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

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

  ^[1]: plants primary metabolism

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 Enzymes 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

• Enzymes 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

• Predators consist of
  • a list of Compounds 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
  • Compounds:
    
  • Enzymes:
    
  • Predators:
    

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

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