BIOLOGY
MATH & STATISTICS
PHYSICS
SJS
Issue 04: Fall 2016
Swarthmore Journal of Science
What’s Bugging You? A Glance at the Gut Microbiome and Human Behavior
CHEMISTRY
ENGINEERING
COMP SCIENCE
Letter from the Editor BOARD
Fall 2016
Dear Readers,
Welcome to the Fall 2016 issue, the 4th issue of the Swarthmore Journal of Science. This journal, like myself, has well entered into its third year. I’m asking the journal, as I’m asking myself, how did the time fly by so fast?
EDITOR-IN-CHIEF
SJS began publishing articles written by students in Fall 2014. The mission at its inception was simple but precisely true to Swarthmore’s intellectual nature: to foster scientific dialogue among Swarthmore students of all social and academic backgrounds. This journal, led by our staff members, has been a space for all students to share scientific pieces that they are passionate about with the campus community. Science communication is ever so important today, when our current events, ethical questions, and scientific understanding are inseparable more than ever before. More generally, scientific breakthroughs are meant to benefit all of society, so it is arguably a civic duty for scientists and non-scientists to be in consistent dialogue with each other. I’m very proud of how SJS has developed over the years and has garnered attention on campus over the last few years.
Christina Labows
My first semester as Editor-in-Chief has been just as breathtaking as it has been hectic. Despite of some of the abrupt challenges I’ve faced, I am so grateful to be able to work with such talented students to create this journal. I would like to thank Helen Wang and Christina Labows, our Managing Editors, for all their expertise and help throughout the past summer and fall. I would also like to thank Luke Barbano, who planned our Science Mixer social event in late September and helped SJS increase its visibility on campus. Last, but certainly not least, I would like to give a special thanks to Taylor Chiang and the graphic design team of Emma Giordano, John Sun, and Letitia Ho. They diligently crafted the layout and graphics of this issue during the heat of the semester.
Ramanan
Aaron Holmes
MANAGING EDITORS Helen Wang
EDITORS Physics: Luke Barbano & Katherine Dunbar Biology: Zelu Sibanda & David Tian Math & Statistics: Meghana Ranganathan Chemistry: Jennifer Guo Computer Science: Jimmy Shah Copy Editors: Lulu Allen-Waller & Vivek
Graphics: Emma Giordano, Letitia Ho, & John Sun Layout: Taylor Chiang
CONTRIBUTERS Biology: Talia Borofsky & May Dong Math & Statistics: Tushar Kundu Chemistry: Aly Rabin
I hope that you enjoy reading the assortment of articles in this issue. They are meant to foster dialogue with you, the reader, and JOIN THE CONVERSATION the rest of the Swarthmore community. So, when you pick up this swatjournalscience@gmail.com issue, share it with a friend and keep the conversation going. Connect with us on: https://issuu.com/swarthmorejournalofscience
Best,
Aaron Holmes
https://www.facebook.com/SwatJournalScience
Editor-in-Chief 2016-2017
http://pinterest.com/sciencejournal
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CONTENTS Always Make Assumptions CS Growth at a Glance Inhibitory Interplay: CDKs in Cancer Treatment What’s Bugging You? A Glance at the Gut Microbiome and Human Behavior Like Neurons, Bees Use Explicity Inhibitory Signals to Flexibly Allocate Foragers Missing the Trump Train
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were about to be shipped offshore to low-wage countries like India and China.” He further detailed how CS growth has challenged universities and it is important to remember that we cannot predict how this rapid growth will end. Princeton University, for example, increased the number of faculty in the CS department by 30%, and even had the President of the institution laud the ‘Power and Promise of Computer Science’. As a direct side effect, this growth has also led to an increased interest in statistics, and just like computer science, there is a strong demand in the field as well. An investigation conducted by McKinsey Global Institute predicts a shortage of up to 190,000 people needed to run projects for big data. From a campus perspective, more accredited institutions are now offering a major in statistics to fill this need. Another outlet to consider is that of diversity, specifically how this growth affects diversity. According to the Tableau survey conducted by the Computing Research Association, the most interesting find was “that women leave computer science not because of their grades (which are typically higher than the men who stay), but because they are isolated, don’t understand their standing relative to peers, and feel the social climate is uncomfortable.” Relating back to Professor Robert and the last spike, the CRA notes that we need to be especially conscious now, so that the computing community can benefit from the diversity that exists now in the industry. It was noted that the field decreased in this capacity soon after the field’s last peak. The growth in CS highlights the complexity of a pattern that seemed simplistic from the surface. When I first started writing this piece, I was hoping to answer the questions and effects of rapid growth for myself. But to be completely honest, I now feel more unsatisfied than when I started. The simple answer is that it is far too early to tell what will happen.
CS Growth at a Glance By Jimmy Shah One of the most recent trends stunning many US colleges and universities has been the rise in popularity of Computer Science. Employment of software developers is projected to grow 17 percent from 2014 to 2024, according to the Bureau of Labor Statistics. This pace is much faster than average due to the need in computer security for emerging apps and the rise in tech within the medical and healthcare industry. From hosting externally funded Hackathons to creating new buildings to support demand, the subject has certainly forced institutions to question how to handle such rapid growth while retaining fundamental mission of the given school. At Swarthmore alone, approximately 21% of the class of 2018 has declared either a major or minor in Computer Science. Furthermore, the difficulty of getting into the introductory computer science courses also makes me wonder if this number is potentially suppressed. The rise in the field from both an undergraduate perspective and diversity perspective allows for interesting avenues of analysis. From an institutional view, computer science has experienced waves of growth and decline throughout the decades, with the latest decay in the mid-2000s. As Professor Eric Roberts of Stanford University notes, “The more recent downturn was clearly caused by the dot-com collapse. After the tech bubble burst in 2001, student interest in computer science waned throughout the United States, a downturn exacerbated by a popular mythology suggesting— entirely contrary to fact—that all jobs in technology
Literature Cited: https://paw.princeton.edu/article/power-and-promise-computer-science https://www.cs.princeton.edu/news/article/princeton-president-discusses-importance-computer-science-faculty-growth http://cra.org/crn/2015/05/booming_enrollments_what_is_the_impact/ http://fortune.com/2015/02/10/college-major-statistics-fastest-growing/ http://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm#tab-6 http://cs.stanford.edu/people/eroberts/CSCapacity.pdf 5
Inhibitory Interplay: CDKs in Cancer Treatment By Aly Rabin Cancer is a dialogue of dualities. It operates on an enormous scale, affecting huge swaths of the population and inciting extensive fundraising and awareness efforts in response. Cancer’s underlying origins, however, operate on the smallest of scales. The cell’s cyclical fluctuations must be carried out flawlessly to ensure its function; any deviation from its elegant operations, if allowed to proceed unchecked, can result in cancer. Functioning normally, the cell cycle is responsible for the controlled division of cells. Once mutated, a cancer cell’s cycle runs uncontrollably, allowing the cell to proliferate unceasingly. The proper workings of the cell cycle are crucial to a cell’s normal growth and replication. As a result, the cycle is heavily regulated by an extensive array of proteins. One particular family of molecules, which has been the recent subject of a great deal of research, is at the forefront of cell cycle regulation: cyclin-dependent kinases (also known as CDKs). Herein lies another of cancer’s crucial dualities, the partnership of this sophisticated protein family with its substrate, the protein cyclin. This cooperation modulates a large component of the cell cycle’s molecular interactions. CDKs have a number of roles in not only cell cycle regulation, but also in DNA repair and postmitotic processes. In cell division alone, five different types of CDKs and 10 different cyclin subtypes contribute directly to regulation (1). CDKs are holoenzymes, proteins that only become activated upon interaction with an appropriate cyclin. Cyclin specificity allows for temporal activation of CDKs, in which growth factors promote the release of specific cyclins at various 6
Figure 1. The molecular structure of THZ1. (3)
stages of the cell cycle. Because of the physical differences among cyclins and the temporal separation of their release, they promote substrate binding specificity among individual CDKs, thereby conferring upon the cell cycle its hallmark periodicity (1). Cyclin binding is so specific and essential to the CDK’s function that, for example, the binding of cyclin A to Cdk2 increases the kinase’s activity by over five orders of magnitude (1). CDKs act to regulate the cell cycle by phosphorylating key substrates, thereby activating or deactivating those aspects of the cycle’s machinery (2). Lung cancer is the deadliest form of cancer in the United States, and encompasses several subtypes. Among those, small cell lung cancer—often caused by smoking, and comprising 10 to 15 percent of lung cancer cases— is the most aggressive, with a five-year survival rate of about six percent (3). Small cell lung cancer expresses a neuroendocrine lineage not seen in normal cells, allowing it to be singled out and sensitive to transcription-targeting drugs (3). Among the molecules upregulated in small cell lung cancer, Cdk7 is responsible for DNA transcription regulation as part of the cell cycle, phosphorylating the C-terminal domain of RNA polymerase II and Cdk9, and activating Cdk1, 2, 4, and 6 in the cell cycle (4). The upregulation of these cell cycle proteins in small cell lung cancer provides a therapeutic window for treatment targeted to molecules expressed at lower levels in normal cells, hopefully eliminating the toxicity concerns that apply to standard chemotherapy. Targeted therapies are among the newer approaches to cancer treatment, selectively inhibiting molecules that are overexpressed in or specific to the malignant cells. An effective CDK7 inhibitor, THZ1, was recently synthesized by chemists at Dana Farber Cancer Institute in Boston (3). THZ1 acts as a covalent inhibitor of Cdk7, drastically reducing RNA polymerase II-mediated gene transcription and downregulating key oncogenic, or cancer-promoting, factors (3). The drug is a phenylaminopyrimidine with
a cysteine-reactive acrylamide moiety (4). In order to inhibit the CDK7 protein, THZ1 combines ATP-site and allosteric covalent binding, allowing it to act both selectively and effectively (4). In vivo trials are currently being carried out, and researchers and doctors will be able to observe whether THZ1 is effective in small cell patient care within the next few years. Cdk7 and THZ1 comprise a tiny fraction of all the research on CDKs and their inhibitors taking place worldwide. The goal of this research highlights a third duality in cancer: the interplay between these essential proteins and their inhibitors, a relationship that will hopefully result in reduced tumor cell viability and better outcomes for cancer patients. The study of CDKs extends beyond small cell lung cancer to all forms of cancer; due to the chemical and functional diversity of the various kinases, and their variable expression in different forms of cancer, targeted CDK inhibitors may one day be essential to cancer patient survival. Literature Cited: 1. Harper, J. W., and P. D. Adams. “Cyclin-Dependent Kinases.” Chemical Reviews Chem. Rev. 101.8 (2001): 2511-526. 2. Esposito, Vincenzo, Alfonso Baldi, Antonio De Luca, Angela M. Groger, Massimo Loda, Giovan Giacomo Giordano, Mario Caputi, Feliciano Baldi, Michele Pagano, and Antonio Giordano. “Prognostic Role of the Cyclin-dependent Kinase Inhibitor P27 in Non-Small Cell Lung Cancer.” Cancer Research 57 (1997): 3381-385. 3. Christensen, Camilla L., Nicholas Kwiatkowski, Brian J. Abraham, Julian Carretero, Fatima Al-Shahrour, Tinghu Zhang, Edmond Chipumuro, Grit S. Herter-Sprie, Esra A. Akbay, Abigail Altabef, Jianming Zhang, Takeshi Shimamura, Marzia Capelletti, Jakob B. Reibel, Jillian D. Cavanaugh, Peng Gao, Yan Liu, Signe R. Michaelsen, Hans S. Poulsen, Amir R. Aref, David A. Barbie, James E. Bradner, Rani E. George, Nathanael S. Gray, Richard A. Young, and Kwok-Kin Wong. “Targeting Transcriptional Addictions in Small Cell Lung Cancer with a Covalent CDK7 Inhibitor.” Cancer Cell 27.1 (2015): 149. 4. Kwiatkowski, Nicholas, Tinghu Zhang, Peter B. Rahl, Brian J. Abraham, Jessica Reddy, Scott B. Ficarro, Anahita Dastur, Arnaud Amzallag, Sridhar Ramaswamy, Bethany Tesar, Catherine E. Jenkins, Nancy M. Hannett, Douglas Mcmillin, Takaomi Sanda, Taebo Sim, Nam Doo Kim, Thomas Look, Constantine S. Mitsiades, Andrew P. Weng, Jennifer R. Brown, Cyril H. Benes, Jarrod A. Marto, Richard A. Young, and Nathanael S. Gray. “Targeting Transcription Regulation in Cancer with a Covalent CDK7 Inhibitor.” Nature 511.7511 (2014): 616-20. 7
What’s Bugging You? A Glance at the Gut Microbiome and Human Behavior By May Dong and Vivek Ramanan
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fragilis to treat mice with behavior typical to autism. The result was an amelioration of anxious and repetitive behaviors as well as improved communication, although it did not impact social preference. The presence of Bacteroides species restored expression of cytokines, proteins involved in regulating inflammation, which in turn led to decreased gut leakiness. It’s clear that bacteria rule the gut, but how do their activities, which take place so far away from the brain, cause such a large impact on behavior? There is no one answer to this question; humans interact with their microbial hitchhikers in a multitude of ways. However, one way in which they impact is what they leave behind as waste. Lactic acid bacteria such as L. rhamnosus produce γ-aminobutyric acid, or GABA, as a byproduct of catabolism (Barrett et al. 2012). GABA acts as a neurotransmitter promoting sleep and regulating anxiety, memory, and vigilance through various receptors (Gottesmann 2002; Rudolph and Moher 2003). Other important byproducts include short-chain fatty acids such as acetate, propionate and butyrate (den Besten et al. 2013). Short-chain fatty acids are responsible for a vast array of functions. Acetate has been found to decrease inflammation in the colon and promote cholesterol synthesis (Arpaia 2014; Wong et al. 2006). Working as a check-and-balance system, propionate inhibits synthesis of cholesterol. Finally, butyrate has a host of functions, from preventing cancer by regulating growth and division in the colon to diminishing gut leakiness through regulation of tight junctions (Wong et al. 2006). Additionally, butyrate is an important factor of epigenetic change through histone modification, a preferred resource for colonic epithelial cells, and plays a key role in the gut-brain axis (Tran et al. 1998; Barcenilla et al. 2000; Bourassa et al. 2009). If microbes and their human hosts jointly regulate health and behavior, it is possible that antibiotics, which are meant to kill certain populations of bacteria, might severely disrupt this delicate balance. Indeed, antibiotics have been shown to amplify imbalances in the microbial population of the human gut, leading to higher likelihoods of inflammatory bowel diseases such as Crohn’s disease (Gevers et al. 2014). In the UK, hospital admittances for Crohn’s disease in young adults increased by 293% from 2003 to 2013 (Harvey and Wyatt 2014). Meanwhile, antibiotic use increased 110% in the UK over that time period, from 37.6 million prescriptions per year in 2003 to 41.6 million in 2013 (UK Prescribing 2004, 2014). While
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In a laboratory in Cork, Ireland, a mouse swims in a bowl of water. The clock ticks. And ticks. And ticks. The mouse doesn’t know it, but its unusual behavior is paving the way for a brand new field of science. Unlike most mice, this one hasn’t stopped trying to escape from the bowl. It’s just an ordinary mouse, but it’s been given an unusual diet− standard mouse food infused with a Lactobacillus rhamnosus probiotic broth. This 2011 study went on to show that you really are what you eat. The mice that were given doses of L. rhamnosus probiotics showed lower levels of anxious and depressive behavior, spending more time exploring and less time hiding compared to their standard broth-fed counterparts. What does this mean for humans, though? It turns out, quite a lot. Current studies suggest that the implications range from physical to cognitive, and that our microbiota influence health and illness in more ways than imagined. Disorders that seem completely unrelated, such as cerebral palsy, cardiovascular disease, and depression are often comorbid with gastrointestinal disruption, which can be a sign of microbial community shifts. Autism spectrum disorder (ASD) is another disorder that is accompanied by altered microbial communities (Hsiao et al. 2013). The first hint that autism could be linked with gastrointestinal issues was the correlation (r = 0.59, p < 0.001) found by Adams et al. in 2011 between severity of autism in children and severity of gastrointestinal distress. A logical question would be whether treating gastrointestinal symptoms leads to decreased ASD traits. So far, the answer, in a mouse model, at least, seems to be yes. Hsiao et al. used a probiotic made from normal commensal bacterium Bacteroides 9
are not a panacea and we must not treat them as such. Crohn’s disease, for instance, while worsened by microbial imbalances in the gut, also has genetic factors that determine one’s risk factor (Gevers et al. 2014). In the study on neurodevelopmental disorders and the microbiome, altering the microbial community was not a perfect remedy; the mice treated with probiotics did not show increased sociability or preference for social interaction. In addition, it can be very difficult to get accurate data on the microbial communities within humans. The abundances of microbial species from intestine, skin, and fecal matter samples individually do not reflect the whole human microbiome and therefore usually require mathematical transformation to account for this problem. Additionally, interactions between microbes -- mutualism, competition, etc.-- influence abundances of other species, which can cause biological data analysis to incorrectly assign significance to microbial populations. Finally, the microbiome is also not a fixed entity. This makes it difficult to definitively characterize: the moment you pin down the populations, they have already changed. While this rapid change can pose a problem for researchers, it also represents the beauty of the microbiome and the reason it has become such a popular research topic. The microbiome may not be the definitive answer to all of our problems, but it is clear that it has been a missing part of the puzzle for too long. The idea that small changes in the microbiome
Figure 1. Adapted from Bravo et al. 2011. Mice were fed probiotic Lactobacillus rhamnosus or broth, then given a vagotomy (severing of the vagus nerve) or a sham procedure. Neither vagotomy nor probiotic had any significant effect on overall motor activity. When the vagus nerve remained intact (sham procedure), L. rhamnosus increased resiliency in the forced swim test and willingness to enter and spend time in the open field. When the vagus nerve was severed, none of these effects were evident. Therefore it seems likely that the effects of gut microbes are mediated via the vagus nerve.
it’s not clear that antibiotics are the only culprit at hand, it is clear that antibiotics should be prescribed with care and with consideration for the changes they cause in the human microbiome. In order to make headway in understanding these complicated human-microbial interactions, science relies on the continued development of advanced computational tools. To model the complex interactions between bacterial populations, their bacterial byproducts, and human hosts requires an incredible amount of data collection and big data analysis. Based on these analyses, it is possible that future treatments of diseases of microbial origins will be developed based on an individual’s microbiome and result in transplants of specific bacterial populations. Fecal transplants, a less specific bacterial transplant currently in use, have already been shown to be successful in treating patients with inflammatory bowel disease (Vrieze et al. 2013). Computational concepts such as network science, probabilistic modeling and big data analysis are essential to mapping the complex microbial pathways within the human body. Therefore, bacterial transplants could one day become an effective psychological treatment for brain related disorders such as autism, considering their integral role in human behavior itself. While it is increasingly accepted that bacteria and other microbes are integral to health, they
Figure 2. A simplified depiction of bacterial population interactions within the human gastrointestinal tract. The leftmost species interacts with the center species, which interacts with the rightmost species. If the species abundances of the first changes, the abundances of the other species will match that based on the attributes of the interaction. For example, if the species exist in a mutualistic relationship, their abundances will rise in unison. 10
and Cell Bio. 92: 561–562 Barcenilla, A., Pryde, S.E., Martin, J.C., et al. 2000. Phylogenetic relationships of Butyrate-producing bacteria from the human gut. Applied Environmental Microbiology 66(4): 1654 - 1661. Barrett, E., Ross, R.P., O’Toole, P.W., Fitzgerald, G.F. and Stanton, C. (2012), γ-Aminobutyric acid production by culturable bacteria from the human intestine. J Appl Microbiol, 113: 411–417. den Besten, G., van Eunen, K., Groen, A.K., Venema, K., Reijngoud, D.J., and Bakker, B.M.. 2013. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res, 54(9): 2325–2340.
Figure 3. Patient outcomes of fecal microbiota transplantation in patients with Clostridium difficile infections as reviewed by Gough et al., 2011 (n = 317). 89% of patients experienced resolution, or complete amelioration of disease symptoms, and 3.9% exhibited a relapse of bacterial populations. 1.3% died due to the treated illness and 4.1% of patients died due to unrelated causes. Standard treatments for C. difficile include antibiotics such as vancomycin, which has a relapse rate of up to 35%.
Bourassa, M.W., Alim, I., Bultman, S.J., et al. 2016. Butyrate, neuroepigenetics, and the gut microbiome: Can a high fiber diet improve brain health? Neuroscience letters, 625(20): 56-63. Gevers, D., Kugathasan, S., Denson, Lee A., et al. 2014. The Treatment-Naive Microbiome in New-Onset Crohn’s Disease. Cell Host and Microbe 15(3): 382 - 392. Gottesmann, C. 2002. GABA mechanisms and sleep. Neuroscience, 111(2):231-9.
have a large effect on our physical and psychological states is the focus of modern day research, but the phenomenon itself is hardly new. Understanding the influence of our vital relationships with our other halves on physical, psychological, and behavioral matters is of great importance as we continue forward in our search for medical answers.
Gough, E., Shaikh, H., and Manges, A.R. 2011. Systematic Review of Intestinal Microbiota Transplantation (Fecal Bacteriotherapy) for Recurrent Clostridium difficile Infection. Clin Infect Disease 53(10): 994 - 1002. Harvey, D., and Wyatt, N. 2014. Huge increase in Crohn’s disease hospital admissions. BBC. Hsiao, E.Y., McBride, S.W., Hsien, S., Sharon, G., Hyde, E.R., McCue, T., Codelli, J.A., Chow, J., Reisman, S.E., Petrosino, J.F. et al. 2013. Microbiota Modulate Behavioral and Physiological Abnormalities Associated with Neurodevelopmental Disorders. Cell, 155(7): 1451–1463. Public Health England. 2015. UK One Health Report: Joint report on human and animal antibiotic use, sales and resistance, 2013. Rudolph, U., and Moher, H. 2003. Analysis of GABAA receptor function and dissection of the pharmacology of benzodiazepines and general anesthetics through mouse genetics. Annual Review Pharm. Tox., 44: 475–498. Tran, C.P., Familari, M., Parker, L.M., et al. 1998. Short-chain fatty acids inhibit intestinal trefoil factor gene expression in colon cancer cells. American Journal of Physiology 275(1): 85 - 94. Tewfik, T.L. 2015. Anatomy of the Vagus Nerve. Medscape. UK Prescribing and Primary Care team, Health and Social Care Information Centre, 2004. Prescription Cost Analysis: England 2003. National Health Services Archive.
Figure 4. Adapted from Xiao et al. 2013. Maternal Immune Activation (MIA) is a model used to study autism traits in mice. Behavioral traits associated with MIA mice include stereotyped behavior, decreased communication, decreased sociability, and decreased social preference. MIA mice treated with Bacteroides fragilis (P+BF) had levels of stereotyped behavior lower than MIA mice without probiotic treatment (P) and comparable to non-MIA mice (S).
UK Prescribing and Primary Care team, Health and Social Care Information Centre, 2014. Prescription Cost Analysis: England 2013. National Health Services Archive.
Literature Cited: Adams, J.B., Johansen, L.J., Powell, L.D., Quig, D., Rubin, R.A. 2011. Gastrointestinal flora and gastrointestinal status in children with autism--comparisons to typical children and correlation with autism severity. BMC Gastroenterol, 11:22.
Vrieze, A., de Groot, P.F., Kootte, R.S., et al. 2013. Fecal Transplant: a safe and sustainable clinical therapy for restoring intestinal microbial balance in human disease? Best Practice and Research Clinical Gastroenterology 27(1): 127 - 137.
Arpaia, N. 2014. Keeping peace with the microbiome: acetate dampens inflammatory cytokine production in intestinal epithelial cells. Immun.
Wong, J.M., de Souza, R., Kendall, C.W., Emam, A., and Jenkins, D.J. 2006. Colonic health: fermentation and short chain fatty acids. J Clin Gastroenterol. 40(3):235-43. 11
Like Neurons, Bees Use Explicit Inhibitory Signals to Flexibly Allocate Foragers By Talia Borofsky
Many of the students and professors who were around this summer might remember that I was constantly in the quad in front of the science center, peering at honey bees feeding on sugar water. I was asked many questions about my project, but often did not have time to properly explain. With the guidance of Professor Christopher Mayack of the biology department, Professor Victor Barranca of the math department, and with the support of Victor Le and other members of my lab, I studied negative feedback in honey bee colonies. My research focused on how negative feedback regulates foraging activity, and how these processes relate to neural networks. Biological systems, from the brain to ecosystems, display self-organization, in which group properties arise from local interactions among individual units (Fewell 2003). Social insects, such as ants and honeybees, as well as neurons in the brain, display â&#x20AC;&#x153;collective decision-making,â&#x20AC;? whereby groups of individuals collectively determine which stimuli to respond to and how to respond to them (Couzin 2009). Three main components of the collective decision-making process are positive feedback, threshold integration, and negative feedback. Positive feedback is communication that amplifies stimuli. For instance, some ants create a trail to a food-source by depositing chemicals called pheromones. Trail deposition of ants (Wilson 1962), the waggle dance in bees (Frisch 1967), and the exchange of excitatory neurotransmitters between neurons (Dayan and Abbot 2001) are examples of positive feedback (Figure 1). In threshold integration, the signals and interaction experienced by an individual must exceed a certain threshold in order to elicit a response (Couzin 2009). For instance, foraging ants and neurons must receive enough total input, and must receive that input fast enough, in order to leave the nest or fire (respectively) (Greene & Gordon 2007; Dayan and Abbot 2001), and for a swarm of bees to choose a nest site as its new home, a threshold number of bees must waggle dance for that site (Seeley 1982). Negative feedback, on the other hand, is when communication inhibits individuals from responding to stimuli. Neurons can apply explicit negative feedback to each other by passing neurotransmitters that inhibit a connecting neuron from firing (Dayan and Abbot 2001). However, the social insect literature has few examples of explicit negative feedback (Detrain and Denouberg 2008). 12
For my observations, I would sit in the field and count the number of bees during 2 minute intervals. I would also note which bees visited the feeder during the intervals by using paint markings to individually identify the bees.
The feeder consisted of an inverted jar, filled with sugar solution, on top of a grooved plexiglass plate. I used acrylic paint markers to individually mark each bee. In a hightech science world, the experimental side of my project was surprisingly low-tech! In the picture, you see one unmarked bee, and the other I would call â&#x20AC;&#x153;light green abdomen.â&#x20AC;?
Recent research has shed light onto the function of an explicit inhibitory signal in honey bees called the stop signal (Nieh 1993; Nieh 2010; Seeley 2012). During a stop signal, the sender briefly vibrates at 380 Hz and butts its head into another bee performing the waggle dance (Nieh 1993) The stop signal causes waggle-dancers to stop waggling, but only after they have received a threshold number of stop signals (Seeley 2012). Nieh (2010) showed that bees deliver the stop signal in response to predation, simulated by an experimenter pinching the bee, and competition. The negative feedback from stop signals down-regulat ed recruitment to the feeders. In fact, bees who were pinched
Figure 1. During a waggle dance, a bee walks in a figure eight and vibrates during the straight part of the eight. The number of circuits indicate the quality of a food source, the frequency of vibrations indicate the distance of the food source from the hive, and the angle of the waggling relative to gravity indicates the angle of the food source relative to gravity from the hive (Winstan 1991)
delivered stop signals to bees waggle dancing for the same feeder, which indicates that the stop signal conveys location information. I call this type of recurrent negative feedback ipsi negative signaling, or feedback which occurs within a group (in this case, the group of bees visiting the focal feeder). Seeley et al. (2012) more directly connected the stop signal to collective decision-making processes in honey bees. When swarms of bees choose a new nest site, there is a danger of a deadlock, where there are equal numbers of bees recruiting for each possible site. This is a serious problem, since the colony can only choose one site for a home. Seeley found that the stop signal prevents this situation from occurring, since bees recruiting for one site delivered stop signals to bees re13
cruiting for the other site, and vice versa. This form of negative feedback, where feedback is exchanged between groups (in this case, between groups of bees advocating for different potential nest sites), can be called contra signaling. No research has related the stop signal to forager allocation based on the nutrition level of food sites. However, the resource value of food sites fluctuate, and honey bee colonies need to switch forager allocation away from a site which is no longer profitable in order to maximize foraging efficiency and productivity of the hive (Seeley 1991). Previous studies suggested that bees can both be recruited for and abandon feeders based on changes in nutrition levels (Seeley 1991). Negative feedback is important to similarly fast and flexible decision-making in ant colonies and neural networks (Grúter 2012), so bees could be using the stop signal to reallocate foragers when a food site loses nutrition. However, the colony might require a threshold number of stop signals in order to dampen forager allocation so decision-making is not too sensitive to occasional stop signals. I examined whether and how stop signals can dampen forager allocation of Apis mellifera to non-nutritious feeders. In separate trials, we either provided a nutritious feeder and maintained the nutrition at a constant value, or we drastically reduced its nutrition. We hypothesized that after this change, there would be an increase in the number of stop signals received by bees waggle-dancing for that feeder correlated with a decrease in the number of bees visiting that feeder. Furthermore, we studied the directionality of stop signals and the time scale of how stop signals affect collective forager allocation. Since bees cannot make direct comparisons of food sites, either while foraging or while being recruited (Seeley 1991), we predicted that bees will use ipsi-signaling. Furthermore, to prevent forager allocation from being too sensitive to occasional stop signals, we predicted that feeder visits would decrease after the total number of stop signals surpass a threshold. The project was very challenging. It was my first time studying animal behavior and my first time leading a research project, so there was a lot of trial-and-error. We had two successful control trials and experimental trials. The data was very unexpected. Professor Chris Mayack, who was videotaping the bees, observed ipsi stop signaling correlated
with a cessation of waggle dancing. Yet, outside I saw that visitations to the feeder increased after the concentration of sugar plummeted. However, this seemed to result from an increase in visitations by bees already loyal to the site -- rather than by more recruits. At the time, there was a dearth of nectar available on campus. The bees might have been desperate for food, so when the sucrose went down, they collectively decided to use up the solution while it was still there. I plan to spend the next semester analyzing my numerical and video data, and also creating models based on theoretical neuroscience to explain some of the phenomena I observed. Assuming the projected results are true, then this study would show that the stop signal aids in allocating foragers to profitable sources. My research would also add to a new field of research drawing connections between social insect colony function and neural networks (Seeley 2012, Greene and Gordon 2007). Most previous examples of negative feedback used by social insects have not been explicit- for instance, ants use the decay of pheromones and crowding as negative feedback (Gruter 2012). But the stop signal is an explicitly inhibitory signal, and thus more similar to the type of negative feedback seen in the brain. Since scientists can more easily observe insect communication than neural interactions, social insect research can complement theoretical neuroscience by revealing how cognitive properties emerge from networks of interacting individuals.
Literature Cited: Couzin, I. D. Collective cognition in animal groups.Trends Cogn. Sci. 13, 36–43 (2009). Seeley, T. D. Social foraging by honeybees: how colonies allocate foragers among patches of flowers. Behav. Ecol. Sociobiol. 19, 343–354 (1986). Dayan, Perter, and L. F. Abbott. Theoretical Neuroscience:Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: Massachussetts Institute of Technology, 2001 Fewell, Jennifer H. “Social insect networks.” Science 301.5641 (2003): 1867-1870. Greene, Michael J., and Deborah M. Gordon. “Interaction rate informs harvester ant task decisions.” Behavioral Ecology 18.2 (2007): 451-455. Grüter, C. et al. Negative Feedback Enables Fast and Flexible Collective Decision-Making in Ants. PLoS One 7, 21–25 (2012). Kietzman, Parry M., and P. Kirk Visscher. “The anti-waggle dance: use of the stop signal as negative feedback.” Frontiers in Ecology and Evolution 3 (2015): 14. Nieh, J. C. The Stop Signal of Honey Bees : Reconsidering Its Message. Behav. Ecol. Sociobiol. 33, 51–56 (1993). Nieh, J. C. A negative feedback signal that is triggered by peril curbs honey bee recruitment. Curr. Biol. 20, 310–5 (2010). Robinson, E. J. H., Jackson, D. E., Holcombe, M. & Ratnieks, F. L. W. Insect communication: ‘no entry’ signal in ant foraging. Nature 438, 442 (2005). Seeley, T. D. et al. Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science 335, 108–11 (2012). Seeley, T. D., Camazine, S. & Sneyd, J. Collective decision-making in honey bees: how colonies choose among nectar sources. Behav. Ecol. Sociobiol. 28, 277–290 (1991).
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Missing the Trump Train By Tushar Kundu Practically unthinkable one short year ago, on Tuesday, July 19th Donald J. Trump became the 2016 GOP presidential nominee. There is now one question dominating every pundit’s thinking: “Should we have seen this coming?” One thing any impartial observer can agree upon is that the Republican Party’s nomination process has been far from normal, highlighted by deep party fractures, an increasingly polarized base, and quite a bit of yelling. While few can claim to have anticipated Trump’s meteoric rise years in advance, it is surprising how few anticipated it even months after the declaration of his candidacy. How could political pundits and data journalists who predict these events for a living ascribe him such a low probability of success for so long? Why were they unwilling to adjust and update their predictions even as he topped the polls? To understand their apparent stubbornness, we must consider how most political prediction models are formed. One of the largest misconceptions around
election models is that they are all constructed in the same manner. Intuitively, one would think prediction models input specific information such as polling data or economic indicators, manipulate the inputs according to a predetermined algorithm, and spit out a series of statistical predictions. And this is exactly correct for general election models. However, political primaries are an entirely different beast. In general election models, any trends and patterns that statisticians incorporate are already on tenuous ground as they are drawn from a history of only to 30 to 40 elections. The issue of data scarcity is taken to the extreme for primary elections - we often forget that the current open primary process dates only to 1972. Furthermore, the primary system is particularly complex. In addition to the convoluted criteria for nomination that differs by party, the sequential nature of the states’ primaries ensures that each primary is no longer an independent event. In other words, when a candidate wins a particular state, 15
this gives her an advantage in the following contests; a feature that is difficult to model before the primaries take place. Contrast this with the general election in which all states vote on the same day. Faced with the daunting task of modeling a chaotic and complex system without historical precedent, forecasters were forced to rely upon personal beliefs. Of course, the downside to this method is possible intrusion of human bias. In this case, forecasters may have provided overconfident predictions that Trump would lose due to their firmly held belief that a hyperbolic reality TV star could never become the nominee of a major political party. Renowned statistician (and infamous Trump doubter) Nate Silver reflects on his own overconfidence through the lens of Bayesian statistics. At its core, Bayesian statistics is built on the idea that probabilities are random variables, not fixed unknown values. Bayesians begin with some belief about a parameter, and update their belief as data is collected. Beliefs about a parameter are formalized as probability distributions (see Figures 1 & 2) that convey confidence in various parameter values. In this context, Trump’s chance of becoming the GOP nominee is modeled as a random variable (let’s call it p) that can take any value from 0 (Trump’s nomination is impossible) to 1 (Trump’s nomination is mathemat-
ically guaranteed). We can now establish a prior probability distribution. This represents our beliefs about p before collecting data. Let’s conservatively assume that without historical context, we have no knowledge about candidates’ chances of success. Then standard practice is to use a uniform prior. Interpret this as Trump’s true chances of winning being equally likely to be any value between 0 and 1 (0% to 100%). Now we incorporate our data. Looking back at history, Silver identifies eight candidates similar to Trump, all of whom failed. We can incorporate each failed campaign as an observation. Figure 2 demonstrates how the probability distribution shifts as observations are incorporated. We would then estimate Trump’s true probability of success as the mean of the final (posterior) distribution. After incorporating all eight failed campaigns, the mean of the distribution is 0.1, or 10%. This method is not perfect by any means, but provides a necessary reality check implying Trump’s nomination was unlikely, but not the unimaginable shocker that it was certainly portrayed as. While the total influence of overconfidence on predictions is arguable, forecasters clearly over-relied on political science theory, specifically the argument
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outlined in the 2008 book The Party Decides. It argues that those most active in the party will converge on a single candidate that adheres to the party’s mainstream views, and will then assert enough influence on voters to ensure that the candidate becomes the party’s nominee. This theory was a large talking point throughout the primary season, and provided the foundational argument for those repeatedly insisting that Jeb Bush, Marco Rubio, and eventually Ted Cruz would break out of their respective ruts and overtake Mr. Trump. Because history and common sense line up with this train of thought, it provided a convincing and frankly comforting reason to dismiss polls and claim that Trump was little more than a colorful distraction. Despite the contents of this article, do not take away the idea that data journalists and pundits are not to be trusted. Rather, I hope to imprint on you that behind all mathematical models and forecasts are assumptions and theories constructed by human statisticians. In order to derive the most value from data analysis, we should avoid taking probabilities at face value, and instead strive to understand possible biases and uncertainties that accompany the headline figures. Looking forward, the 2016 presidential election is bound to be one of the most consequential in
history, and general election models can provide a good picture of what lies ahead. As I write this today, The Upshot’s model gives Hillary Clinton an 80% chance of winning, PredictWise (using betting markets) gives her 75%, and Nate Silver’s FiveThirtyEight gives Hillary a 68% chance at victory. This is quite a large range! While these models are largely devoid of the specific problems I have outlined in this article, the variance in statistical predictions is demonstrative of the different methodologies and assumptions underlying each team’s model. I hope that you will look into what these differences are, and in the process become a highly analytical and better-informed forecaster. Literature Cited: Bernstein, Jonathan. “My Mea Culpa on Trump Is Different.” Bloomberg. com. Bloomberg, 13 May 2016. Web. 11 Sept. 2016. Byler, David. “How Data World Missed the Boat on Trump.” RealClearPolitics. N.p., 9 May 2016. Web. 11 Sept. 2016. Cohn, Nate. “What I Got Wrong About Donald Trump.” The New York Times. The New York Times, 04 May 2016. Web. 11 Sept. 2016. Matthews, Dylan. “One of the Best Election Models Predicts a Trump Victory. Its Creator Doesn’t Believe It.” Vox. N.p., 14 June 2016. Web. 11 Sept. 2016. Silver, Nate. “How I Acted Like A Pundit And Screwed Up On Donald Trump.” FiveThirtyEight. N.p., 19 May 2016. Web. 11 Sept. 2016. 17
Notes:
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Dear SJS: Now that Iâ&#x20AC;&#x2122;ve read and critically analyzed SJS cover-to-cover, what do I do now? Whether you skimmed
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curled up in a Science Center chair, we hope that we have left you w ith a deeper appreciation for the fascinating real m of science. But above all, we hope that this journal inspires you, ou r readers, to continue to learn and engage w ith science and the wor ld around us!
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