Steven Crane Compiled Section Handouts. Human Behavioral Biology 2012

Ex: fish hierarchies do not follow predictions of the dyadic interactions.

Ex: brownian motion in distributing mitochondria, TFs, other cytosolic things when zygotes split and twin.

Ex: random transposable genetic events during neurogenesis.

THE MOST FUNDAMENTAL FAILURE of reductionism: nonlinear chaos

1. 
   What are the characteristics of nonlinear systems?
   
   1. 
      You can't predict the final state from the starting state. You have to go through every step.
      
   2. 
      Divergence/butterfly effect: small changes in the starting state can produce large changes in the ending state. A nonlinearity.
      

   3. Convergence: large changes in starting state can minimally affect the final state. A nonlinearity.

   4. Scale free: starting states of 26 and 27 can have as much difference than starting states of .00026 and .00027.

   5. 
      They ARE deterministic, just not predictable many generations into the future. Randomness and chance (above) are what we mean by systems that would NOT be deterministic.
      
2. 
   One way of summarizing all that: sensitive dependence on initial conditions/the butterfly effect.
   
   1. 
      Illustrated with cellular automata and their interactive reproduction rules.
      
   2. 
      What are some key features of cellular automata, their rules, and their outcomes?
      
      1. 
         Most starting states go extinct
         
      2. 
         Most others create boring patterns (like all-filled-in). This is a "non-organic" analogy. Like the structure of a rock or crystal.
         
      3. 
         Only a few interesting, dynamic mature patterns emerge as possibilities from very different starting states. Thus you're likely to converge on those.
         
      4. 
         You can't predict generation thirty based on generation one. You have to go through all the intervening generations.
         
      5. 
         Asymmetric starting states tend to produce dynamic, fluctuating mature states more than symmetric starting states.
         
3. 
   Translating these findings to the biological realm:
   
   1. 
      Vast majority of species have gone extinct.
      
   2. 
      Biological systems converge. Ex: high-altitude plants and their convergent evolution.
      
      1. 
         Knowing the final (converged) state doesn't tell you about the starting state.
         
   3. 
      The butterfly effect/divergence illustrated by macroevolutionary mutations (TFs, splicing enzymes)
      
   4. 
      You get patterns in seashells that mimic those of the cellular automata handout from class!