Conclusion

Next: Defining properties of biocomplexity?

=8 Conclusion=

In this course we have looked at modelling (complex) biotic systems and what //can// and //cannot// be done (sensibly). As a consequence we have obtained both **methodological** (how can I model complex biology) and **biological** (what have I learned about complex biology) results. Thus we see that when we form theory, //it is possible to model complex biological systems.// However, while we can model a lot about them, //can we tackle the defining propoerties of a biological system?//

Clearly a model must be simple enough to understand, however if it has no parameters at all there will be no understanding. Thus a model should be simple as possible, but not more than that (cf Einstein, Ashby)//,// i.e. if the obervables one uses are trivial enough then it is very easy!. We should keep this in mind: **many observables are //too// general, i.e. it is an empty cross-section / comparison.** Therefore, we should develop modeling approaches such that biological complexity becomes feasible. In this way we can develop alternative caricatures in order to survey a set of possibilities.

What we don't want is to:
 * pile up equations (doesn't give insight)
 * obviously many parameters are unknown (cf Lochness syndrome)
 * we could use evolution to evaluate parameters (cf Lac operon study)
 * not take small numbers of molecules into account (cf Attofox)
 * only consider well-mixed situations (cf spatial pattern formation)

In this section we consider the following issues:
 * 1) Defining properties of biocomplexity?
 * Nothing in biology makes sense except in light of ...
 * 1) How to model a complex system (Major Transitions)?
 * Major transitions (Maynard Smith & Szathmary)
 * 1) Ecosystem complexity, selection and insights
 * Ecosystem complexity
 * Replicator versus influx
 * Ecosystem stability
 * Neutrality and speciation
 * Neutrality and evolvability
 * 1) Models of explanation (and end)
 * Waarom zijn de bananen krom?

Next: Defining properties of biocomplexity?


 * References**

COURSE NOTES 2006-2007

Modelling (complex) biotic systems: what can / cannot be done (sensibly) - methodological and biological results

- when form theory: is it possible to model complex biological systems - can model a lot about them but can we tackle defining properties of biological system?

- model simple enough: no parameters, understanding - but not simpler (cf Einstein, Ashby): if observables one uses are trivial enough then very easy! - should keep this in mind: many observables are TOO general: i.e. is it an empty cross-section

- so should develop modeling appoaches such that bio complexity becomes feasible - to develop alternative caricatures to survey set of possibilities

What we don't want - pile up equations - parameter unknown: lochness syndrome: cf lac operon using evolutoin to evaluate parameters - small # of molecules: cf attofox? - cell not well mixed: spatial pattern formation

Defining properties of biocomplexity?

- local interactions, many different entities in small numbers: spatial individual-based models

- leaky mutliple levels of organization (feedbacks): but not so that can be studied independently and plugged into other, but have feedbacks (leaks) - self-organization (recognition): explicity effiort, e.g. if don't look at screen to id spirals can't understand reversal of selection - predefined (mmm)(cpm): definition at more than one scale, nice tool to see leaky interactions

-interlocking-timescales: coupled to leaky levels - timescale levels interact - evo + eco dynamics: interacting, not too far apart. Eco co-existence depends on small scale evolution interactions (wiggle) - we have seen that concept fitness (which is ill defined) is a very time dependent property - regulation + evolution interlock: don't have a free space phenotype to choose from, but stays within sensible states: keep what before get something new (Kaufmann seminar paper)

-evolved / evolving systems: - if we try to understand, not just function but also how we got there - neutrality; non-functional (virtual cell seminar paper) - not simplest implementation: dynamics of evo, + other things needs to do, i.e. relative to other things might be simplest - evolutionary signatures: process make certain biases in how organisms do things

- make modification biocomplex systems very difficult! - so lets make short cuts! - lets focus on those phenomena which we can address without space, multi-level, interlocking, evolution - but it is possible to target these defining properties as seen in the course

Nothing in biology makes sense.... - in light of evolution (Dobzhansky) - in light of self-organization (not wow, but can be avoided) - might be in the way! if want to be well mixed - simple rule to complex behaviour - loal interactions - micro-macro transition - non-linear dynamics etc Much makes sense in light of both!

How model complex systems? - how do biological systems become complex? - certainly not due to survival of fittest mechanism: not all system evolve to complexity, many go to simpler! - often ignored: wondering where complexity comes from! Maynard Smith: all evolutionary theory doesn't help to understand how particular properties evolve .... elephant etc 80's.

1995: Major Transitions book - try to identify real major changes in information processing, or prebiotic soup to now


 * They did || We did ||
 * - reconstruct: what did happen (at some times flawed) || - assume: Darwinian selection and local interactions (quite sure of these) ||
 * - in light of: Darwinian selection + chemistry ||  ||
 * - infer intermediate steps + common princimples || - observe what happens due to those assumptions: what DOES happen ||
 * Bottomline for both: ||  ||
 * multi-level evolution (entities first on own become part of larger system) ||  ||

They: - must be explained in terms of **immediate selective advantage** to **individual** replicators why? if not constrained could make any story they want! We: - because of from of higher-level entities, both premises are NOT TRUE: a) fitness time dependent on different time scales, b) not in terms of individual replicators, but SPIRALS

They: - transitions are: entities capable of independent replication before transition can replicate only as part of larger whole afterwards We; - have observed this: in spirals, subfunctionalization duplicated genes: can't do it alone: losing ability due to being in environment where something done by other certain will do it!; speciation, division of labour + human / insects

They: - If there are transitions to higher-levels: it is exception to rule: conflict selection at different levels e.g. cooperation: fair meiosis, sex reproduction, division of somatic growth, non-reproductive castes conflict: meitic drive, parthenogenesis, escape from growth control, egg-laying workers We: - can also see multi-level when no conflict! e.g. host-parastie wave-spirals - if in chaotic region evolution such that chaotic region formed + vice versa: self-reinforcing spatial pattern

- Also ? of int here: population / individual-based diversity: plasmids -ind level same direction to higher level! - Div labour: automatic arise due to TODO, not cooperation per se!

LOCAL INTERACTION DARWINIAN EVOLUTION: feedback emerging pattern to local rules self reinforce LOCAL INTERACTIONS: intertwined mutliple space and time scales: comp depends on global int. structure LOCAL INTERACTION important for evolution complex replicators: this is suggested needs to be pinpointed, change circumstance all kinds of scales LOCAL INTERACTIONS: sparse fitness more efficient optimization TODO + LOCAL INTERACTION: automatic adaptation: self-organization clock.

Multi-level modelling (Methodology)

Not glueing together model at multi-scale predefined notion what each will do

BUT

exploiting self-organization that can take place

- simple interactions: complex behaviour: coherence of ? - need multi-level to understand single level phenomena: alleviate uncertain parameters intensive problems

- need it to understand: complex to complex, evolutionary signitures, novelty

Major Transitions cont. - limit levels? - unlimited (open-ended) - examples: - autocatalytic set vs replicating molecules: complexity molecules increases - regulatory states vs epigenetic inheritance - limited cultural inheritance vs language

Hogeweg; - attractor based vs storage-based inheritance: defined dynamics system but not part of dynamics of dynamic system (Q: Possible?) (cf Interaction between gene regulation and evolution)

Maynard Smith: same phenomena at different levels Example: TODO + cell behaviour: decision making TODO + grouping: in order to have coherent motion: do what neighbour does + align - picks up signal from those who do something else - prototype BOIDS Compare to cells: persistency leads to coherences (via crowding) Beltman & Maree: CPM + persistency - have direction: initial random - because direction movement particular direction more probably - if moved another direction, adjust direction - cell alone: more random direction - more cells: all move same direction + move faster Same mechanism as animals: but slight different feeling during interpretation! of mechanism because "need" less to interpret!

Ecosystem complexity, selection + insights

- equilibrium system? of what property?: easier that what will transient be! what is equilibrium, which species, how many speices!

- replicator vs influx systems - entities self- replicating vs autocatalytic not self replication

- neutrality vs selection

- levels of complexity

Ecosystem complexity

- 60's: diversity leads to stability (emergent truth, misinterpretation of lotka-volterra models) - 70's: Gardener + Ashby / May: arbitrary interation: diversity systems less likely to be stable: close to equilibrium, random jocobian matrices: sigma*root(species*connectivity) < diagonal ? : almost all cases if get bigger ?? terms hold, less chance of being stable - very little biological knowledge: just equillibrium stability analysis

later: more bio structure; same trend but more stable - equilibrium (true for ecosystems?): predifined non-changing species

Replicator vs influx: de Boer + Hogeweg 1985 (REF)

postulated- immune system: complexity different from ecosystem similar analysis as above: replicator: on diagonal -a + Ecijnj: to be stable, negative: harder with greater N influx: on diag: -b - Ecijnj, always negative, the more stable

Bottomline: self or non-self replicator dynamics quite different - problem is out competition (hard to stabilize diversity + stability): not problem in influx - this is important for autocatalytic to self-replicator system!

Ecosystem stability - interlocking timescales: evolution timescale important for stabilizing diversity! wiggles (adapting polymorphism)

- population-based diversity: co-evolution populations (virus + host): may blow up complexity of system!

- ecosystem-based information accumulation: (based-problem solving, hypercycles), all sorts of side-effects create new niches: based on selection

- neutral theory (Hubble 2001): simply due to randomness birth death and dispersal, explain observed diversity: species abundant patterns in "equilibrium", but what is there is changing - so non-functional niches? no, but should take into account 'baseline' expectation - does function niche disturb species abundance as we would expect from random process? (now combined with Tilman niche differentiation ...) cf Cordero: random process genes: number of observation in gene regulation networks, conclusion not that FFL cannot have function ...

Neutrality + speciation - quasi-species variation: -mutatnts close in genotype but not in phenotype, - depend on G-P mapping, variants quite different phenotype (cf Critters) "shapes in shadow" on neutral path

- on flat landscape (finite): (diff) clustering due to gyneology, simply random births + death Higgs + Derrida: cf neutral path

- standard speciation: allopatric (could be selection) + mating, incompatability after neutral drift (e.g. via duplications + network attractors) - population / individual based diversity (bistability) - alternative: nich differentiation + new interactions: ecosystem based probelm solving - selection based, competition avoidance

About the elephant: - individual based diversity

vs - information threshold: not arbitrary amount information - side-effect of evolution (fixed target) KOZA - side-effect of small populations (Lynch): contradict information threshold "constructive neutral evolution": effective population size is much smaller of complex organisms might be over information threshold: selected genome, population size shrinks, suboptimal genotypes will arise, longer in population because mechanism by subfunctionalization - e.g. chaperones because there, proteins loose independent protein, more freedom to evolve chaperones: with stress work less well, more variation proteins, way to rescue system?

- genetic operators: duplication, GCR, transposons

- structuring gene regulation networks

- evolutionary signatures (only information local ia) - as alternative to population based diversity - as alternative to mutation priming red Q - study how solve different tasks? qualitative insights what can be done - study as mutli-level system: critters: genes + system with dynamics of own, multiple side effects - automatic orchestration of mutliple features - interface with self-organization - robustness

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

Modes of explanation

what is an explanation? "waarom waarom zijn de bananen krom?"

why why: if not bent no banana! - trivial tautology: not bent not banana! (making form reasonable question and unreasonable) NOT INT FORM OF EXPLANATION? cf. survival of fitness: as tautology can give very interesting insight: not necessarily the case, fitness time dependent

what is real, banana / culture / fitness

- e.g. definitions 'culture' - cf ALL model results: tautology follow from inputs, doesn't mean not interesting, (re)define concepts such as most informative interesting insights - define sets of concepts such that they hang together in a meaningful way

ALMOST ALL CASES - BEND: is generic, many ways - straight: only one explanation - if local interaction: default is spatial pattern formation (cf lymph nodes) - however straight simplest (linear) case: easiest solvable vs generic (so explain why bent)

Bending is OPTIMAL: against gravity? - cf walking in circles behaviour: very special? - for what is optimal: minimum distance to patrol target area -or- - robot: one motor: one step forward + turn angle - dangerous to have explanation in terms of optimal because generic property of motor is such that more circle

- optimal for whom? (in multi-level selection) - cost / benefit trade-off - dangerous, not much info + don't see other interacting pattern than those would expect

NOT TRUE ONLY ONE EXPLANATION WORTHWHILE TO DO - ALL ARE: LOOK AT DIFFERENT ASPECTS

MULTI-LEVEL SYSTEMS IN BIOLOGICAL SYSTEMS REQUIRE MULTI-LEVEL EXPLANATIONS