My notes on the four macromolecules that are essential to life as we know it: proteins, fats (lipids), carbohydrates (saccharides), and nucleic acids.
Entries Categorized as 'Natural World'
January 10, 2017
September 25, 2016
On this year’s trip to the Current River with the Middle School we were able to see outcrops of the three major types of rocks: igneous, metamorphic, and sedimentary.
We stopped by Elephant Rocks State Park on the way down to the river to check out the gorgeous pink granite that makes up the large boulders. The coarse grains of quartz (translucent) interbedded with the pink orthoclase crystals make for an excellent example of a slow-cooling igneous rock.
On the second day out on the canoes we clambered up the rocks in the Prairie Creek valley to see jump into the small waterfall pool. The rocks turned out to look a lot like the granite of Elephant Rocks if the large crystals had been heated up and deformed plastically. This initial stage of the transformation allowed me to talk about metamorphic rocks althought we’ll see some much more typical samples when we get back to the classroom.
We visited a limestone cave on the third day, although we’ve been canoeing through a lot of limestone for on the previous two days. This allowed us to talk about sedimentary rocks: their formation in the ocean and then uplift via tectonic collisions.
The Rock Cycle
Back at camp, we summarized what we saw with a discussion of the rock cycle, using a convergent plate margin as an example. Note: sleeping mats turned out to be excellent models for converging tectonic plates.
Note to self: It might make sense to add extra time at the beginning and end of the trip to do some more geology stops. Johnson Shut-Inns State Park is between Elephant Rocks and Eminence, and we saw a lot of interesting sedimentary outcrops on the way back to school as we headed up to Rolla.
September 13, 2016
I’m teaching a numerical methods class that’s partly an introduction to programming, and partly a survey of numerical solutions to different types of problems students might encounter in the wild. I thought I’d look into doing a session on genetic algorithms, which are an important precursor to things like networks that have been found to be useful in a wide variety of fields including image and character recognition, stock market prediction and medical diagnostics.
The ai-junkie, bare-essentials page on genetic algorithms seemed a reasonable place to start. The site is definitely readable and I was able to put together a code to try to solve its example problem: to figure out what series of four mathematical operations using only single digits (e.g. +5*3/2-7) would give target number (42 in this example).
The procedure is as follows:
- Initialize: Generate several random sets of four operations,
- Test for fitness: Check which ones come closest to the target number,
- Select: Select the two best options (which is not quite what the ai-junkie says to do, but it worked better for me),
- Mate: Combine the two best options semi-randomly (i.e. exchange some percentage of the operations) to produce a new set of operations
- Mutate: swap out some small percentage of the operations randomly,
- Repeat: Go back to the second step (and repeat until you hit the target).
And this is the code I came up with:
''' Write a program to combine the sequence of numbers 0123456789 and the operators */+- to get the target value (42 (as an integer)) ''' ''' Procedure: 1. Randomly generate a few sequences (ns=10) where each sequence is 8 charaters long (ng=8). 2. Check how close the sequence's value is to the target value. The closer the sequence the higher the weight it will get so use: w = 1/(value - target) 3. Chose two of the sequences in a way that gives preference to higher weights. 4. Randomly combine the successful sequences to create new sequences (ns=10) 5. Repeat until target is achieved. ''' from visual import * from visual.graph import * from random import * import operator # MODEL PARAMETERS ns = 100 target_val = 42 #the value the program is trying to achieve sequence_length = 4 # the number of operators in the sequence crossover_rate = 0.3 mutation_rate = 0.1 max_itterations = 400 class operation: def __init__(self, operator = None, number = None, nmin = 0, nmax = 9, type="int"): if operator == None: n = randrange(1,5) if n == 1: self.operator = "+" elif n == 2: self.operator = "-" elif n == 3: self.operator = "/" else: self.operator = "*" else: self.operator = operator if number == None: #generate random number from 0-9 self.number = 0 if self.operator == "/": while self.number == 0: self.number = randrange(nmin, nmax) else: self.number = randrange(nmin, nmax) else: self.number = number self.number = float(self.number) def calc(self, val=0): # perform operation given the input value if self.operator == "+": val += self.number elif self.operator == "-": val -= self.number elif self.operator == "*": val *= self.number elif self.operator == "/": val /= self.number return val class gene: def __init__(self, n_operations = 5, seq = None): #seq is a sequence of operations (see class above) #initalize self.n_operations = n_operations #generate sequence if seq == None: #print "Generating sequence" self.seq =  self.seq.append(operation(operator="+")) # the default operation is + some number for i in range(n_operations-1): #generate random number self.seq.append(operation()) else: self.seq = seq self.calc_seq() #print "Sequence: ", self.seq def stringify(self): seq = "" for i in self.seq: seq = seq + i.operator + str(i.number) return seq def calc_seq(self): self.val = 0 for i in self.seq: #print i.calc(self.val) self.val = i.calc(self.val) return self.val def crossover(self, ingene, rate): # combine this gene with the ingene at the given rate (between 0 and 1) # of mixing to create two new genes #print "In 1: ", self.stringify() #print "In 2: ", ingene.stringify() new_seq_a =  new_seq_b =  for i in range(len(self.seq)): if (random() < rate): # swap new_seq_a.append(ingene.seq[i]) new_seq_b.append(self.seq[i]) else: new_seq_b.append(ingene.seq[i]) new_seq_a.append(self.seq[i]) new_gene_a = gene(seq = new_seq_a) new_gene_b = gene(seq = new_seq_b) #print "Out 1:", new_gene_a.stringify() #print "Out 2:", new_gene_b.stringify() return (new_gene_a, new_gene_b) def mutate(self, mutation_rate): for i in range(1, len(self.seq)): if random() < mutation_rate: self.seq[i] = operation() def weight(target, val): if val <> None: #print abs(target - val) if abs(target - val) <> 0: w = (1. / abs(target - val)) else: w = "Bingo" print "Bingo: target, val = ", target, val else: w = 0. return w def pick_value(weights): #given a series of weights randomly pick one of the sequence accounting for # the values of the weights # sum all the weights (for normalization) total = 0 for i in weights: total += i # make an array of the normalized cumulative totals of the weights. cum_wts =  ctot = 0.0 cum_wts.append(ctot) for i in range(len(weights)): ctot += weights[i]/total cum_wts.append(ctot) #print cum_wts # get random number and find where it occurs in array n = random() index = randrange(0, len(weights)-1) for i in range(len(cum_wts)-1): #print i, cum_wts[i], n, cum_wts[i+1] if n >= cum_wts[i] and n < cum_wts[i+1]: index = i #print "Picked", i break return index def pick_best(weights): # pick the top two values from the sequences i1 = -1 i2 = -1 max1 = 0. max2 = 0. for i in range(len(weights)): if weights[i] > max1: max2 = max1 max1 = weights[i] i2 = i1 i1 = i elif weights[i] > max2: max2 = weights[i] i2 = i return (i1, i2) # Main loop l_loop = True loop_num = 0 best_gene = None ##test = gene() ##test.print_seq() ##print test.calc_seq() # initialize genes =  for i in range(ns): genes.append(gene(n_operations=sequence_length)) #print genes[-1].stringify(), genes[-1].val f1 = gcurve(color=color.cyan) while (l_loop and loop_num < max_itterations): loop_num += 1 if (loop_num%10 == 0): print "Loop: ", loop_num # Calculate weights weights =  for i in range(ns): weights.append(weight(target_val, genes[i].val)) # check for hit on target if weights[-1] == "Bingo": print "Bingo", genes[i].stringify(), genes[i].val l_loop = False best_gene = genes[i] break #print weights if l_loop: # indicate which was the best fit option (highest weight) max_w = 0.0 max_i = -1 for i in range(len(weights)): #print max_w, weights[i] if weights[i] > max_w: max_w = weights[i] max_i = i best_gene = genes[max_i] ## print "Best operation:", max_i, genes[max_i].stringify(), \ ## genes[max_i].val, max_w f1.plot(pos=(loop_num, best_gene.val)) # Pick parent gene sequences for next generation # pick first of the genes using weigths for preference ## index = pick_value(weights) ## print "Picked operation: ", index, genes[index].stringify(), \ ## genes[index].val, weights[index] ## ## # pick second gene ## index2 = index ## while index2 == index: ## index2 = pick_value(weights) ## print "Picked operation 2:", index2, genes[index2].stringify(), \ ## genes[index2].val, weights[index2] ## (index, index2) = pick_best(weights) # Crossover: combine genes to get the new population new_genes =  for i in range(ns/2): (a,b) = genes[index].crossover(genes[index2], crossover_rate) new_genes.append(a) new_genes.append(b) # Mutate for i in new_genes: i.mutate(mutation_rate) # update genes array genes =  for i in new_genes: genes.append(i) print print "Best Gene:", best_gene.stringify(), best_gene.val print "Number of iterations:", loop_num ##
When run, the code usually gets a valid answer, but does not always converge: The figure at the top of this post shows it finding a solution after 142 iterations (the solution it found was: +8.0 +8.0 *3.0 -6.0). The code is rough, but is all I have time for at the moment. However, it should be a reasonable starting point if I should decide to discuss these in class.
September 6, 2016
A couple new article relevant to our study of Earth History.
Research on the high pressure and temperature conditions at the Earth’s core suggest that most of the carbon in the early Earth should have either boiled off into space or been trapped by the iron in the core. So where did all the carbon necessary for life come from? They suggest from the collision of an embryonic planet (with lots of carbon in its upper layers) early in the formation of the solar system.
It took a few billion years from the evolution of the first photosynthetic cyanobacteria to the time when there was enough oxygen in the atmosphere to support animal life like us. Why did it take so long? NPR interviews scientists investigating purple microbial mats in Lake Huron.
September 5, 2016
MolView is a great site for drawing molecules and rendering them in 3D.
May 22, 2016
These three excellent, short videos on John Snow’s life and work on cholera do a nice job of describing what makes for good science–careful observation; good notes; creative analysis of data, etc. They should make a good “spark your imagination” introduction to biological science.
They also have an excellent explanation of all the ‘lies’ and liberties they took in the making of the video.
March 10, 2016
For our annual fundraiser’s silent auction, I made a chess board. The structure was made of wood–I learned how to use dowels to attach the sides–but the black squares were cut out of the material they use for matting the borders of pictures. My student drew “cheat sheet” diagrams of each of the black squares in bright gel pen colors. The squares were laid on a white grid and the entire top epoxied with a clear glass-like coat. We also made two sets of chess pieces with the 3d printer (rounds versus squares).
It turned out quite well.
February 5, 2015
One of my students wanted to figure out how to make animals photosynthesize. Well, this article indicates that sea slugs have figured out how eat and digest the algae but keep the algal chloroplasts alive in their guts so the sea slug can use the fats and carbohydrates the chloroplasts produce (the stealing of the algal organelles is called kleptoplasty). To maintain the chloroplasts, the slugs have actually had to incorporate some of the algae DNA into their own chromosomes–this is called horizontal gene transfer and it’s what scientists try to do with gene therapies.
More details here.