Team:Czech Republic/Project
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Contents
Abstract
The IOD band is a general diagnostic test enabling early detection and mapping of tumor mobility. Over a billion unique tests are made accessible to field experts outside of synthetic biology with a unique clone-free assembly feature. Tumor mobility is incredibly difficult to diagnose due to the rarity of circulating tumor cells (CTCs) and the complexity of surface marker combinations. The IOD band strives to make it easy. The central players are processing units called Input Output Diploids or IODs. IODs use antigen recognition and intercellular communication to create a logical network by which even single cells carrying the desired marker profile can be identified in a background of millions. Affirmative CTC localisation triggers a global response manifested by IOD initiated clumping at levels visible to the naked eye. As such, IOD bands do in a test tube what normally requires days to do in the lab.
Motivation
Tumor mobility is likely the most significant prognostic factor for all types of cancer. Contained primary tumors often present no symptoms and if discovered early can be safely removed without needing subsequent chemotherapy. If left untreated, however, primary tumors spread through the lymphatic or blood circulatory systems to other parts of the body. Given enough time, the cancer cells transition and take on the forms of cells from other organs. At this stage, the cells invade compatible organs and secondary tumors called Mets develop. Early mets are less diverse and still present hope for treatment. Later mets, however, are too diverse and are usually associated with terminal diagnosis.
The difference between early stage and late stage diagnosis can be staggering. The table below lists the survival rate differential between early and late diagnosis for common cancer types.
| Stage | Kidney | Breast | Lung | Colorectal | Skin | Prostate |
|---|---|---|---|---|---|---|
| Stage I | 81% | 100% | 45% | 92% | 86% | 100% |
| Stage IV | 8% | 22% | 1% | 11% | 15% | 28% |
Early detection of cancer and its localization is very difficult. General early detection tests look for specific molecular traces in samples of blood or urine. Such tests usually carry a high rate of false positives and are difficult to calibrate for each individual. In addition, these test provide limited information regarding the primary and secondary tumor sites. Indeed, the localization of tumors is a real issue. Total body scans are impractical at the necessary resolution level and carcinogenic if applied regularly.
Circulating tumor cells (CTCs) provide an alternate path for tumor detection. These stray cells originate from the tumor site and enter the blood stream after begin pushed out from the forefront of the primary tumor. These stray cells are also the first to invade other organs and seed secondary tumors. During the process of detachment and invasion CTCs undergo several transitions downregulating local adhesian molecules and upregulating distal adhesian molecules and stem cell markers. Deciphering CTC surface markers holds the key to understanding the tumor's ability to invade the host system.
Methods and tools for detecting circulating tumor cells (CTCs) are very limited. The only FDA approved CTC test ...
The I
The IOD band concept
At team Czech Republic, we envision a body surface marker atlas. Each cell type is unique in its interaction with the system. A kidney cell is ... Hence marker profiles serve as unique area codes linking a cell to an organ.
The IOD band is a system that reads marker profiles and generates a signal visible to the eye when an interesting profile is found.
The IOD band is based on intelligent synthetic organisms called IODs that work together to solve complex problems such as the deciphering of marker profiles.
Functional prototype
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Module 1 - Signal transduction
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Module 2 - Synthetic signals and receptors
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Module 3 - Reversible cellular adhesion
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Module 4 - Modeling
IOD systems function at both the intracellular and intercellular level. Intracellular biochemical reactions determine signalling activities and gene expression. Extracellular reactions include signalling molecule diffusion and cell movement including cell-cell interactions. At each level models of various complexity are available. We selected minimalistic models that capture only the key design elements and integrated these models in a single simulator STEW. Subsequently simulation was used to study the robustness and efficiency of different signal transmission patters in identifying cells with specific marker profiles while minimising false positives.
Methods and Software
Stochastic biochemical reactions
Intracellular reactions are modelled as stochastic biochemical reactions. Each reaction $$i$$ has an exponential waiting time and changes the number of molecules $$X_j$$ by the stoichiometric amount $$S_{j,i} with the propensity $$w_i(X)$$, where $$X$$ is the vector of copy number for all modelled species.
Biochemical reactions were simulated using Gillespie's next reaction method. To correctly model the multicellular nature of the system, well-mixed assumptions are only applied at the individual cell level. In other words, each cell was treated separately in evaluating propensities and executing reactions.
Signalling pathways
Hydrodynamics
2D diffusion with active degradation
The simulator STEW
A simulator engine was developed to Emulate the multicellular Signal Transmission World (STEW). The simulator STEW integrates intracellular stochastic biochemical reactions with extracellular diffusion and cell movement including cell-cell interactions. Hence, STEW captures stochastic, continuous, discrete, and spatial phenomena. This was not an easy task as the numerical stability of each simulator component involves requirements on spatial and temporal granularity. In addition to the methods described above, the following open source modelling libraries were implemented and integrated with the inhouse utilities.
- cell dynamics including collision and movement due to hydrodynamic forces were simulated using ***.
- visualisation of the simulator was pefored using the *** offering shape smoothing and ***
Simulation results
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