algae/complex_down_regulation/talk_outline.htm

outline for NWAS 2002 talk

December 2, 2001

Regulation of algal physiology: applying complex models to the CSR framework

1. Statement of the problem

friendly ammendment to the CSR framework is that there must be different types of regulation for C, S, & R type cells

C - efficient, homeostasis energy to push the metabolism toward an efficient use of resources

S - differentiation or other metabolic innovation that will lead to avoidance of competition under stress conditions

R - pattern recognition or entrainment

 

2. Regulatory logic in the cells

Three descriptive examples of logic

simple feedback - NADPH increases and shuts off production

a little more complicated -

longer string -

these represent shorter to longer algorithms for describing the outcome of NADPH

3. Goal and limitations of my research

attempt to look at regulation as related to these strategies

what are the characteristics

can only measure outcomes at physiological level

some types of regulation would be too fast, such as the balance hypothesis for the relationship of absorbtion between PS2 and PS1

some would be too long, too many growth paratmeters, genetic regulation

down regulation of photosynthesis happens at the right time scale

sidebar - description of down regulation

model for down regulation

descriptive with 6 parameters

 

4. Models: time step/dynamic vs. Boolean

physiological models that use time sequence of responses and threshold or "fancy" response between input and output

Boolean - input either high or low, on or off,

These two types of models show the same behaviors

With Boolean models

can examine the result of all input states exhaustively

in this case with 6 parameters, there are 64 possible starting states and each has a single possible output state

shows the same types of behaviors

modelling - poorly behaved dynamic model, wild fluctuations, can see in both which means that these types of poorly behaved systems stems from the underlying logic

 

5. Boolean model description for down regulation

6 parameters

diagram

examples of simple logic

light stays on A2=A1

NADPH from above

 

6. The "random grammar approach"

for each parameter, generate multiple examples of logic

the length of the string is the algorithmic length

since we can't examine all the logics exhaustively, sample random combinations of these

each set has an algorithmic length -

characterize the outcomes

number of possible states hit in time 2

 

Figure X. Number of possible states in time 2 versus the alogithmic length of the logic statements. The algorithmic length was calculated as the length of the strings for the logic statements as expressed in EXCEL.

This figure shows a maximum of number of states for intermediate algorithmic length. An intermediate length would be a good candidate for R-type regulation grammars.

 

number of basins,

a few large basins, whether they are periodic or point, would be hypothesized to be good level of grammar for S-type regulation.

point attractors

simple logic - leading to point attractors

 

7. Describing outcomes from the random grammar exercise in terms of CSR

In regulatory space; X axis is algorithmic_complexity and Y axis is either number_of_states, number_of_basins, or length_of_basins

C strategists should fall in the area of short algorithmic complexity and few states

S strategists should fall in the region with more algorithmic complexity and sharply delineated basins

R strategists should have long basins

This is a type of pattern matching, but should not be confused with a highly cognitive level of analysis.

It is a simple algorithm that looks for a pattern, if it doesn't find a beneficial pattern it resets and starts over again. The cell is constantly probing and reseting its state and is driven toward a single, efficient state.

Ruderal algae do a dance to the environmental music. If they start their dance early, they just keep restarting until they get in step. The grammar provides the choreography to the environmental music

These schemes should make some sense physiologically, but the value isn't that these describe particular regulation strategies but shows the relationship between

8. Conclusion

might help us look at regulation differently - not always an optimization goal

homeostasis - simple control to remain in efficient state

oscillation -

differentiation

cell state progressions - the dance

R strategists in persistant disequilibria

help analyze the literature

example

 

help design experiments to look at the R strategies

give an example here