Some of us are involved in the [email protected] project, spending time, money, and resources.
I would like to know the answer to two main questions:
- How do we know we fold it right? I mean, these models used in folding calculations. Is it probable that in a decade we will find that all these results are nil and void?
- Do we have any example that these calculations were practically applied anywhere?
in silico modelling of anything in biology is an active field of research. It's very useful for making predictions and developing hypotheses, but any findings need to be confirmed experimentally.
From the [email protected] website:
[email protected] has been a success. In 2000-2001, we folded several small and fast folding proteins with experimental validation of our method. We are now working to further develop our method, and to apply it to more complex and interesting proteins and protein folding and misfolding questions. Since then (2002-2006), [email protected] has studied more complex proteins, reporting on the folding of many proteins on the microsecond timescale, including BBA5, the villin headpiece, Trp Cage, among others. In 2007, we crossed the millisecond milestone by simulating a protein called NTL9, and the 10 millisecond barrier in 2010 with ACBP.
More recently (2006-present), we have been putting a great deal of effort into studying proteins relevant for diseases, such as Alzheimer's and Hunntington's Disease. You can learn more about our results and peer-reviewed scientific achievements on our Papers Page.
As they say, you should check out their research contributions for how it's being practically applied.
You may also be interest in this article for the science behind and prospects of protein folding prediction: Bowman GR, Voelz VA, Pande VS. 2011. Taming the complexity of protein folding. Curr Opin Struct Biol 21(1):4-11. At least try reading the conclusion for a less technical overview.
I will try answer your question directly.
How do we know if we fold it right?
A. If you're interested in only the end product pf folding -- the 3D structure, then this is the subset of the folding problem called the structure prediction (from sequence alone).
a. We can verify the structure experimentally by determining the 3D structures by NMR or crystallography. In fact, the CASP competition withholds newly solved structures and see which algorithm comes out the closest to the experimental structure.
b. Based on Anfinsen's hypothesis, we can calculate the free energy of folding, and the structure that has the lowest energy among the assembles is probably (but not necessarily) the folded state.
B. If you're also interested in how exactly the protein folds, including its folding rate, plausible intermediates etc.:
a. You can experimentally determine the folding rate although the computed folding rate is often off by several order of magnitude.
b. Some interactions, esp non-native interactions (aka. interactions between amino acids that are only observed during folding but not in the final folded structure) can be verified by carefully designed NMR experiment.
1b. Yes it is possible. Adjusting the force constants and force field in molecular dynamics (MD) (the core engine of [email protected]) so that the calculations converge to consistent and sensible results is not trivial.
- If you mean the method of calculation, then yes, MD has a wide range of application outside the field of protein folding. Besides the improvement in MD algorithm and necessary approximation, it also facilitates some advances on calculation on more specialized hardwares e.g. GPU and programmable array.
2b. If you mean the end of product of computation model, then probably no, but one example came close. A model determined purely computationally (aka. ab initio), although still not as useful as an experimental structure, was close enough to facilitate the experimental determination of that structure (by a method called molecular replacement in X-ray crystallography)
A certain fold of a peptide string can be validated or ruled out if other experimental data is available. Some other techniques to infer protein structure are X-ray crystallography (requires pure protein that will crystallize) and single particle analysis.
[email protected]: Citizen Scientists Gain Insight on COVID-19
Image: The protease is the target of the COVID-Moonshot project, a collaboration [email protected] is participating in with chemists and biologists around the world. Read more about the project, and see it featured on [email protected]’s “Meet the Proteins” series.
As the COVID-19 pandemic makes waves around the world, scientists and researchers are searching for a remedy. One project, [email protected] ([email protected]), has emerged as a leader in this research. Launched in 2000, [email protected] is a distributed computing project through which citizen, or volunteer, scientists study and simulate proteins. Since its launch, the project has played a role in research on Alzheimer’s disease and Ebola. Now, participating scientists are tackling COVID-19.
We connected with Sukrit Singh (Ph.D. candidate, Washington University in St. Louis and, [email protected] team member) to learn more about the project’s background, its impact to date on COVID-19 research, and the importance of the citizen-scientist community. Also, John Fenno (global marketing director of GeForce, NVIDIA) shares why the team at NVIDIA has long been a collaborator on the project.
“[email protected] has been doing great work for many years, rallying gamers to donate extra computing capacity for research. When COVID-19 hit California, we were scared and nervous like everyone else, and our social media team asked what we could do to help,” Fenno says. “At the same time, Pedro at PCMR alerted us to renewed [email protected] efforts, supporting research. [email protected] needed some help driving awareness — it was a no-brainer for us to promote the effort globally through our GeForce social channels.”
Read on for a deep dive into [email protected] from Singh.
SIGGRAPH: Share some background on [email protected] How did it get its start? How has it supported past disease research before taking on coronavirus?
Sukrit Singh (SS): [email protected] ([email protected]) launched in 2000 as a distributed computing project to study “protein folding,” the phenomena by which the proteins in our body (linear chains of amino acids) assemble into complex shapes that allow us to see, move, think, and more. Imagine if you were able to take the individual parts of a car and lay them out in your driveway, and once you did, the components spontaneously assembled themselves into a fully functioning car!
Computers can simulate the movement of atoms over time with an approach known as molecular dynamics (MD). MD acts as a “computational microscope” that lets us study atomic motions and how they relate to protein behavior and biological function. However, studying protein folding is a computationally expensive task, and generating sufficient data to study protein motions can take hundreds of years on a single desktop computer.
Inspired by [email protected], [email protected] founder Vijay Pande and his team at Stanford built [email protected] as a way to run MD simulations of proteins. To achieve this, the team worked out a way to break up MD simulations into smaller chunks and distribute them across the internet. This way, the protein motions that might take hundreds of years to observe on a single desktop can be modeled in much less time by distributing the calculation across hundreds of desktops. In 2019, Prof. Greg Bowman (Washington University in St. Louis) became director of the [email protected] project.
Many early breakthroughs were made over the next decade with [email protected], including the first observation of the complete folding pathway of a protein in 2010. Over 200 papers using [email protected] have been published on a variety of topics. Notably, [email protected] simulations have allowed scientists to find novel, small-molecule inhibitors to β-Lactamase and model never-before-seen processes in our cells that relate to cancer.
Most recently, the Bowman lab released a preprint that computationally identified a cryptic pocket in an ebolavirus protein that is critical for function. In the publication, this prediction is followed up with experiments that support the existence of this pocket.
Now, we are bringing all the power behind [email protected] to fight the coronavirus.
SIGGRAPH: Let’s get technical. How do citizen scientists use [email protected]?
SS: More information about the background of [email protected] and how the gaming community can help now can be found in here.
In terms of a description of what happens when you participate, here’s a brief overview: When someone downloads the [email protected] app and runs our software (the “client”), they are connected to our servers and receive a “work unit”, which is one part of a simulation. Each simulation is a collection of atoms moving over time. The movement of these atoms relative to one another can be described using Newton’s laws of motion. Over a long enough simulation, the proteins will “shapeshift” and change their structure. These new structures can have useful information, explaining biological functions that the protein performs or providing insight into why certain mutations cause disease. Some new structures may even present new druggable opportunities.
Each simulation, which starts from a single configuration of atoms with positions and velocities, is broken up into work units with each one representing one temporal component of the whole simulation. This means that a completed work unit has “time-series” data of atoms and their positions over time. Each work unit is completed sequentially — that is, one work unit must be completed, and the end of that work unit is the start of the subsequent work unit for the next client.
SIGGRAPH: How do you fold proteins?
SS: When we analyze work units that our community returns, we do so by stitching the work units together into long time-series trajectories. For any one project, we end up with multiple “movies” of the atoms moving over time and the protein shape-shifting.
We then utilize multiple statistical methods to parse and analyze these “movies”. In particular, we often construct Markov state models (MSMs), which are “map” representations of a protein’s structural diversity. In these MSMs, each “state” is a unique structure of the protein where the atoms are in a specific, distinct configuration. We then use [email protected] data to estimate the probability of moving from any one state into another. This way, we have a view of the full “landscape” of structures a protein can adopt.
One particularly useful feature of these MSMs is our ability to identify new areas on proteins that can serve as targets for drug design. As the protein moves over time (adopting different states in our MSM), the atoms shift around relative to one another which exposes new surfaces, grooves, or pockets of atoms to the environment. These previously unobserved “cryptic pockets” often are appropriately shaped to bind a drug-like molecule and present promising targets for future drug design methods.
These cryptic pockets can be hard to identify with experimental data alone, but they can be observed using the computing power of [email protected] In fact, as I mentioned above, we recently published a paper where we utilized [email protected] and our computational toolset to identify a cryptic pocket in an ebolavirus protein and were able to experimentally support its existence. Importantly, jamming this pocket open with a small-molecule label inhibits protein function, making this pocket a promising target for drug design methods! This paper is available currently as a preprint on bioRxiv.
SIGGRAPH: How has [email protected] impacted COVID-19 so far?
SS: It is important to note that while scientific research takes time, we have already made progress and new insights into COVID-19 as we have scaled up the research infrastructure of [email protected]!
One particularly exciting finding we recently released was our observation of the motions of the SARS-CoV-2 Spike protein. The Spike protein exists on the surface of the virus (visible in most renderings of the viral particle) and undergoes an opening process that allows it to bind human host cells to infect them. Using adaptive-learning algorithms and [email protected], we have observed the process of opening. Knowing the states adopted in this opening process could be useful for antibody and therapeutic design.
We also are simulating proteins of SARS-CoV-2 to hunt for new cryptic pockets. Empowered by our community’s size and enthusiasm, we have launched almost every known protein structure from SARS-CoV-2 onto [email protected], and, with our citizen scientists, we are generating data at a breakneck pace (greater than 100 times what we were generating prior to the pandemic). In the last few weeks, we have launched 126 projects focused on SARS-CoV-2 and almost 12 are already being run through our analysis pipelines to hunt for therapeutic opportunities.
Thanks to our network of citizen scientists, the combined compute power of [email protected] recently crossed into the exascale, climbing as high as 2.4 x 86 exaFLOPs. This makes us 15 times faster than IBM Summit and faster than the top 500 supercomputers in the world combined! This affords us the opportunity to try multiple strategies against this virus.
One such strategy [email protected] is participating in is the “COVID-moonshot,” a crowdsourced chemistry initiative to identify chemical inhibitors against one target of SARS-CoV-2, the main protease. Here, [email protected] simulations are simulating potential small-molecule inhibitors against the protease target, and this information is helping chemists around the world prioritize and optimize their synthetic strategies to design a viable therapeutic. These suggestions are crowdsourced, and anybody is welcome to submit ideas for new molecules to try out.
One awesome part of [email protected] has been watching communities, both academic and corporate, come together against this virus. We have been lucky to partner with academic groups around the world, such as the Joint European Disruptive Initiative (JEDI) and the COVID Moonshot project, as well as companies like Microsoft, Avast, NVIDIA, and Oracle. We also are thankful for the long-time support of many communities on the internet, such as Linus Tech Tips and the PCMR subreddit community.
Image credit: [email protected] team members during a Consortium Meetup in October 2019
SIGGRAPH: With over 1 million devices now part of [email protected] (even bitcoin miners have joined the charge), how do you continue to build the community? Why is the community aspect so important?
SS: The power of [email protected] is in its distributed nature and derives directly from our community. This means that every citizen scientist has been equally important in pushing [email protected] into exascale computing power and allowing us to tackle science at an unprecedented scale.
We would not be where we are now without the members of our community who have stepped up and helped out. We have grown almost 60 times in power and demand for work units, and with that we have gained many new users who may need support with the client or want to learn more about the science. With time and resources being limited, the long-time members of our community on the forums and our Discord channel have stepped up and started answering the hundreds of questions that have poured in. Our community members should feel proud for everything they’ve done to help us.
Further, this surge in demand and resources has given the [email protected] team more opportunities to interact with our community. Along with our forums and social media channels, we now have a verified Discord channel. This Discord channel is particularly exciting because community members can interact with one another, get support, compare Folding PC specs, or just generally hang out with their fellow citizen-scientists as we tackle COVID-19 together.
SIGGRAPH: Where do you envision [email protected] will be in the future, post-COVID-19?
SS: COVID-19 is just one of the many biomedical problems we are tackling, and we are excited to tackle more diseases with our newfound power. Prior to COVID-19, [email protected] simulations studied multiple biomedical problems from multiple types of cancer to antibiotic resistant infections. We want to bring the same power we have now against all of these maladies, working to identify new therapeutic strategies and making insight into fundamental biological questions.
We hope to retain many of our community members moving forward. This surge has been incredible, and we are developing ways to further engage with our citizen scientists to share progress and results as we generate data. Good science takes time, but we are excited to share all we have! We plan to release our data, methods, and code as open-source, and to publish on open-access websites like bioRxiv.
Further commenting on NVIDIA’s support of [email protected], Fenno noted, “Our first tweet reached millions of gamers, and we’re happy to see that this and other ecosystem efforts helped mobilize gamers to renew their support for a great cause. Today, over 500K GeForce gamers are contributing their hardware, and [email protected] has achieved 2.5 exaFLOPs of power, making it the largest distributed supercomputer solution ever created. We’re proud of the whole gaming community for stepping up to support [email protected] We can only hope the research efforts will help the medical community discover treatments as quickly as possible.”
Sukrit Singh is a Ph.D. candidate in molecular biophysics at Washington University in St. Louis and a member of Prof. Greg Bowman’s lab and the [email protected] team. He started his thesis work in 2015, joining Prof. Bowman’s lab right after it had first opened. Since then, Sukrit has been involved in [email protected] primarily as a scientist and researcher but also has been managing social media, communications, and outreach since 2019.
Prior to [email protected]’s shift to COVID-19 studies, Sukrit’s thesis work focused on studying information transfer in proteins, a phenomena known as “allostery.” Sukrit’s focus on allostery and protein dynamics centers around cell signaling, specifically on understanding molecular switches implicated in uveal melanoma and other cancers.
Sukrit originally hails from India, but also spent 10 years of his life in Singapore before obtaining his Bachelor’s degree in chemistry and biology (double major) at Washington University in St. Louis. During his undergraduate years, he worked in the lab of Dr. Garland Marshall, developing new peptidomimetics against HIV and bacterial infections.
John Fennois the global marketing director of GeForce at NVIDIA. He manages a shared resource team covering social media, influencer marketing, and original content videos.
Fenno’s marketing career began at Mattel, Inc., where he managed the Hot Wheels, Batman, and DC Comics brands. He later made the leap from toys to video games, joining Electronic Arts to work on the Harry Potter, The Sims, and Maxis businesses, serving as global marketing lead for The Sims 4. Fenno’s career spans entertainment marketing, branded packaged goods, global and regional marketing, and shared service team management.
September 29, 2007
What can Thermodynamics tell us?
Thermodynamics (and it's "older brother" statistical mechanics -- but I'll refer to both as Thermodynamics) plays a very important role in what we do. I thought I'd talk a little about it. Thermodynamics plays a major role in several aspects of work. What thermodynamics helps us answer is what the eventual probability of finding various states would be in equilibrium (i.e. if we wait long enough for everything to settle down). For example, what is the probability of finding the folded state?
First, let me talk a bit about how to think of this concept of probability. One way to think of this is in terms of ensembles -- if there's a 70% chance of finding the native state, that means that if we ran 100 simulations and looked at them to see which ones folded, 70 of them would have folded. We can also think of this in terms of time -- if there's a 70% chance of finding the native state, that means that if we look at a single protein, 70% of the time we'll see it in the folded state. Either way works and often we use these two ideas interchangeably.
WIth knowing about thermodynamics, we can develop and apply new tricks which let us run our calculations much more efficiently. In particular, sometimes the probabilities we're looking for is 1 in a million. In cases like that, it's not efficient at all to run millions of simulations to look for a few rare cases. Instead, with a knowledge of thermodynamics, we have methods that allow one to use many fewer simulations and still find these rare events.
I teach Thermodynamics to juniors at Stanford in the fall and Statistical Mechanics to graduate students in the Spring. The nature of the course is the basis behind the research that we do.
Lab Report Essentials
Not all lab reports have title pages, but if your instructor wants one, it would be a single page that states:
- The title of the experiment.
- Your name and the names of any lab partners.
- Your instructor's name.
- The date the lab was performed or the date the report was submitted.
The title says what you did. It should be brief (aim for ten words or less) and describe the main point of the experiment or investigation. An example of a title would be: "Effects of Ultraviolet Light on Borax Crystal Growth Rate". If you can, begin your title using a keyword rather than an article like "The" or "A".
Introduction or Purpose
Usually, the introduction is one paragraph that explains the objectives or purpose of the lab. In one sentence, state the hypothesis. Sometimes an introduction may contain background information, briefly summarize how the experiment was performed, state the findings of the experiment, and list the conclusions of the investigation. Even if you don't write a whole introduction, you need to state the purpose of the experiment, or why you did it. This would be where you state your hypothesis.
List everything needed to complete your experiment.
Describe the steps you completed during your investigation. This is your procedure. Be sufficiently detailed that anyone could read this section and duplicate your experiment. Write it as if you were giving direction for someone else to do the lab. It may be helpful to provide a figure to diagram your experimental setup.
Numerical data obtained from your procedure usually presented as a table. Data encompasses what you recorded when you conducted the experiment. It's just the facts, not any interpretation of what they mean.
Describe in words what the data means. Sometimes the Results section is combined with the Discussion.
Discussion or Analysis
The Data section contains numbers the Analysis section contains any calculations you made based on those numbers. This is where you interpret the data and determine whether or not a hypothesis was accepted. This is also where you would discuss any mistakes you might have made while conducting the investigation. You may wish to describe ways the study might have been improved.
Most of the time the conclusion is a single paragraph that sums up what happened in the experiment, whether your hypothesis was accepted or rejected, and what this means.
Figures and Graphs
Graphs and figures must both be labeled with a descriptive title. Label the axes on a graph, being sure to include units of measurement. The independent variable is on the X-axis, the dependent variable (the one you are measuring) is on the Y-axis. Be sure to refer to figures and graphs in the text of your report: the first figure is Figure 1, the second figure is Figure 2, etc.
If your research was based on someone else's work or if you cited facts that require documentation, then you should list these references.
Estimating the Uncertainty in Measurements
Before you combine or do anything with your uncertainty, you have to determine the uncertainty in your original measurement. This often involves some subjective judgment. For example, if you’re measuring the diameter of a ball with a ruler, you need to think about how precisely you can really read the measurement. Are you confident you’re measuring from the edge of the ball? How precisely can you read the ruler? These are the types of questions you have to ask when estimating uncertainties.
In some cases you can easily estimate the uncertainty. For example, if you weigh something on a scale that measures down to the nearest 0.1 g, then you can confidently estimate that there is a ±0.05 g uncertainty in the measurement. This is because a 1.0 g measurement could really be anything from 0.95 g (rounded up) to just under 1.05 g (rounded down). In other cases, you’ll have to estimate it as well as possible on the basis of several factors.
Significant Figures: Generally, absolute uncertainties are only quoted to one significant figure, apart from occasionally when the first figure is 1. Because of the meaning of an uncertainty, it doesn’t make sense to quote your estimate to more precision than your uncertainty. For instance, a measurement of 1.543 ± 0.02 m doesn’t make any sense, because you aren’t sure of the second decimal place, so the third is essentially meaningless. The correct result to quote is 1.54 m ± 0.02 m.
The Human Genome Project, an international research effort completed in 2003, determined the sequence of nucleotides for each human chromosome. This sequence information allows researchers to provide a more specific address than the cytogenetic location for many genes. A gene’s molecular address pinpoints the location of that gene in terms of nucleotides. It describes the gene’s precise position on a chromosome and indicates the size of the gene. Knowing the molecular location also allows researchers to determine exactly how far a gene is from other genes on the same chromosome.
Different groups of researchers often present slightly different values for a gene’s molecular location. Researchers interpret the sequence of the human genome using a variety of methods, which can result in small differences in a gene’s molecular address.
Our staff scientists offer the following tips for thinking about and writing good hypotheses.
- The question comes first. Before you make a hypothesis, you have to clearly identify the question you are interested in studying.
- A hypothesis is a statement, not a question. Your hypothesis is not the scientific question in your project. The hypothesis is an educated, testable prediction about what will happen.
- Make it clear. A good hypothesis is written in clear and simple language. Reading your hypothesis should tell a teacher or judge exactly what you thought was going to happen when you started your project.
- Keep the variables in mind. A good hypothesis defines the variables in easy-to-measure terms, like who the participants are, what changes during the testing, and what the effect of the changes will be. (For more information about identifying variables, see: Variables in Your Science Fair Project.)
- Make sure your hypothesis is "testable." To prove or disprove your hypothesis, you need to be able to do an experiment and take measurements or make observations to see how two things (your variables) are related. You should also be able to repeat your experiment over and over again, if necessary.
To create a "testable" hypothesis make sure you have done all of these things:
- Thought about what experiments you will need to carry out to do the test.
- Identified the variables in the project.
- Included the independent and dependent variables in the hypothesis statement. (This helps ensure that your statement is specific enough.
Don&rsquot Over-Interpret the Results!
Interpretation is a subjective exercise. As such, you should always approach the selection and interpretation of your findings introspectively and to think critically about the possibility of judgmental biases unintentionally entering into discussions about the significance of your work. With this in mind, be careful that you do not read more into the findings than can be supported by the evidence you have gathered. Remember that the data are the data: nothing more, nothing less.
MacCoun, Robert J. "Biases in the Interpretation and Use of Research Results." Annual Review of Psychology 49 (February 1998): 259-287.
Structure and Writing Style
I. Types of Abstracts
To begin, you need to determine which type of abstract you should include with your paper. There are four general types.
A critical abstract provides, in addition to describing main findings and information, a judgment or comment about the study&rsquos validity, reliability, or completeness. The researcher evaluates the paper and often compares it with other works on the same subject. Critical abstracts are generally 400-500 words in length due to the additional interpretive commentary. These types of abstracts are used infrequently.
A descriptive abstract indicates the type of information found in the work. It makes no judgments about the work, nor does it provide results or conclusions of the research. It does incorporate key words found in the text and may include the purpose, methods, and scope of the research. Essentially, the descriptive abstract only describes the work being summarized. Some researchers consider it an outline of the work, rather than a summary. Descriptive abstracts are usually very short, 100 words or less.
The majority of abstracts are informative. While they still do not critique or evaluate a work, they do more than describe it. A good informative abstract acts as a surrogate for the work itself. That is, the researcher presents and explains all the main arguments and the important results and evidence in the paper. An informative abstract includes the information that can be found in a descriptive abstract [purpose, methods, scope] but it also includes the results and conclusions of the research and the recommendations of the author. The length varies according to discipline, but an informative abstract is usually no more than 300 words in length.
A highlight abstract is specifically written to attract the reader&rsquos attention to the study. No pretense is made of there being either a balanced or complete picture of the paper and, in fact, incomplete and leading remarks may be used to spark the reader&rsquos interest. In that a highlight abstract cannot stand independent of its associated article, it is not a true abstract and, therefore, rarely used in academic writing.
II. Writing Style
Use the active voice when possible, but note that much of your abstract may require passive sentence constructions. Regardless, write your abstract using concise, but complete, sentences. Get to the point quickly and always use the past tense because you are reporting on a study that has been completed.
Abstracts should be formatted as a single paragraph in a block format and with no paragraph indentations. In most cases, the abstract page immediately follows the title page. Do not number the page. Rules set forth in writing manual vary but, in general, you should center the word "Abstract" at the top of the page with double spacing between the heading and the abstract. The final sentences of an abstract concisely summarize your study&rsquos conclusions, implications, or applications to practice and, if appropriate, can be followed by a statement about the need for additional research revealed from the findings.
Composing Your Abstract
Although it is the first section of your paper, the abstract should be written last since it will summarize the contents of your entire paper. A good strategy to begin composing your abstract is to take whole sentences or key phrases from each section of the paper and put them in a sequence that summarizes the contents. Then revise or add connecting phrases or words to make the narrative flow clearly and smoothly. Note that statistical findings should be reported parenthetically [i.e., written in parentheses].
Before handing in your final paper, check to make sure that the information in the abstract completely agrees with what you have written in the paper. Think of the abstract as a sequential set of complete sentences describing the most crucial information using the fewest necessary words.
The abstract SHOULD NOT contain:
- Lengthy background or contextual information,
- Redundant phrases, unnecessary adverbs and adjectives, and repetitive information
- Acronyms or abbreviations,
- References to other literature [say something like, "current research shows that. " or "studies have indicated. "],
- Using ellipticals [i.e., ending with ". "] or incomplete sentences,
- Jargon or terms that may be confusing to the reader,
- Citations to other works, and
- Any sort of image, illustration, figure, or table, or references to them.
Abstract. Writing Center. University of Kansas Abstract. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College Abstracts. The Writing Center. University of North Carolina Borko, Harold and Seymour Chatman. "Criteria for Acceptable Abstracts: A Survey of Abstracters' Instructions." American Documentation 14 (April 1963): 149-160 Abstracts. The Writer&rsquos Handbook. Writing Center. University of Wisconsin, Madison Hartley, James and Lucy Betts. "Common Weaknesses in Traditional Abstracts in hte Social Sciences." Journal of the American Society for Information Science and Technology 60 (October 2009): 2010-2018 Procter, Margaret. The Abstract. University College Writing Centre. University of Toronto Riordan, Laura. &ldquoMastering the Art of Abstracts.&rdquo The Journal of the American Osteopathic Association 115 (January 2015 ): 41-47 Writing Report Abstracts. The Writing Lab and The OWL. Purdue University Writing Abstracts. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University Koltay, Tibor. Abstracts and Abstracting: A Genre and Set of Skills for the Twenty-First Century. Oxford, UK: 2010 Writing an Abstract for Your Research Paper. The Writing Center, University of Wisconsin, Madison.
Q: How to write the rationale for research?
I need some ideas on how to write the rationale for my research. Do you have any suggestions?
The rationale of your research is the reason for conducting the study. The rationale should answer the need for conducting the said research. It is a very important part of your publication as it justifies the significance and novelty of the study. That is why it is also referred to as the justification of the study. Ideally, your research should be structured as observation, rationale, hypothesis, objectives, methods, results and conclusions.
To write your rationale, you should first write a background on what all research has been done on your study topic. Follow this with ‘what is missing’ or ‘what are the open questions of the study’. Identify the gaps in the literature and emphasize why it is important to address those gaps. This will form the rationale of your study. The rationale should be followed by a hypothesis and objectives.
To learn in depth how to write a persuasive Introduction for your research paper, check out this course designed exclusively for researchers: How to write a strong introduction for your research paper.