# Chemodiversity: A short overview of this project

Chemodiversity
A short overview of this project
Stefan Dresselhaus
Theoretic Biology Group
Bielefeld University

## 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)
- 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.

# 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` and `Products`:
  - `Substrate` can just be used (i.e. real substrates if the whole metabolism should be simulated, `PPM`[1] in our simplified case)
  - `Products` are nodes in our chemistry environment.

- In Code:

      data Compound = Substrate Nutrient
                    | Produced Component
                    | GenericCompound Int

## Usage in the current Model

- The Model used for evaluation just has one `Substrate`:
  `PPM` 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`, `Compound 2` …
- 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`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` and/or `Products`
- Outputs can only be `Products`
- ⇒ This makes them to Edges in a graph combining the chemical compounds

## Usage in the current Model

- `Enzyme`s all
  - only map `1` input to `1` Output with a production rate of `1` per `Enzyme`
    (i.e. `-1 Compound 2 -> +1 Compound 5`)
  - are equally dominant
  - need no catalysts

## Defining Predators

- `Predator`s consist of
  - a list of `Compound`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` need not necessary be biologically motivated
  - i.e. rare, nearly devastating attacks (floods, droughts, …) with realistic probabilities

## Example Environment

- The complete environment now consists of
  - `Compound`s:
  - `Enzyme`s:
  - `Predator`s:

Our default test-environment

Additional rules:

- Every “subtree” from the marked `PPM` is treated as a separate species (fungi, animals, …)
  ⇒ 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

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