PLoS Computational Biology Research Articles

Teaching Bioinformatics at the Secondary School Level

Bioinformatics is now an integral part of biology and biological research. The field began with a few people from other disciplines teaching themselves and each other the techniques that are now considered commonplace. These pioneers then began graduate programs [1]–[3] to educate the next generation. Those early graduate students typically came as bench biologists or as computer scientists, both groups requiring significant time to “hybridize”. Not surprisingly, this then led to undergraduate majors in bioinformatics to better prepare students for graduate school and research careers in bioinformatics. In addition, teaching bioinformatics in undergraduate biology classes is also a priority [4], [5]. Through the Education section of PLoS Computational Biology we have tried to support this evolution through a collection of educational articles pertinent to the undergraduate level and beyond. It is only natural that we would take the next step [6]. We now introduce a subsection of the Education section with articles devoted to teaching bioinformatics in secondary schools that is derived from the work of the Education committee of the International Society for Computational Biology (ISCB), who identified a need to address the issue of incorporating bioinformatics into secondary school biology classes. They also recognized the interest among researchers to build and participate in outreach programs at the secondary school level given that many funding agencies worldwide encourage such a component in grant applications. To move the ball forward on secondary school bioinformatics education, at ISCB's 2010 international conference, Intelligent Systems in Molecular Biology (ISMB), the ISCB Education committee organized a half-day tutorial aimed at secondary school biology and chemistry teachers in the Boston area interested in learning about bioinformatics and how to include it in their curricula. The tutorial also attracted researchers involved in organizing or formulating outreach programs in their community. The main focus of the ISMB tutorial was the presentation of lesson plans by a secondary school teacher (David Form, a biology teacher at Nashoba Regional High School, Bolton, Massachusetts) who has successfully incorporated bioinformatics into his courses for more than five years. His is one example of such an effort and is embraced in the Ten Simple Rules and its supplementary material found in this issue. Also in this issue we have an article by Suzanne Gallagher and colleagues on the experience of teaching secondary school level bioinformatics in Boulder, Colorado. There are many examples of outreach efforts to high school students that we would like to feature in coming months, which incorporate bioinformatics into their programs (see Table 1). Table 1 Examples of Online Resources and Outreach Programs. There are many other examples of educators doing similar work in school districts worldwide. A recent issue of Briefings in Bioinformatics was dedicated to bioinformatics education [7] with a specific example of programs for secondary school students [8], [9].The ISCB Education committee is building a resource of information useful to secondary school teachers who would like to incorporate bioinformatics into their curriculum. In addition, the committee has begun to explore how to include bioinformatics in Advanced Placement courses and exams in the United States, which we also hope to feature in the Education section of the journal. We encourage feedback of any form, including comments on this editorial, and hearing about your experience teaching bioinformatics to secondary school students.

Ten Simple Rules for Teaching Bioinformatics at the High School Level

Fran Lewitter is Education Editor of PLoS Computational Biology. Given the availability of free, online genomic databases and tools for the analysis of biological data, it is now feasible to teach bioinformatics in the high school classroom [1]. There are a number of reasons why it is appropriate and desirable to introduce bioinformatics at the high school level. Students can engage in inquiry-based activities that involve approaching real-world problems using 21st century skills, while being tailored to high school biology frameworks. Many tools, such as 3-D protein visualization software, allow for differentiated and highly interactive instruction. The foremost reason may be that students can develop a research toolkit that they will be able to use subsequently during college and beyond. As a high school science teacher for the past 23 years, I (DF) have had the opportunity to incorporate bioinformatics into my courses to enrich the teaching of concepts of molecular biology, human biology, genetics, and evolution, providing increased opportunities for effective differentiated instruction and individual student research. This past experience has inspired the creation of this set of Ten Simple Rules. It is important to distinguish between curricula designed to teach the fundamentals of bioinformatics and those that utilize bioinformatics as a teaching tool. Examples of both types of successful teaching can be found in Text S1, Text S2, and Text S3.

PLoS Computational Biology Issue Image | Vol. 7(5) May 2011

In the top right image the simulated methylation patterns in a nonhierarchical (classical) malignancy, indicated with different colors, radiate outward from the center. In the bottom left image, in contrast, the patterns occurring in a hierarchical (CSC-driven) tumor spread as patch-like colonies, increasing the overall intratumor heterogeneity and greatly affecting the evolutionary properties of the cancer cell population. (See Sottoriva et al., doi: 10.1371/journal.pcbi.1001132) Image Credit: Andrea Sottoriva, Cambridge University, United Kingdom

PLoS Computational Biology Issue Image | Vol. 7(4) April 2011

The rat vibrissal system provides an ideal model to examine how the physical embodiment of a sensory array shapes the sensing process. We quantified the morphology of the rat vibrissal array to develop a model that can be used to simulate the spatiotemporal patterns of vibrissa—object contact during tactile exploratory behavior (see Towal et al. doi:10.1371/journal.pcbi.1001120). The left half of the image illustrates the vibrissal array of a real rat, and the right half illustrates the model. Image Credit: Brian Quist, Northwestern University

PLoS Computational Biology Issue Image | Vol. 7(3) March 2011

PLoS Computational Biology Issue Image | Vol. 7(3) March 2011

PLoS Computational Biology Conference Postcard from PSB 2011

PLoS Computational Biology Conference Postcard from PSB 2011

PLoS Computational Biology Issue Image | Vol. 7(2) February 2011

Small-scale genetic interactions have been used for decades to unravel biological pathways. In recent years, experimental techniques have been developed to map them at the genome level, providing large-scale genetic interaction networks. In particular, in budding yeast the genetic network mapped by Synthetic Genetic Array was revealed as both a powerful tool to identify functional modules in the cell and a tremendous resource ready for more exploration. This image shows a view of this genetic interaction network visualized with BioLayoutExpress3D. Patterns in the genetic network can help understand the organization of biological processes (see Michaut, et al. doi:10.1371/journal.pcbi.1001092). Image Credit: Magali Michaut, University of Toronto

Predicting Sequence of Events upon Ligand Binding Using PMT Model: A Case Study of Adenylate Kinase

Conformational transitions play a crucial role during the reaction cycle of many enzymes. In the case of adenylate kinase (AK), binding of ATP and AMP induces a conformational change where the closing of the LID and NMP domains over the core domain is followed by the phosphate transfer from ATP to ADP. This conformational change is rate-limiting for the enzymatic reaction, and AK is thought to be in equilibrium between open and close states. In this work, we studied the sequence of events along the conformational transition pathway. Using the Perturbation-based Markovian Transmission (PMT) model [Lu and Liang, PLOS Computational Biology, 2009], we study each of the 45 intermediate conformations available in PDB. We apply an initial perturbation on the binding domains of the enzyme, whose transmission is modeled as a Markovian Process. The dynamics of the probability flow is then computed by solving the Master Equation using a Krylov subspace method. From the landscape of time-evolving probability flow of all residues upon initial perturbation, we calculated the information entropy and related parameter for each residue. By analyzing time-dependent changes in entropy of residues located within or are in-contact with the LID/NMP domains, we predicted contacts that would break first for each conformation. Using the initial open state conformation only we are able to identify the next conformation along the conformation transition pathway with an average accuracy of 85% in predicted bond breakage We also predicted a set of critical residues with distinct dynamic behavior that are important in ligand binding.

A Review of 2010 for PLoS Computational Biology

A Review of 2010 for PLoS Computational Biology

PLoS Computational Biology Issue Image | Vol. 7(1) January 2011

Antibiotics have been the mainstay of treatment for a variety of infectious diseases such as pneumonia. Due to the increasing incidence of antimicrobial resistance, we are at risk of returning to the pre-antibiotic era, and the need to develop new drugs rapidly is becoming more urgent. A new computational method could provide quantitative insights to the interaction between an antimicrobial agent and a pathogen, which would facilitate new agent development by rationally guiding selection of dosing regimens. (See Tam and Nikolau, doi:10.1371/journal.pcbi.1001043.) Image Credit: Thomas Shea, University of Houston Cullen College of Engineering

The Whole Brain Catalog: Platform for neuroscientific data integration

The Whole Brain Catalog: Platform for neuroscientific data integration

PLoS Computational Biology Issue Image | Vol. 6(12) December 2010

PLoS Computational Biology Issue Image | Vol. 6(12) December 2010

Toward the integration of personalized and systems medicine: challenges, opportunities and approaches.

Personalized medicine: from disease-centered to human-centered medicine Personalized medicine deals with individual diversities and the complexity of the human body. Current healthcare models are diseaseoriented and focus on pathogens and environmental or external factors [4]. Personalized medicine would enable the change from such disease-centered medicine to human-centered medicine. Pharmacogenomics arose in response to such recognition. Pharmacogenomics studies genetic variations among individuals to predict disease susceptibility and responses to therapeutic agents [5,6]. The investigation of genetic diversity may enable the identification of optimal drug targets for certain patient populations, and empower physicians to make the right decisions. By focusing on patients’ genetic or biomarker profiles, pharmacogenomics represents the evolution from treating the disease itself to treating the malfunction of an individual person, the ‘root’ of the disease [7]. With such practices, patients can be regrouped and drugs can be recategorized. For instance, similar diseases may require different treatments, while different diseases may be treated with the same or similar approaches. Because of the diversity of patients’ biological backgrounds, the same disease may be caused by genetic variation in different people, who will respond differently to the same drug. For example, polymorphisms in the ABCB1 gene contribute to cancer risk and different therapeutic responses [8]. Patients with certain genotypes, such as 1236C/C, showed poorer survival than patients with other genotypes. Such situations require individualized treatments for the same disease (cancer) to ensure the best possible results. On the other hand, Challenges & new concepts in biomedicine In this era of change, biomedicine is heading toward a revolutionary new path. This path is a transformation from reductionism toward a holistic paradigm, from ‘one-size-fits-all’ therapeutics towards personalized medicine. Such changes are necessary to meet the challenges in healthcare and the pharmaceutical industry, for example, the high costs in healthcare, low efficacy of drugs and increased incidents of adverse drug reactions (ADRs). ADRs are one of the leading causes of death and illness in the USA [1]. Although the research and development costs in the pharmaceutical industry are soaring, high-profile drug withdrawals are elevating, while the US FDA’s approval for new drugs is decreasing [2]. At the same time, the gap between biomedical science and clinical practice has made it hard to translate scientific advancements into improved healthcare. These challenges and difficulties are calling for the development of new strategies, new concepts, or even a new kind of medicine to meet these goals:

Counterpoint: Activation of the Intrarenal Renin-Angiotensin System is the Dominant Contributor to Systemic Hypertension

Our discussion regarding the dominant mechanism responsible for hypertension calls to mind the famous poem, “The Blind Men and the Elephant,” by John Godfrey Saxe based on an ancient fable from India. In essence, the blind men described the elephant based on their specific encounter, thus

PLoS Computational Biology Issue Image | Vol. 6(11) November 2010

The phosphoprotein tau is normally bound to microtubules. However, tau that is abnormally phosphorylated dissociates from microtubules. Accumulated soluble tau molecules then misfold and aggregate. These aggregations are a hallmark of the tauopathies responsible for cognitive dysfunction and neuropathies such as Alzheimer's disease (see Yuraszeck et al. doi:10.1371/journal.pcbi.1000997). Image Credit: Peter Allen

PLoS Computational Biology Conference Postcards from ISMB 2010

The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the largest meeting of the International Society for Computational Biology (ISCB). In 2010 it was held in Boston, United States, July 11–13. What follows are four conference postcards that reflect different activities considered exciting and important by younger attendees. Postcards, as the name suggests, are brief reports on the talks and other events that interested attendees. You can read more about the idea of conference postcards at http://www.ploscompbiol.org/doi/pcbi.1000746, and if you are a graduate student or postdoctoral fellow, please consider contributing postcards at any future meetings of interest to the PLoS Computational Biology readership. We want to hear your view of the science being presented.

Up Against the Wall

![Figure][1] Rapoport CREDIT: PATRICK GILLOOLY/MIT Just about all serious marathon runners have experienced it. In the last half of a marathon, usually at about mile 21, their energy suddenly plummets. Their legs slow down, and it's almost impossible to make them go faster. Nutritionists blame carbohydrate loss: When the supply runs out, runners “hit the wall.” Now a model published this month in PLoS Computational Biology tells runners when they'll hit the wall, helping them to plan their carb-loading or refueling strategies accordingly. Benjamin Rapoport, a dual M.D.-Ph.D. student at Harvard Medical School and the Massachusetts Institute of Technology, says the idea began 5 years ago, when a class conflicted with his running in the Boston Marathon. His professor let him skip, provided he give a talk on the physiology of endurance running afterward. The talk became an annual tradition, and now he's quantified his ideas. Rapoport's model looks at multiple factors, such as a runner's desired pace, muscle mass, and aerobic capacity, the amount of oxygen the body can deliver to its muscles. “It's a real tour de force,” says physiologist Michael Joyner of the Mayo Clinic in Rochester, Minnesota, although he adds it is hard to account for all individual differences. Which is better, chowing down days before or grabbing some sugar during the race? “Both,” says sports nutritionist Edward Coyle of the University of Texas, Austin. “The carb loading will raise the glycogen levels in your muscles, and taking in carbs during the race will keep your blood glucose levels up.” And now Rapoport even has an app for that: Athletes will soon be able to calculate their thresholds at . [1]: pending:yes

PLoS Computational Biology Issue Image | Vol. 6(10) October 2010

PLoS Computational Biology Issue Image | Vol. 6(10) October 2010

PLoS Computational Biology Issue Image | Vol. 6(9) September 2010

PLoS Computational Biology Issue Image | Vol. 6(9) September 2010

PLoS Computational Biology Issue Image | Vol. 6(8) August 2010

PLoS Computational Biology Issue Image | Vol. 6(8) August 2010