--- title: Chemodiversity subtitle: A short overview of this project author: Stefan Dresselhaus license: BSD affiliation: Theoretic Biology Group
Bielefeld University abstract: Attempt to find indications for chemodiversity in the plant secondary metabolism according to the screening hypothesis date: \today papersize: a4 fontsize: 10pt documentclass: scrartcl margin: 0.2 slideNumber: true ... What is chemodiversity? ----------------------- - It was observed, that many plants seem to produce many compounds with no obvious purpose - Using resources to produce such compounds (instead of i.e. growing) should yield a fitness-disadvantage - one expects evolution to eliminate such behavior Question: Why is this behavior observed? -------------------------------- - Are these compounds necessary for some unresearched reason? - unknown environmental effects? - unknown intermediate products for necessary defenses? - speculative diversity because they could be useful after genetic mutations? Screening Hypothesis -------------------- - First suggested by Jones & Firn ([1991](https://doi.org/10.1098/rstb.1991.0077)) - new (random) compounds are rarely biologically active - plants have a higher chance finding an active compound if they diversify - many (inactive) compounds are sustained for a while because they may be precursors to biologically active substances . . . There are indications for and against this hypothesis by [various groups](https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.12526#nph12526-bib-0093). -------------------------------------------------------------------------------- Setting up a simulation ======================= >If you wish to make apple pie from scratch, you must first create the universe > - Carl Sagan -------------------------------------------------------------------------------- Defining Chemistry ------------------ - First of all we define the chemistry of our environment, so we know all possible interactions and can manipulate them at will. - We differentiate between **`Substrate`{.haskell}** and **`Products`{.haskell}**: - **`Substrate`{.haskell }** can just be used (i.e. real substrates if the whole metabolism should be simulated, **`PPM`{.haskell}**^[1]^ in our simplified case) - **`Products`{.haskell }** are nodes in our chemistry environment. - In Code: ```haskell data Compound = Substrate Nutrient | Produced Component | GenericCompound Int ``` ::: footer ^[1]^: plants primary metabolism ::: Usage in the current Model -------------------------- - The Model used for evaluation just has one `Substrate`{.haskell}: `PPM`{.haskell} with a fixed Amount to account for effects of sucking primary-metabolism-products out of the primary metabolic cycle - This is used to simulate i.e. worse growth, fertility and other things affecting the fitness of a plant. - We are not using named Compounds, but restrict to generic `Compound 1`{.haskell}, `Compound 2`{.haskell} ... - Not done, but worth exploring: - Take a "real-world" snapshot of Nutrients and Compounds and recreate them - See if the simulation follows the real world Defining a Metabolism --------------------- - We define **`Enzyme`{.haskell}s** as - having a recipe for a chemical reaction - are reversible - may have dependencies on catalysts to be present - may have higher dominance over other enzymes with the same reaction - Input can be `Substrate`{.haskell} and/or `Products`{.haskell} - Outputs can only be `Products`{.haskell} - $\Rightarrow$ This makes them to Edges in a graph combining the chemical compounds Usage in the current Model -------------------------- - `Enzyme`{.haskell}s all - only map `1`{.haskell} input to `1`{.haskell} Output with a production rate of `1`{.haskell} per `Enzyme`{.haskell} (i.e. `-1 Compound 2 -> +1 Compound 5`{.haskell}) - are equally dominant - need no catalysts Defining Predators ------------------ - **`Predator`{.haskell}s** consist of - a list of `Compound`{.haskell}s that can kill them - a fitness impact ($[0..1]$) as the probability of killing the plant - an expected number of attacks per generation - a probability ($[0..1]$) of appearing in a single generation - `Predator`{.haskell} need not necessary be biologically motivated - i.e. rare, nearly devastating attacks (floods, droughts, ...) with realistic probabilities Example Environment ------------------- :::::::::::::: {.columns} ::: {.column width=37%} - The complete environment now consists of - `Compound`{.haskell}s: ![](img/compound_example.png){style="vertical-align:middle"} - `Enzyme`{.haskell}s: ![](img/enzyme_example.png){style="vertical-align:middle"} - `Predator`{.haskell}s: ![](img/predator_example.png){style="vertical-align:middle"} ::: ::: {.column width=63% .fragment} ![Our default test-environment](img/environment.tree.png){width=75%} Additional rules: - Every "subtree" from the marked `PPM`{.haskell} is treated as a separate species (fungi, animals, ...) $\Rightarrow$ Every predator can only be affected by toxins in the same part of the tree - Trees can be automatically generated in a decent manner to search for environmens where specific effects may arise ::: :::::::::::::: ::::: notes ::::: CTRL+Click for zoom! - All starts at PPM (Plant Primary Metabolism) - Red = Toxic - Blue = Predators :::: -------------------------------------------------------------------------------- Plants ------ A plant consists of ... Metabolism simulation --------------------- Compounds are created foo.. Fitness ------- - Static costs of enzymes - Cost of active enzymes Attacker -------- - Rate of attack ~> Paper, Formulas - Defenses - single plant - automimicry Haploid mating -------------- - fixed population-size (100) - $p(\textrm{reproduction}) = \frac{\textrm{plant-fitness}}{\textrm{total fitness in population}}$ - Gene - mutation - duplication - deletion - addition - activation-noise -------------------------------------------------------------------------------- Simulations ----------- Parameters tested - x - y - z -------------------------------------------------------------------------------- Results ======= >It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong. > - Richard P. Feynman --------------------------------------------------------------------------------