DH: Modeling need to address real problems.
=> plant Form and function in sbio115:
- essential nutrients.
transportation in celery stalk (transpiration, stomata),
transportation data was messy.
-> shade responses mediated by phyotchromes
phytochroms in two forms: Pr form, Pfr form, influenced by red and far-red photons.
-> Plant growth models:
plant growth model +/- iron. experiment done in petri dish.
use dry-weight to measure growth of plants (arabdopsis).
DH: how iron influence growth function? Can simulation enhance the actual learning objective?
Using various models to model the sigmoidal growth curves?
"Why modeling?" ---> Prediction, Quantification. (but would this impress the unders?)
Every model is a hypothesis. Different model fitting are quantitative way to test different hypotheses.
Learning objective should use "verb" students be able to do sth. "Understand" is a state of mind, not measurable.
Use sigmoid effects to explain the growth-limiting effect of certain nutrients?
Growth function = Growth_Function(Fe) X Growth_Function(N)
Overall Fitness is a production
Viner systesm with O2 and CO2 sensors for a leaf in a chamber.
Large seeds took longer time to show effect of soil on growth.
water transport in celery stalk.
water with dye, traverling up the stalk.
Plants tend to close stomata during dark, thereby reduces transpiration. (However, there is some adjustment time).
xylen, minerals from bottom to up,
pholem, sugar from up to bottom,
Pressure gradient in plants
water pressure in plants, always negative, highly in roots, lowest in leaves,
=> Animal form and function in SBIO115
-> hormonal feedback regulation
positive and negative feedback?
-> Hemoglobin-oxygene dissociation curve
-> negative feedback : homeostatis
-> positive feedback: more commons in developmental stage? pattern formation?
-> Glucose homoestatis
West, G. B., Brown, J. H. & Enquist, B. J. A general model for ontogenetic growth. Nature 413, 628-631, doi:http://www.nature.com/nature/journal/v413/n6856/suppinfo/413628a0_S1.html
=> What's out there on the modeling aspect?
Exposure to q-bio. student attitude change towards quantitative modeling and computational studies (tools). Interests to take up-level qbio, math and cs courses?
Thinking mechanistically, "think deeper"
What's kind of outcomes to convince other faculty to buy-in the qbio approach?