Ecosystem+complexity,+selection+and+insight

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TODO List:
 * REF Lotka volterra
 * REF Gardener & Aschby + CLARIFICATION
 * REF De Boer & Hogeweg 1985? immune influx system
 * REF Hubble 2001?
 * REF Higgs & Derida?
 * REF koza
 * REF Lynch small pops?
 * REF Kimura Waddington
 * REF Kauffmann: robustness maintained while making novelty

=Ecosystem complexity, selection and insights=
 * Equilibrium of a system? But what property of that system should be then consider? Which species, how many species? Often it is much easier studying transients.
 * Replicator versus influx systems
 * entities self-replicating, versus autocatalytic (not self-replicating)
 * neutrality versus selection
 * levels of complexity

Ecosystem complexity

 * In the 60's the main idea was that diversity leads to stability. This was an emergent truth, but a misinterpretation of Lotka-Volterra models.
 * In the 70's the likes of Gardener and Ashby, and May studied systems with arbitrary interactions:
 * here diverse systems were less likely to be stable
 * close to equilibrium of the random Jacobian matrices:
 * sigma*root(species*connectivity) < diagonal in almost all cases (??)
 * if get bigger (??) term hold, there is less chance of being stable
 * very little biological knowledge inputed, only equilibrium analysis
 * Later, more biological structure included: this gave a similar trend but showed much more stability
 * But: are equillibria so important or relevant for real ecosystems? The are only valid in settings with pre-defined non-changing species

Replicator versus Influx
This is based on a study by de Boer and Hogeweg 1985 (REF) on immune system diversity to study the postulate that in an influx driven system like the immune-system, complexity is different from an ecosystem case. Using a similar analysis as above (70's case): Bottomline: self- or non-self replicator dynamics are quite different
 * replicator system: on diagonal -a + Ecijnj: to be stable, negative, harder with greater N
 * influx system: on diagonal: -b - Ecijnj, always negative, the more stable
 * problem in out competition (hard to stabilize diversity and stability), which is not a problem in influx case
 * this is important for autocatalytic to self-replicator transition!

Ecosystem stability
We saw that:
 * interlocking timescales: the evolutionary timescales can be important for stabilizing diversity! (adapting polymorphism)
 * population-based diversity: co-evolution of populations (virus and host), may blow up complexity of system!
 * eco-system based information accumulation: (ecosystem based problem solving, hypercycles) all sorts of side-effects, creates new niches, based on selection
 * neutral theory (Hubble 2001): diversity simply due to randomness of birth death and dispersal. Species abundance patterns in "equilibrium", but what is there is changing.
 * so non-functional niches? no, but should take into account as "baseline" expectation
 * does function niche disturb species abundance as we should expect from random process? (now combined with Tilman niche differentiation)
 * cf Cordero: random process genes generates a number of observation in gene regulatory networks, but we conclude that FFL are not functional

Neutrality and speciation

 * quasi-species variation:
 * mutants are close in genotype but not in phenotype
 * depends on geno-pheno mapping variants can be quite different in phenotypes (cf Critters)
 * these are "shapes in the shadow" on the neutral path
 * on flat landscape (finite): clustering due to gyneology, simple random births and deaths (Higgs and Derida?), cf neutral path
 * standard speciation:
 * allopatric (could be selection) and mating, incompatability after neutral drift (e.g. via duplications and network attractors)
 * population - individual-based diversity: the bistability between them
 * alternative:
 * niche differentiation and new interactions: eco-system based problem solving
 * selection based competition avoidance

Thus about the elephant: versus
 * individual-based diversity
 * information threshold (not arbitrary amount of information)
 * side-effect of evolution (versus fixed target) (cf Koza)
 * side-effect of small populations (Lynch): contradicts information threshold
 * "constructive neutral evolution": effective population size is much smaller of complex organisms
 * they may be over information threshold: selected genome, population shrinks, suboptimal genotypes will arise, longer in population because mechanism by sub-functionalization
 * e.g. chaperones there because proteins loose independent protein, more freedom to evolve (chaperones, with stress work less well, more variation in proteins, way to rescue system?).
 * genetic operators (duplication, gcr, transposons)
 * structuring gene regulation networks
 * evolutionary signatures (only information local)
 * as alternative to population based diversity
 * as alternative to mutation priming, red Queen
 * study how to solve different tasks? qualitative insights what can be done
 * study as multi-level system:
 * critters: genes and system with dynamics of own, multiple side-effects
 * automatic orchestration of multiple features
 * interface with self-organization
 * robustness

Neutrality and evolvability

 * important for adaptation in evolution
 * long neutral paths and networks
 * there is a short way to go to better solution (many solutions 1% of all protein sequences do have enzyme function)
 * phenotype-first evolution (pre-pattern): Kimura, Waddington
 * mutational priming: needs nothing other than previous evolutionary history
 * evolutionary robustness (versus evolvability): fixed versus variable target, do not hinder each other, explore genotype space. Kauffmann: robust attractors can remain while making novelty

Next: Models of explanation (and end)


 * References**
 * De Boer & Hogeweg** (1985)
 * Lynch ??**
 * Higgs & Derida ??**
 * Hubble 2001?**
 * Tilman ?**
 * Kimura ?**
 * Waddington ?**
 * Kauffmann?**
 * Gardener & Ashby (197?)**