as well as our surveys into designing a brochure in both Arabic and English. We also participated in a breast cancer awareness campaign in Tunisia.
We incorporated info from these meetings as well as our surveys into designing a brochure in both Arabic and English. We also participated in a breast cancer awareness campaign in Tunisia.
Education
One of our prime objectives has always been imparting scientific attitudes into younger generations so we came up with the Synfair program where we take in students and teach them the basics of SynBio in a series of workshops and then host them again by the end of the summer for a science-fair style competition. This year, with the help of the Egyptian Ministry of Education, we were able to multiply the numbers of students who attended the competition last year, the total number of students exceeded 120 and the smallest group we taught this year was 40 students. In a highly interactive atmosphere, every group was educated in simple ways that fit the students' abilities to understand the topics. To guarantee that students understood the topics, quizzes were held all over the course in addition to the problem and team-based learning cases we presented. Fortunately, all our lectures were held before the Covid-19 pandemic so were able to bring about the students into lecture halls safely. We performed the science fair portion of our SynFair online using zoom.
We provided our SynFair content to AUC-EGYPT and they used it in the creation of their SynBio game.
We made synthetic biology lectures for a wide range of recipients from all age groups. We made sure our lectures were easy, understandable and customized to fit all the levels where we try to build the wall of knowledge in their minds from the ground up. The sessions were staged starting with the people who have no or little information about the DNA, genes, nucleotides, proteins and all the basics that would help them reach furthers levels in the synthetic biology field, ending up with sessions for the advanced stage, delivering the highest percentage of the acquaintance we already have.
We provided our SynFair content to AUC-EGYPT and they used it in the creation of their SynBio game.
We made synthetic biology lectures for a wide range of recipients from all age groups. We made sure our lectures were easy, understandable and customized to fit all the levels where we try to build the wall of knowledge in their minds from the ground up. The sessions were staged starting with the people who have no or little information about the DNA, genes, nucleotides, proteins and all the basics that would help them reach furthers levels in the synthetic biology field, ending up with sessions for the advanced stage, delivering the highest percentage of the acquaintance we already have.
Software
AFCMEasy Model was a tool developed as a part of Team AFCM-Egypt 2017 IGEM project. It is an interactive web tool that embeds R Codes used for modeling ceRNA networks to solve ODEs easily, specify parameters, rates and reaction species. The tool aimed to help other teams model their projects in an easy interactive way without facing technical issues.
And for this year, we decided to construct an efficient tool through improving and developing our previous made tool. It’s done via modification of the tool source code provided in Github to include the equations and parameters simulating replicon amplification which is the core. Thereby being beneficial to us and other teams who will depend on replicon in their projects to consume the time needed for the calculations and the simulation as well. In addition to consuming less time , You may also save your plot as an image and modify the parameter values within a defined range to be suitable for each project. The tool depends on a set of differential equations inspired by previous literature and further developed by our team. That’s why we created RepliconModel, to Achieve this aim in our project and help other futuristic teams in modeling their projects using replicons or different Designs by our developed easy-use tool.
Access the tool Here Access Github code Here.
And for this year, we decided to construct an efficient tool through improving and developing our previous made tool. It’s done via modification of the tool source code provided in Github to include the equations and parameters simulating replicon amplification which is the core. Thereby being beneficial to us and other teams who will depend on replicon in their projects to consume the time needed for the calculations and the simulation as well. In addition to consuming less time , You may also save your plot as an image and modify the parameter values within a defined range to be suitable for each project. The tool depends on a set of differential equations inspired by previous literature and further developed by our team. That’s why we created RepliconModel, to Achieve this aim in our project and help other futuristic teams in modeling their projects using replicons or different Designs by our developed easy-use tool.
Access the tool Here Access Github code Here.
Business Model
Traditional therapies for Breast cancer require accurate signatures that correspond with the dynamic state of the disease and its stage which is both time and money consuming, compared with Immunotherapeutic approaches. In addition to their adverse effects which are more money consuming for both the patients and the company. DNA vaccination as a promising approach yet remains a challenge for an improved delivery system, it was remarkable how DREP-based vaccine showed outstanding results based on our mathematical modeling and simulation regarding, its effect on enhancing body's own immune system with least side effects and with affordable financial funds required for its development. So, the usage of DREP-based vaccines would elicit a higher efficacy (as illustrated in our mathematical modelling and simulation) with lower cost (as avoiding multiple doses in DREP) that results in increasing patients’ compliance.
Unmet Need:
What is unique about our solution?
Our Market
Unmet Need:
What is unique about our solution?
Our Market
Future Directions
What next for our project?
Due to COVID-19, we had no access to our labs and campus. We therefore decided to keep our participation in iGEM this year as a phase I project. We performed extensive bioinformatics analyses and modeling to come up with an appropriate design. Through our engineering and multiple iterations, we further refined our design.
We are now looking forward to the next phase of our project which is the in vitro proof of concept. We will test our proposed assemblies in multiple cell lines to ensure that our design works. Afterwards, we look forward to applying for funding for animal studies that will hopefully lead into clinical trials.
We strongly believe that our design has the potential to become a good candidate immunotherapy against TNBC and that our design platform and pipeline, if succesful, could later on be adapted to various other tumors.
Due to COVID-19, we had no access to our labs and campus. We therefore decided to keep our participation in iGEM this year as a phase I project. We performed extensive bioinformatics analyses and modeling to come up with an appropriate design. Through our engineering and multiple iterations, we further refined our design.
We are now looking forward to the next phase of our project which is the in vitro proof of concept. We will test our proposed assemblies in multiple cell lines to ensure that our design works. Afterwards, we look forward to applying for funding for animal studies that will hopefully lead into clinical trials.
We strongly believe that our design has the potential to become a good candidate immunotherapy against TNBC and that our design platform and pipeline, if succesful, could later on be adapted to various other tumors.
Acknowledgements and Sponsors
Acknowledgements
Dr. Khaled Shoukry
Dr. Ayman Shawky
Dr. Wagida Anwar
Special thanks to Dr. Hana Abdelzaher
Dr. Khaled Shoukry
Dr. Ayman Shawky
Dr. Wagida Anwar
Special thanks to Dr. Hana Abdelzaher