MFCs illustrate key concepts for introductory biology students:

1. Respiration consists of electron-transfer reactions that take electrons from organic molecules and transfer them to an electron transport chain localized in the membrane, and ultimately to a terminal electron acceptor.
2. In the absence of oxygen, microbial anaerobic respiration uses alternative terminal electron acceptors other than oxygen.

The Mudwatt by Keegotech is a simple microbial fuel cell. It features living soil, a container, and two electrodes. The key is that the two electrodes are placed in different redox environments. One electrode (the anode) is 5-6 cm deep in the soil, in an anoxic layer. The other electrode (the cathode) is at the top of the soil, exposed to air containing oxygen.

Microbes at the anode metabolize organic carbon through anaerobic respiratory pathways. As in aerobic respiration, anaerobic respiration oxidizes organic carbon to reduce NAD+ to NADH. The electrons flow from NADH through the electron transport chain localized in the plasma membrane to generate a pH gradient across the membrane. The resulting proton motive force powers oxidative phosphorylation of ATP from ADP and inorganic phosphate.

However, in the absence of oxygen, electrons flowing down the electron transport chain reduce (give electrons to) alternative electron acceptors such as nitrate or sulfate. The difference in redox potential between NADH (-320 mV) and the terminal electron acceptor determines how much ATP the bacterium can make per mole of organic carbon respired. Bacteria undergoing aerobic respiration make the most ATP because oxygen at +820 mV has the highest redox potential among electron acceptors, resulting in a net potential difference from NADH of 1.14 V. Bacteria in anoxic sediments that reduce sulfate (-220 mV) make much less ATP per mole of organic carbon respired, with only 100 mV of potential difference from NADH.

Redox potentials for electron acceptors for microbial respiration

Electrogenic bacteria can reduce extracellular terminal electron acceptors. Using either soluble electron carriers or electrically conductive pilli, they can reduce a solid metal or graphite anode. The microbial fuel cell provides an electrical circuit to take electrons from the anode to a cathode placed in an oxygen-rich air/water interface. The difference in redox potential between sulfate and oxygen results in a maximum theoretical voltage of approximately 1 Volt.

Questions:

What is the source of the electrons produced by these bacteria?

How do these electrons get from the source molecules to the bacterial plasma membrane?

Compare and contrast aerobic versus anaerobic respiration.

Can bacteria running fermentation reactions generate electricity in a MFC?

Resources and references:

Gorby, YA, Yanina, S, McLean, JS, Rosso, KM, Moyles, D, Dohnalkova, A, Beveridge, TJ, Chang, IS, Kim, BH, Kim, KS, Culley, DE, Reed, SB, Romine, MF, Saffarini, DA, Hill, EA, Shi, L, Elias, DA, Kennedy, DW, Pinchuk, G, Watanabe, K, Ishii, S, Logan, B, Nealson, KH and Fredrickson, JK, 2006. Electrically conductive bacterial nanowires produced by Shewanella oneidensis strain MR-1 and other microorganisms, Proc Natl Acad Sci USA 103:11358-11363. DOI: 10.1073/pnas.0604517103

http://www.nsf.gov/news/special_reports/science_nation/wastetoenergy.jsp?WT.mc_id=USNSF_51

B1510_module3_5_respiration_questions_2011Fall – slides with clicker questions

Advanced-Intro-to-MFCs (Keegotech)

• homologous chromosomes pair up and align in prophase I
• crossing over occurs between homologous chromosomes in prophase I
• homologous chromosomes separate to daughter cells, and sister chromatids do not separate
• the first division is when daughter cells become functionally or genetically haploid

The last point appears to be the most difficult for students to grasp. I illustrate this point using the X and Y chromosomes. They pair in prophase I, and then separate in the first division. The daughter cells have either an X or a Y; they don’t have both. Each cell now has only one sex chromosome, like a haploid cell.

Several years ago I designed an interactive flash tutorial, with programming assistance from Pearson. It can be used as a classroom demonstration, or a homework exercise for students. It uses human chromosome 7, with wild type and cystic fibrosis alleles for CFTR, to track segregation through meiosis, with and without crossing over.

Meiotic Segregation tutorial

An international collaboration of scientists from DOE Joint Genome Institute, the Bigelow Laboratory, the Monterey Bay Aquarium Research Institute, the University of Vienna, and MIT used leading-edge single-cell genome amplification and sequencing technologies to characterize the prokaryotic communities in the dark ocean (Figure 1).

Fig. 1 Proportions of bacterial and archaeal single-cell amplified genomes

The bacterial communities at 800 m differed markedly from the surface waters (note the absence of the cyanobacterium Prochlorococcus), but were similar between the North Pacific and South Atlantic research stations. The SAR11 group of bacteria is abundant in both surface waters and deep waters. A recent phylogenomic analysis found that the SAR11 bacteria share a common ancestor with mitochondria (Thrash et al. 2011).

Screening the amplified genomes revealed that at least 12% of the dark-ocean bacteria possess genes for Rubisco. Labeling with carbon-14 bicarbonate showed that at least one of these groups of bacteria with Rubisco genes assimilates inorganic carbon (Figure 3A and 3B).

Fig. 3 Micrographs demonstrating bicarbonate uptake and aggregation around particles by Deltaproteobacteria SAR324.

So what is Rubisco doing in an environment with no light? How can the Calvin cycle operate without the products of the light reactions?

Clearly, these deep-ocean bacteria with Rubisco that assimilate inorganic carbon cannot be photoautotrophs; they must be chemoautotrophs. Most of the bacterial genomes that have genes for Rubisco also have genes for oxidation of reduced sulfur compounds. Like many anoxygenic photosynthetic bacteria, they obtain reducing power from sulfur to reduce NAD+ to NADH and drive the Calvin cycle. They are most likely mixotrophs, obtaining energy and carbon both from metabolism of organic food and from chemoautotrophic fixation of inorganic carbon.

What’s the takeaway? Rubisco is not just for photosynthesis. Rubisco is hard at work even in the deep, dark ocean, running a carbon sink that can help counteract increasing atmospheric carbon dioxide.

Questions for Intro Biology students:

1. Is the oxygenase activity of Rubisco relevant in the dark ocean? Explain.

2. What does the Calvin cycle need to fix carbon dioxide and to regenerate RuBP?

3. How are these bacteria making ATP? Examine Figure 3C – are they most likely running aerobic or anaerobic respiration? Are they making ATP mostly through oxidative phosphorylation or exclusively through substrate-level phosphorylation?

4. Where is the electron transport chain and ATP synthase in these bacteria?

References:

Science Daily – Up from the depths: how bacteria capture carbon in the “twilight zone” http://www.sciencedaily.com/releases/2011/09/110901142054.htm

Swan et al. 2011, Potential for Chemolithoautotrophy Among Ubiquitous Bacteria Lineages in the Dark Ocean. Science 333: 1296-1300 DOI: 10.1126/science.1203690

Thrash et al. 2011, Phylogenomic evidence for a common ancestor of mitochondria and the SAR11 clade. Scientific Reports 1: article 13 doi:10.1038/srep00013

When reading nucleotides, there are several possible reading frames depending on how you group codons. For example, in mRNA there are three possible reading frames all starting on a different nucleotide, leading to the possible interpretation of 3 different codon sequences. Due to this phenomena, you can have overlapping genes in the same sequence which have different reading frames. An open reading frame (ORF) is a reading frame that does not contain a stop codon and insertions or deletions (in a non-multiple of 3) cause frameshift mutations and dislocate the sequence for stop codons. The presence of same strand overlapping ORFs can either be caused by frame shifted genes producing multiple ORFs or they can be true overlapping/adjacent genes. This blog entry summarizes a paper entitled “GeneTack: Frameshift Identification in Protein-Coding Sequences by the Viterbi Algorithm,” published in the “Journal of Bioinformatics and Computational Biology” (Vol. 8, No. 3).

Introduction

Frameshifts found in protein sequences can either be correct (the result of mutation) or the result of a sequencing error. Traditionally there have been two groups of programs that detect frameshifts of both kinds: comparative genomics and single sequence (ab initio). Comparative genomics, or similarity search, search the translation of the ORF in known protein databases for a hit, which leads to the limitation that it is impossible to detect frameshifts in genes with no known homologs. Ab initio methods are not hindered by this limitation. Presented is a new algorithm for intron-less nucleotide sequences, in particular those of a prokaryote genome. Prokaryote genomes are suitable for ab initio methods since they have one long, continuous ORF for each gene.

GeneTack is a program designed to run on DNA fragments with all genes located in the same strand. It employs the use of a Hidden Markov Model and a dynamic programming algorithm known as the Viterbi Algorithm. In order to process actual sequence data, GeneTack-GM is a combination program that is a wrapper for GeneTack and uses GeneMarkS to parse whole genomes into fragments with collinear genes. The program predicts both natural and error-related frameshifts, although natural predictions are not as accurate as other programs since it does not use signaling sequence information.

GeneTack Algorithm

The GeneTack Algorithm can be broken into 3 steps:

1. Algorithm takes in a fragment of the genomic sequence containing collinear genes in the direct strand.
2. A probabilistic Hidden Markov Model is produced allowing for different scenarios.
3. The Viterbi Algorithm is applied to the HMM to determine the maximum likelihood path.

In step 1, a frameshift may result in the prediction of two adjacent genes. GeneTack attempts to discriminate between correctly predicted adjacent genes and adjacent genes produced due to a sequence error (ie a split of a single gene by frameshift). A probabilistic Hidden Markov Model is constructed that allows for three scenarios: the presence of true overlapping genes, true non-overlapping adjacent genes, and adjacent genes predicted due to presence of a frameshift. The HMM, shown in Figure 1, consists of 28 states divided into 4 groups.

1. States 1, 2, and 3 emit protein coding sequences related to the 3 possible global reading frames.
2. The state denoted as “n/c” emits a non-coding sequence.
3. The states denoted as “i-j” emit sequences where two adjacent genes overlap, with i and j correspond to the global reading frame of the upstream and downstream gene, respectively.
4. 18 states emitting nucleotide of start (triangle) and stop (square) codons.

The HMM is similar to a state transition diagram, with each hidden state emitting a single nucleotide. The initial hidden state in analysis should be either “n/c,” “start,” or “stop.” Each transition has an associated probability value.

The Viterbi Algorithm is a dynamic programming algorithm with a variety of applications. When applied to graphs (like one produced by the HMM), the viterbi algorithm can be used to determine maximum likelihood paths. Using the probability values at each “node” in the graph (the different states), the Viterbi Algorithm can calculate the probabilities of various paths with an efficient runtime. Once the maximum likelihood path is discovered, the transitions between the various states in the path indicate specific information:

• Direct transitions between states 1, 2, and 3 correspond to frameshifts
• Transitions between 1, 2, and 3 passing through the “n/c” state indicate non-overlapping adjacent genes
• Transitions between 1, 2, and 3 that pass through an i-j state indicate overlapping adjacent genes

GeneTack-GM Algorithm

In order to run GeneTack, one needs to estimate parameters and parse the sequence into fragments. GeneTack-GM accomplishes this by using GeneMarkS. Figure 2 depicts the logic of operations in the GeneTack-GM program.

Initially, GeneMarkS is run for several iterations to determine the HMM parameters. At completion of the “training process,” GeneMarkS defines the set of predicted genes. The output is used to split the sequence into fragments, which the GeneTack program analyzes to identify possible frameshifts. Finally, several filters are applied to reduce the number of false positives. It should be noted slight modifications were made to the algorithm for high GC-content (guanine-cytosine) genes.

Datasets

GeneTack-GM was assessed on 17 prokaryote genomes with GC-content randing from 28%-75%. The original E. Coli genome, used for the training process (estimating program parameters), was not included in any of the datasets. From the 17 prokaryote genomes, datasets were generated to test performance at different gene lengths by simulating frameshifts in a randomly selected gene at a random position of the gene, at least a certain distance (insensitivity zone) from either gene end. Dataset_1000 simulated frameshifts in 400 genes of length greater than 1,000bp. Dataset_600_1000 simulated frameshifts in 200 genes of length 600-1,000bp.

Results

GeneTack was compared to two other frameshift detection programs, FrameD and FSFind. All three programs were applied to both datasets. The coordinates of the predicted frameshift were compared to the coordinates of the known simulated frameshift. A True Positive (TP) is a predicted frameshift within 50bp of an actual frameshift. A False Positive (FP) is a predicted frameshift further than 50bp from an actual frameshift. A False Negative (FN) resulted from no predicted frameshift within 50bp of a known frameshift. The performance was calculated using the conventions of Sensitivity (Sn) and Specificity (Sp) Sensitivity, Sn = (TP)/(TP+FN) is defined with respect to the actual number of frameshifts. Specificity, Sp = (TP)/(TP+FP) is defined with respect to the number of predictions made. The average of these two values were used to evaluate performance. Table 3 lists the results for Dataset_1000.

In the Dataset_1000, GeneTack-GM outperformed FrameD and FSFind by a margin of 9.4% and 9.1%, respectively. In Dataset_600_1000, GeneTack outperformed FrameD and FSFind by 5.9% and 9.9%, respectively.

Analysis

GeneTack-GM provides the most accurate frameshift detection out of the programs tested. However, certain questions arise. One such example is whether GeneTack can be used to predict programmed frameshifts. Some genes have evolved sequences that induce frameshifting by altering the ribosomal frame during protein translation, known as programmed frameshifts. To determine this, GeneTack was applied to 23 sequences with +1 and -1 annotated programmed frameshifts. GeneTack was able to successfully predict frameshifts in 18 of these sequences.

It is difficult to detect frameshifts near the start of end of a gene, known as insensitivity zones. For the Dataset_1000, an insensitivity zone of 180bp was used, but how was this value determined? In setting the insensitivity zone, frameshifts were introduced at 5 nucleotide steps from 1 to 200bp from the gene border. It was observed that the accuracy of detection steadily increased with offset from the gene end, topping off around 90% at a distance of 180bp in genes of greater than 1,000bp. It was also observed that GeneTack is able to detect frameshifts closer to the gene end better than those closer to the start end. This phenomena can be explained due to the logistics that it is easier to predict adjacent genes downstream rather than upstream, correlating to the number of start and stop codons in the genetic code.

A variety of filers were applied to the GeneTack data. In the proceeding data, filters removed 72% of false positives while keeping 91% of true positives. However, one might ask how to improve the filters. One possibility is to have filter parameters dependent on GC-content.

Can GeneTack-GM be adapted to other genomic sequences of intron-less genes? One possible application in metagenomics is to use GeneTack with heuristic models to predict genes in short sequences.

This paper was originally revised on February 12^th, 2010. I was unable to find any new frameshift detection algorithms produced since then.

Proteins are assembled as a linear chain of amino acids covalently linked by peptide bonds. As this chain is being assembled (each subsequent amino acid is added onto the free carboxy- terminus of the nascent polypeptide chain), the polypeptide chain begins to fold. This folding process is determined by interactions between non-adjacent amino acid peptide backbone atoms, between the amino acid side chains, and between the amino acid side chains and the aqueous environment.

Biologists distinguish 4 levels of protein structure. Students should be able to identify the four levels of protein structure, and the molecular forces or interactions responsible for stabilizing each level of structure.

Primary – the linear sequence of amino acids, held together by covalent peptide bonds.

Secondary – alpha helices and beta sheets, stabilized by hydrogen bonds between peptide backbone atoms.

Tertiary – overall 3-D shape of the folded polypeptide chain, that can be described as the spatial relationships of the secondary structure elements linked by loops. Stabilized by various types of amino acid side chain interactions, including: hydrophobic interactions, hydrogen bonding, ionic bonds, covalent disulfide bonds between cysteine residues, and interactions with solvent water molecules.

Quaternary – assemblage of two or more folded polypeptides into a functional unit. Stabilized by interchain hydrophobic interactions, hydrogen bonding, ionic bonds, and covalent disulfide bonds between cysteine residues on different polypeptide chains.

Case examples:

1. The classic case exploring protein structure is hemoglobin. Functional hemoglobin is a tetramer, consisting of two alpha-globin and two beta-globin polypeptide chains. Hemoglobin also requires a cofactor, heme (also called a prosthetic group), containing an iron atom that binds oxygen.

Questions:

a) What levels of protein structure does hemoglobin exhibit?

b) The most common sickle-cell disease mutation changes a glutamic acid (a negatively charged amino acid) in beta-globin to valine (a hydrophobic amino acid). Where would you most commonly expect to find a charged amino acid like glutamic acid, in the interior of the folded protein or on the surface?

c) Which of the following changes do you think might also cause sickle-cell disease?

i) the glutamic acid changes to an aspartic acid, a different negatively charged amino acid

ii) the glutamic acid changes to a lysine, a positively charged amino acid

iii) the glutamic acid changes to a tryptophan, a hydrophobic amino acid

iv) the glutamic acid changes to a serine, an uncharged, hydrophilic amino acid

d) Sickle cell hemoglobin mutations alter what levels of protein structure (when sickling of red blood cells is apparent)?

My lecture video is posted below:

A promising avenue for treatment of sickle cell disease by inducing expression of fetal gamma-hemoglobin was published on-line in Science:

Xu et al., 2011 Correction of Sickle Cell Disease in Adult Mice by Interference with Fetal Hemoglobin Silencing, Science DOI: 10.1126/science.1211053

See Harvard U. press release

2. The most common mutation associated with cystic fibrosis causes a single amino acid, a phenylalanine, to be omitted from the protein called CFTR (cystic fibrosis transmembrane conductor). The CFTR protein functions as a chloride channel in the membrane, formed as the single long CFTR polypeptide chain crosses the membrane back and forth several times. The absence of this phenylalanine, which has a large hydrophobic side chain, causes the protein to be mis-folded. This mis-folded protein is recognized by the cellular quality control system and sent to the cellular recycling center (the proteasome); only about 1 percent makes it to the proper destination, the plasma membrane. My case study is published as a blost post:

Cystic Fibrosis: A Case Study for Membranes and Transport

3. Microbes that live in extreme environments of temperature, salt and pH have proteins that are adapted for structural stability in these extreme environments.

CF affects multiple tissues and organs in affected patients. CF patients have hypersaline (extra salty) sweat, have trouble digesting food and gaining normal weight, and as teens and adults suffer from respiratory symptoms and eventually die of complications of lung infections and respiratory disease. Half the patients die before they reach their late 30′s. Life expectancy for the most severe forms are shorter. CF is a life-long disease, requiring constant management:

Here’s a historical view of the changes in the median survival age for CF patients, along with developments in treatment:

Improving survival in CF, from http://knol.google.com/k/jerry-nick-m-d/cystic-fibrosis/UtI7gr91/HU2bIw#

Note that before 1940, the survival age is less than 1 year. In medieval times, midwives reportedly licked the foreheads of newborns, and those with extra salty sweat were abandoned to die, because they would die within a few weeks anyway of malnutrition, no matter how much breast milk they consumed. As treatments are found that prolong patients’ lives, additional complications arise in later ages.

What’s the common factor behind these multiple symptoms affecting different organs and body systems? A disorder in osmosis, caused by the failure of CFTR to conduct chloride (Cl-) ions across cell surface membranes. Cells lining the small airway passages of the lung and the pancreatic ducts have CFTR protein on their plasma membranes in position to conduct Cl- ions into or out of the airways or ducts.

CFTR is an integral membrane protein with multiple transmembrane domains, as shown in this figure from Wikipedia:

CFTR cartoon from Wikipedia, originally from 1989 Journal of NIH Research

The CFTR protein is made by cells that line small airway passages of the lung, and the secretory ducts in the pancreas, and sweat glands in the skin, among others.

In what compartment of these cells will the CFTR protein be synthesized?

If you wanted to track the synthesis and eventual cellular location of isotopically labeled CFTR, what isotope would you use: 32P, 35S, or 15N?

In such labeling experiments, cells are grown in medium containing isotopically labeled subunits that are incorporated into the type of macromolecule the experimenter wants labeled. To label newly synthesized proteins, the cells should be given what type of labeled subunits (amino acids, monosaccharides, nucleotides, or fatty acids)?

Describe the route of CFTR from its site of synthesis to its final location, the plasma membrane, naming the cellular compartments or organelles in order.

The most common mutation in CF patients is called deltaF508. In this mutation, phenylalanine, the 508th amino acid in the CFTR polypeptide chain, is missing. This amino acid is normally located in the nucleotide binding domain 1 (NBD1), and is part of an alpha-helix. The figures below show the location of NBD1 in a schematic of CFTR domains and the location of F508 (Phe508) in a model of NBD1.

Diagram depicting the five domains of the CFTR membrane protein (Sheppard 1999).

Theoretical Model of NBD1, from http://www.ornl.gov/sci/techresources/Human_Genome/posters/chromosome/cftr.shtml

Phenylalanine is a hydrophobic amino acid. Keeping in mind that the structure of the complete CFTR protein has not yet been determined (only the NBD1 domain part has been modeled), what do you think would be the most likely location of Phe508 in the structure of the native CFTR protein in the cell? Do you think it’s on the surface interacting with water molecules? If it is on the surface of NBD1 as shown, what might that imply?

In the mutant deltaF508 CFTR protein, what levels of protein structure (primary, secondary, tertiary, quaternary) must be altered, given the information thus far?

The cell recognizes that the deltaF508 mutant CFTR protein is mis-folded, and most of it is destroyed. Less than 1% reaches the plasma membrane.

At the plasma membrane, CFTR acts as a gated chloride ion channel. Once opened, chloride ions are free to diffuse through the channel down the concentration gradient. This is an example of what type of transport? If you plotted the rate of chloride transport versus the difference in chloride concentration across the membrane, what would this plot look like?

To understand why the lack of CFTR function causes problems, let’s look at the small airways in the lung. The cells lining the small airways have cilia that constantly beat to move a thin layer of protective mucus. The mucus layer floats atop a thin layer of liquid. The cilia move the mucus along with any trapped particles and bacteria up through the trachea and to the back of the throat, where it is swallowed into the digestive tract. The cilia require the thin layer of liquid to have room to beat.

To maintain the layer of water, the airway lining pumps sodium ions into the passageway. To balance the charge of the Na+ ions, the CFTR passage opens and allows exit of Cl- ions. If the cells lack CFTR, or the CFTR channel doesn’t open, the charge difference severely limits the amount of Na+ that enters the airway liquid. Without enough NaCl, the airway liquid is hypotonic. Which way will water flow via osmosis, into the airway or into the lining? See the video clip below for the answer. What are the channels that permit water molecules to cross membranes?

Now see what happens as a result:

Without enough water, the cilia cannot beat, the mucus accumulates and dries, and bacteria colonize and establish long-term infections.

The problems in the pancreas are similar: mucus plugs prevent pancreatic digestive enzymes from reaching the GI tract to break down proteins and lipids. In the affected tissues, lack of CFTR chloride channel function causes a problem with osmotic balance and affects secretion.

Now that you’ve explored the biochemical and physiological basis of CF, see if you can make sense of the various treatments for management of CF symptoms.

Sources:

Cystic Fibrosis http://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0001167/

Cystic Fibrosis Gene http://www.ornl.gov/sci/techresources/Human_Genome/posters/chromosome/cftr.shtml

Cystic Fibrosis: History, clinical manifestations and treatment by Jerry Nick, M.D.  http://knol.google.com/k/jerry-nick-m-d/cystic-fibrosis/UtI7gr91/HU2bIw#

From my point of view, I’m having a blast. I’m having a room of 180 students actively discussing biology, using their laptops to look up Wikipedia articles instead of checking email or facebook or shopping, for the entire class period. I’m showing them how biology is not just a collection of facts, but integrates with other disciplines, and how different areas of biology interconnect. They’re looking and pondering the meaning of real data.

The problem is, many of them hate it. Here are some comments students have posted on our class site in Piazza.com:

I believe that we are missing out on the detailed explanations that the teacher gives us during the lecture. Also I think a better way to approach if you didn’t want to get rid of the activity then change it to regular lecture Monday and Wednesday. Then on Friday we could do the group activity, but it should be related to what we learned on Monday and Wednesday. This will actually enhance our knowledge of the subject by applying what we have heard in the lecture. I feel like we are just googling answers to put on a sheet and not understanding anything about it.

I don’t like it. This unit in biology is already inundated with tons of vocabulary and new concepts. Our previous lecture style gave me multiple exposures to the new material (book, lecture, masteringbio). Now i feel like I’m learning everything on my own and going to class to do activities that are somewhat related to the material.

I wish he’d go back to the old lecture style so I could learn more. I feel like there’s a reason 99% of professors teach- because it works.

I feel as though I learned with Dr. [] and would attend class regardless of PRS/attendance points. I can not say the same for this new lecture style as I probably would not come to lectures if attendance was not recorded.

I agree on this subject. I don’t mind doing such activities sometime, but ONLY doing those during lecture time is really not helping me to get the material. Can we at least go through some important concepts in class?

——-
My group members and I agree. I of course mean no disrespect, but at times, we simply can’t fathom Dr. Jung’s explanations. Comprehension is surely easier for the considerable amount of biology-related majors in the course, but I, for example, am a CS major. Chemistry and microbiology aren’t my bailiwicks.

As you stated, I feel as if I’m teaching myself in lieu of being taught. Even then, I find it difficult to teach myself considering I possess a paltry understanding in the first place.
The novel approach Dr. Jung utilizes is admirable, and over time, perhaps it will grow on me. Nevertheless, I don’t wish to be the experimental guinea pig.

I miss the old style of lecture I like the idea of doing activities in class to engage our learning in an active manner, but I feel like I learned more during traditional style lectures.

You get the general idea. There’s been a couple of positive comments, but the sentiment appears to be running heavily negative, although the evidence is anecdotal. Clearly, some students are feeling very uncomfortable. I need to make adjustments in simplifying the activities because one issue appears to be that students are floundering with some of the technical vocabulary – I will streamline the data and have better explanations.

I’m just unsure how to respond to the students. Do I show them data that students don’t really learn from lectures? That people are poor judges of their own learning? I want them to stick with it for at least a while longer. I’ll try to explain my rationale and ask them to give it a good try for the rest of this unit.

I welcome any suggestions or comments from readers.

For my first class, on the topic of biological macromolecules, I built a case study from the controversial report published in Science, that claims that a bacterial strain adapted for growth in a high arsenic environment actually incorporates arsenate into DNA and other macromolecules in lieu of phosphate (Wolfe-Simon et al., 2011). This question could address elements and atomic structures, the Periodic Table and valence electrons, the elemental composition of organic molecules, the structure and composition of macromolecules, evolution and natural selection, the origin of life, and the ongoing process of science.

I asked my freshman biology classes to at least read the abstract and the first two paragraphs (introduction) before class, and answer these three questions (on a worksheet uploaded to T-square, our implementation of Sakai):

1. What are the six major elements that comprise living organisms?

2. What cellular macromolecules contain P?

3. Why is arsenate (AsO₄^3-) toxic to living organisms?

Then, in class, I had them working in groups of 3, 4, or 5 students. Each group had to assign 3 roles: a note-taker who completes the worksheet with the group consensus answer, a questioner who communicates questions the group cannot resolve to the instructor and the rest of the class via Piazza.com (or Twitter), and 1-3 researchers who hunt for information and answers using all the resources available on the internet.

The groups’ first task was to determine from reading the paper what they do not understand, and to list their questions on Piazza.com or Twitter, in order to answer the following questions on the worksheet:

4. Describe to your group mates the procedure Wolfe-Simon et al. used to isolate GFAJ-1. Do you think GFAJ-1 existed in the original inoculum from Mono Lake sediment, or did it come into being during culture?

5. What do you conclude from Figures 1A and 1B? Do these look like logistic growth curves?

6. Compare the measured intracellular content of As and P (Table 1) in cells grown in +As/-P medium and -As/+P medium. What are the As and P concentrations in these media? Do GFAJ-1 cells accumulate (and use) As as well (or as much) as they do P?

7. Wolfe-Simon et al. used a radioactive isotope of As to determine whether As became incorporated into macromolecules (Table 2). Discuss how strongly the data support their claim that they observed arsenic “in protein, metabolite, lipid, and nucleic acid cellular fractions.” Do you have any questions about their fractionation method, before you can make a judgment?

8.Wolfe-Simon et al. explicitly assume that the distribution of As in GFAJ-1 cells would be similar to the distribution of P. Do you think this assumption is valid?

9. Summarize the evidence that As is in the DNA of GFAJ-1 cells. Identify the controls in the experiments.

We then went over the answers to these questions in the last 10 minutes of class, followed by two clicker questions. Many students answered that the Wolfe-Simon demonstrated As was in proteins (based on table 2), and that GFAJ-1 was able to “breathe” arsenic. They just weren’t ready to handle this paper. I’ll have to think about writing a digest that simplifies the experimental approach and focuses on just a couple of the easier to understand data.

Wolfe-Simon, F. et al. 2010, A bacterium that can grow by using arsenic instead of phosphorus. Science doi:10.1126/science.1197258

B1510_module3_1_arseniclife powerpoint slides with clicker questions and figures from Wolfe-Simon et al. paper

ArsenicLife Class Worksheet doc file of in-class group worksheet

I’ve recently reviewed chapters and content dealing with chemistry for both a leading traditional textbook and a new ebook, both for biology majors. I have reviewed hundreds of syllabi for introductory biology courses from all kinds of colleges and universities worldwide, in my role as gatekeeper of transfer credits for biology courses. Both the textbooks and the vast majority of introductory courses begin with chemistry basics, from atomic structure to chemical bonds, the properties of water, the chemistry of carbon, simple organic molecules, and then lipids and biological polymers. I think this approach is tedious to both students and instructors, and fails to integrate chemistry with biology.

Origin of life to introduce elements, atoms and chemical bonds

How did life on Earth get started? What experimental approaches are available to investigate this question? We can start with the following observations:

Q: Are living organisms made of the most abundant elements available in the environment?

Compare elemental (atomic) composition of living organisms: C, H, O, N, P, S, + trace minerals (Ca, Mg, Zn, Mn, Fe) with elemental composition of earth’s crust.

Q: What is special about the most abundant elements of living organisms (elements that comprise organic molecules)?

Elements react with each other to form molecules, held together by chemical bonds. The kinds of reactions and bonds that an element will form is determined by its valence electrons. The Periodic Table organizes elements by their valence electron configurations. All chemical bonds, and all chemical reactions, involve sharing or movement of valence electrons.

Metal atoms, such as those in group 1 and group 2, readily give up their electrons to atoms in group 17. The resulting ions are held together by strong electrostatic attraction, an ionic bond.

Different atoms have different avidities for electrons (electronegativity). Oxygen is one of the most electronegative elements. Carbon and hydrogen have much lower electronegativity. When carbon atoms and hydrogen atoms form chemical bonds with each other, they share their electrons almost equally, resulting in non-polar covalent bonds. When carbon or hydrogen atoms form bonds with oxygen, the oxygen nucleus hogs the shared electrons, such that the oxygen atom acquires a partial negative charge, and the hydrogen or carbon atom has a partial positive charge. Such unequal sharing of electron pairs results in polar covalent bonds.

Organic molecules consist of carbon, hydrogen, oxygen, nitrogen, phosphorus and sulfur. The key element of life is carbon – life on earth is carbon-based. But not all molecules that contain carbon, or carbon, hydrogen and oxygen, are organic molecules. For example, carbon dioxide (CO2) and carbonic acid (H2CO3) are inorganic molecules. The distinction is that organic carbon is reduced.

I put together a short lecture (less than 30 min) in an attempt to capture these ideas, and posted it on YouTube. This was my very first lecture video, for use in my “flipped” class this semester, so it’s rather rough. Do you think this is enough for intro biology students, or am I missing key concepts? Some concepts, like redox, will be explained more fully when I get into energy metabolism, and are well explained already in a Khan academy video.

I’ve been gradually and incrementally working towards active learning. The first major step was the use of clickers in the large intro bio classes. I used them to ask anywhere from 4 to 8 questions every class period, many addressing common misconceptions, and most questions requiring students to apply concepts or solve small analytical or numerical problems. Nearly every class period student answers would show no clear consensus, and I would have students discuss it with their neighbors and then revote. I enjoy the instant feedback, and the student reactions, either of satisfaction at getting it right, or the bemusement at seeing a split opinion.

But after a few iterations, I felt like I had taken clicker questions about as far as I could, and I was ready for more. I was still primarily in content-delivery mode, and I’ve been thinking about how to switch class time to student-inquiry mode, where students actively seek out knowledge and struggle to grasp content in order to solve a real-life problem. However, I was daunted by the prospect of doing this with a lecture hall with 200+ students. Like most university faculty, I had zero experience with active learning in large lecture classes, and had no example to learn from.

Then this year, Science published two papers on science education. The first, published in the 13 May issue, is by Deslauriers et al., Improved Learning in a Large-Enrollment Physics Class, where students taught by novice instructors trained in active learning techniques vastly outperformed students taught by an experienced instructor using traditional lectures. The difference in performance was so astonishingly large that it deserves a closer examination, and will be the subject of another blog post.

The second paper was published in the 3 June issue, by Haak et al. (Scott Freeman is the corresponding author), titled Increased Structure and Active Learning Reduce the Achievement Gap in Introductory Biology. The data in this paper showed that what I have been doing with clicker questions in my class was clearly insufficient. Haak et al. classified this as “moderately” structured class, and this type of instruction had no statistically significant effect on the achievement gap between disadvantaged and advantaged students. However, a highly structured course, with virtually no lecture, resulted in greater than 50% reduction of the achievement gap, and increased learning among all students. Now this is the same subject and level that I teach, and their class sizes are even larger than my classes. I have run out of excuses. I came to feel that I would be derelict if I did not at least attempt to go fully into highly structured, active-learning in my own classes.

Of course, I have to figure out how to do this my own way. Fortunately, I am co-teaching Biol 1510 with the intrepid Chrissy Spencer, who is also thinking along the same lines, and is enthusiastic about trying new methods and new technologies. In fact, she teaches roughly the first half of the course, so I have time to flesh out my ideas for what I want to do. Meanwhile, she gets to lay the groundwork.

I will also be able to try out the basic approach with my small Genomics class. I have some concern because a high percentage are graduate students, and most of the graduate students are from India and China. My colleague, Jennifer Leavey, who taught Immunology last spring as a POGIL class, ran into strong resistance from a few graduate students, who didn’t like the idea of group work during class, and repeatedly asked her to lecture. I will explain the rationale for my approach, and ask for feedback from the students.

We will also experiment with Piazza.com, an on-line social discussion platform with some unique features. I had intended to use a Twitter stream to facilitate peer discussion both during and outside of class time, but Piazza.com has wiki features and doesn’t have the 140-character limit. I posed a question in the first day of class and asked students to respond on Piazza, to get a feel for how it might work. I will discuss Piazza more fully in later blog posts, but my initial impression is that students have really taken to it and are asking and answering lots of questions, just as intended. It’s not so great at capturing simultaneous input from a class of 200 students, and I’ll have to think about how to use it effectively in conjunction with small group exercises.

I’ve laid out my plans in this post, and I will post weekly to document my trials and tribulations, and I hope, some successes.  I promise to post video as well, later in the semester when I take my turn in teaching the large intro course.