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Course Objectives

Course Objectives. To learn about research studies driving the field and computing techniques that have been developed. To learn about computational and informatics projects related to biology, medicine, and other “life science” disciplines at Emory.

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Course Objectives

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  1. Course Objectives • To learn about research studies driving the field and computing techniques that have been developed. • To learn about computational and informatics projects related to biology, medicine, and other “life science” disciplines at Emory. • To learn about opportunities for summer research and dissertation topics. • To stimulate ideas for further collaboration between Math/CS and X. But impossible to give a complete treatment of field.

  2. Why?Computational and Life Science? So you need to go see a doctor?

  3. Why CLS? • A look at your personal medical history • Do you eat right? Do you exercise? • Do you smoke? Do you drink alcohol? • What is your current / past profession? • Have you had any of the following: • Difficulty breathing Circulatory problems • Eating disorder Behavioral issues • Heart problems Visual impairment • Allergies Asthma

  4. A shallow picture of your medical profile. Ancestral Knowledge Future symptoms Information today Disease

  5. Follow a specific problem (nausea) • Additional lab tests (bacterial, viral, hormonal, ulcers, celiacs, diabetes, cancer) • More specific questions to determine extent of problem and other symptoms

  6. Look at your family medical history Has any one in your immediate family had any of the following: • Heart disease Diabetes • Cancer Alzheimers If so who? Mother, Father, Siblings, Aunts/ Uncles, Grandparents

  7. Ancestral Knowledge Future symptoms Information today Disease

  8. But how can this picture give any facts of the specifics, or causes of diseases within your ancestral medical history. • Was your grandmother a heavy smoker, was your grandfather overweight. • Even if similar symptoms the causes may be more due to personal choices or environment • How could we decipher the facts / causes? (venn diagram of symptoms and causes)

  9. A deeper picture of your medical profile. • More depth. • Cumulation of information points to specific diagnosis. But this required symptoms…

  10. Current diagnostics like to follow a single path at a time. • Do test, examine results, prove or disprove. • If disproved, evaluate a second route. Efficient in the case of clinical costs, inefficient in the cost of time.

  11. Even with symptoms the diagnosis may be wrong. Car link

  12. Well, the flow chart diagnosis has been completed and the final result is a defective PCM. I just had a strange feeling and I just cannot seem to accept that. … The other PCM made no difference. What went wrong? The diagnostic trouble chart was carefully followed and yet the end result was incorrect? Was the flow chart misleading? Absolutely NOT, one thing to KEEP IN MIND when following the flow charts is that the "MOST LIKELY" cause will be shown. There is no way to know exactly what fails from one case to another. I don't fault the information at all, as a matter of fact, even though the problem was not yet known, I do know, by following the flow chart, what areas are correct. So, there you have it, not every cause will be listed as the end result when using a diagnostic flow chart.

  13. The current information is not sufficient • What if we could add to this information? • What would you want to add? Can we start the diagnosis earlier – before symptoms? • Is one’s personal prevalence for a specific disease measurable? • How would one determine this?

  14. What can be measured? • Recorded? • Compared between normal and diseased? • Can a variance be measured? • Is this variance predictive?

  15. Back to data points. • Clinical lab studies (images, chemical monitoring, physical exam, etc.) • Scientists are currently accumulating data in multiple areas (DNA, RNA, protein, etc.) • Recording data for normals, diseased, with treatment, without treatment. • Many, many replicates! • Billions of data points • Comparison • What features correlate with normal or disease, etc. • Can this feature be predictive?

  16. Technology and CS Requirements Given 1000’s of instances • queriable database • feature definition, feature extraction • feature selection • comparison, classification, correlation • prediction • modeling: predictive risk models Will discuss this protocol in many different instances.

  17. DILS 2005 keynotes Shankar Subramaniam, Professor of Bioengineering and Chemistry at UCSD : • the standard paradigm in biology: ‘hypothesis to experimentation (low throughput data) to models’ is being replaced by ‘data to hypothesis to models’ and ‘experimentation to more data and models’. • need for robust data repositories that allow interoperable navigation, query and analysis across diverse data, a plug-and-play environment that will facilitate seamless interplay of tools and data and versatile biologist-friendly user interfaces.

  18. Databases • Data, Data, Data • Organization of database (studies, experiments, sample sets, patients, treatments) • Meta-data, including experimental conditions and clinical data • repeated data points • Secondary experimental procedures (more variate data) • Incomplete data sets • Multiple analysis runs (multiple data sets) (scaling, normalization, archive, comparisons, requerying) • From experimental results, re-query data on other meta-data and reprocess • Annotations of experimental data points (genes, proteins, etc.)

  19. Technology and CS Requirements • Definition of data structure • Download of data into database • Storage and retrieval • Security • Integrated database, data archive, analytical results archive • ... Feature selection and modeling generation of sophisticated, integrated predictive risk models

  20. Predictive Health Health: general condition of the body or mind with reference to soundness and vigor, freedom from disease or ailment. Diagnose: to recognize (a disease) by signs and symptoms, to analyze the cause or nature of. Predict: to declare in advance (of symptoms) on the basis of observation, experience, scientific reasoning.

  21. Predictive Health Predictive health is an “emerging paradigm that emphasizes maintaining health by detecting the genetic risk factors for illness and taking steps to prevent disease or illness before it starts.” “In the future, providers will combine an individual’s genetic information with cutting edge biotechnology to keep that person healthy. Eventually, the occurrence of disease will be seen as a failure of the health care system, rather than its main focus.” Momentum Summer 2006, Seeking Ponce’s Dream

  22. Momentum Winter 2006-2007, DNA Rubric “SNP accounts for some of the variation among humans. These naturally occurring differences, polymorphisms, help explain difference in human appearance and why some people are susceptible to diseases like lung cancer and others aren’t. They also provide an explanation for why there can be individualized responses to environmental factors and medications.” “These patterns (of specific variation) will help us predict the future health of an individual and develop personalized health treatments, including specific drugs tailored to each individual, given their specific genetic code.” Scott Devine, PhD, Biochemistry

  23. Predictive Health 2007 Center for Health Discovery and Well-Being • participants - 100 - 200 generally healthy people • collect physical, medical and lifestyle histories, environmental factors • perform 50+ blood and plasma tests (including genotypes) that target known critical predictors of health and illness • the research program will develop and validate novel biologic markers to predict health, disease risk, and prognosis. • based on these profiles and a predictive risk model, each participant will be prescribed a personalized health program designed to address individual risks.

  24. Technology and CS Requirements • Database and Security • Integrated database and data archive • Feature definition, feature extraction • Feature selection • Comparison, classification • Prediction • Modeling: sophisticated, integrated predictive risk models • Annotations, data-mining • ...

  25. Systems Biology Systems Biology is the science of discovering, modeling, understanding and ultimately engineering at the molecular level the dynamic relationships between the biological molecules that define living organisms. Leroy Hood, ISB http://www.systemsbiology.org/Systems_Biology_in_Depth

  26. Momentum Winter 2006-2007, Fresh Air “Molecular signaling pathways within normal cells follow a cascade of molecular reactions that emit proteins, which turn on…” “The premise acknowledges that a single genetic mutation doesn’t cause lung cancer. Instead there are many causes on the cellular level, with many genetic mutations from many different sources.” Fadlo Khuri, PhD. Clinical and Translational Research

  27. Ingenuity

  28. List of Model Repositories: • CellML: biochemical and cellular processes • DOQCS: DB of Quantitative and Cellular Signalling • Model DB: Sense Lab, nerves and neurons • SigPath and SigMoid: Signalling pathways • PathArt: Metabolic pathways

  29. Systems biology markup language

  30. Technology and CS Requirements • Database and Security • Integrated database and data archive • Feature definition, feature extraction • Feature selection • Comparison, classification, correlation • Prediction • Modeling: sophisticated, integrated predictive risk models • Annotations, data-mining • ...

  31. CS in CLS? 5% of biological researchers have hired a CS or DB staff. 95% who don’t because: • do not see the need, • have no experience in CS or managing CS, • can not raise the funds. Communication, Communication, Communication

  32. Meta-Objectives • How does a CS knowledgeable person become an X-informatics or computational-X researcher? • How useful is it to work with just symbolic abstractions? • How much X does one need to learn for the research to be meaningful? • How can it be more mutual collaboration? • Most of the time, it is just CS servicing X. • X researchers really don’t care how the CS is done. Just Do It!

  33. Meta-Objectives: CS in CLS? The CS scientist should know enough biology to probe beyond the obvious question that the biologist is asking. Be able to and willing to offer direction. “You can use this CS technology or algorithm to answer X about your data.”

  34. NCBI Derivative Sequence Data (Maureen J. Donlin, St. Louis University) C C Curators GA ATT GA GA C ATT GA C RefSeq TATAGCCG ACGTGC TATAGCCG AGCTCCGATA CCGATGACAA ATTGACTA CGTGA TTGACA Labs TTGACA TTGACA ACGTGC Genome Assembly TATAGCCG ACGTGC TATAGCCG ATTGACTA CGTGA CGTGA ATTGACTA TATAGCCG CGTGA ATTGACTA ATTGACTA TATAGCCG TTGACA ATTGACTA TATAGCCG TATAGCCG TATAGCCG TATAGCCG ATT C GenBank UniGene GA AT GA C C Algorithms ATT C C GA ATT GA GA ATT GA GA ATT C GA C ATT GA

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