26 January, 2007 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.
Transcript
26 January, 2007
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.
26 January, 2007
Why?Computational and Life Science?
So you need to go see a doctor?
26 January, 2007
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:
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)
26 January, 2007
A deeper picture of your medical profile. More depth. Cumulation of information points to specific
diagnosis.
But this required symptoms…
26 January, 2007
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.
26 January, 2007
Even with symptoms the diagnosis may be wrong.
Car link
26 January, 2007
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.
26 January, 2007
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?
26 January, 2007
What can be measured? Recorded? Compared between normal and diseased? Can a variance be measured? Is this variance predictive?
26 January, 2007
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?
26 January, 2007
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.
26 January, 2007
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.
26 January, 2007
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.)
26 January, 2007
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
26 January, 2007
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.
26 January, 2007
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
26 January, 2007
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
26 January, 2007
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.
26 January, 2007
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 ...
26 January, 2007
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.
“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
26 January, 2007
26 January, 2007
Ingenuity
26 January, 2007
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
26 January, 2007
Systems biology markup language
26 January, 2007
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 ...
26 January, 2007
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
26 January, 2007
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!
26 January, 2007
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.”
26 January, 2007
NCBI Derivative Sequence Data (Maureen J. Donlin, St. Louis University)
