CMPE 58B Project Presentation Digital Organism Simulates Life and Cancer Evolution Presented by Melih Sรถzdinler 15.01.10
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Contents ●
Introduction
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Previous Work
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Methods
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Experiments
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Future Work
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Conclusion
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Introduction ●
We try to simulate a digital organism
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To see behaviour of the simulated organism
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To represent a hierarchy of the body ●
From cells to tissues
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From tissus to organs
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From organs to systems
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Introduction(cont.)
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We can form a simple digital organism that lives artificially by meaning maintains its whole structure in the simulation. We can adopt some environment and nature rules such as die, mutate, reproduce.
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Introduction(cont.) â—?
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When we adopt these rules specifically to simulated organism, can we answer why cancer like diseases occur in real life by occuring in simulated life? The motivation behind this research to investigate this concept in simulated organisms.
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Previous Work ●
Many efforts to produce digital organism ●
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Digital organism definition may change due to application and simulation. –
Efforts for representing with instructions
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Finite automata representation
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Graph based approaches by maintaining some data structures to node.
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Previous Work(cont.) ●
Aims and motivations ●
Convey complex feature
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Origin of multicellular organism
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Mutation
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The effect of other parameters
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Methods â—?
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We aim to design graph based digital organism We propose several parameters and assumptions. We mainly focus on the ratio of mutated cells in an organism.
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Methods(cont.) ●
Digital organism has ●
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Binary gene sequence with L Several organs whose cells have a proper gene sequence on L according to the organ type Organ cell is connected to some of the same organ cells and some organ cells are also connected to other organ cells.
L of the cell represents an organ if L includes k genes of the organ. 10
Methods ●
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We added some functions and parameters ●
Probability of die
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Probability of mutate
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Probability of die alone
Probability of die alone stands for immune system. Mutated cells assumption
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Experiments ●
We have several comparisons, mainly we consider the ratio of mutated cells namely cancer cells.
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Die Alone Probability vs Ratio of Cancer Cells
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Mutation Probability vs Ratio of Cancer Cells
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Connectivity vs Ratio of Cancer Cells
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Treatments vs Ratio of Cancer Cells
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Die Rate vs Ratio of Cancer Cells 14
Experiments Die Alone Probability vs Ratio of Cancer Cells
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Experiments Mutation Probability vs Ratio of Cancer Cells
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Experiments Connectivity vs Ratio of Cancer Cells
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Experiments Treatments vs Ratio of Cancer Cells
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Experiments Die Rate vs Ratio of Cancer Cells
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Futute Work ●
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We provide a novel simulation of digital organism based on graphs while looking for cancer cell ratio. We have some future work to do
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Due to computation power simulations of each organism is limited, some plots have peaks and fluctuations.
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Assumption of mutation that changes all the gene sequence may be considered in different ways. 20
Futute Work(cont.) ●
We have some future work to do
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Importing immune system to the organism by introducing new cell type rather than organ cells and cancer cells.
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Immune cells could be both mobile and stable. If it is mobile each cell have a coordinate in x-y plane in 2D and x-y-z space in 3D.
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Having more diseases such as flu viruses. This force us to introduce one more cell type as a mobile agents in an organism which are viruses. 21
Conclusion ●
To conclude ●
Mutated cells can be cured by increasing network size and probability die alone rate
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High die rates can result with the case of spread in mutation
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Increasing connectivity, decreases the number of cancer cells
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Smaller organism are more vulnerable to mutation as we see in digital life and know from real life Probability of die alone can work against the normal cells when the mutation probability is high
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Questions THANKS FOR YOUR LISTENING
"Don't undertake a project unless it is manifestly important and nearly impossible." Edwin Land
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