25 replies to this topic

[Ghostrider]

What can bioinformatics do for longevity?

[maestro949]

Below is an example. Keep in mind that bioinformatics is still in infancy. Even pre-infancy. We're utilzing less than 1% of our potential IMO. The good news is that bio and chemoinformatics programs are popping up in universities around the world. As our ignorance of metabolism decreases and more engineers engage biology you'll see exponential growth in bioinformatics and increasing application in areas that will affect aging. We have all the tech we need today to engineer some really sophisticated tools, technologies and models that can simulate enormous amounts of complexity. We're just lacking the tools to capture the raw data at the subcellular level and femptosecond speed. That's changing pretty rapidly though. Exciting times are ahead.

Engineer joins fight against Alzheimer's, diabetes

December 8, 2006 - Christina Chan is using metabolic engineering and systems biology in the fight against Alzheimer’s disease and diabetes.

Using National Institutes of Health funding, Chan and an interdisciplinary team of researchers are taking a two-pronged approach to understanding and treating diseases that together contributed to the deaths of more than 300,000 Americans last year.

Working from what she calls a top-down approach, the researcher is developing mathematical models to help identify which genes and proteins are responsible for Alzheimer’s and diabetes.

Chan, an associate professor of chemical engineering and materials science, hopes that her systems biology research will lay the groundwork for software that can identify novel targets for treating these diseases from gene expression and metabolic data.

“This could be a highly effective tool to help people understand the genes or proteins causing any number of diseases,” she said. “For example, we could identify the pathways responsible for faster growth of certain cancer cells, shut them down, and thus slow the rate of cancer metastasis.”

Chan said pharmaceutical companies could also benefit from the systems biology framework.

“If you understand how a disease develops, you can develop more targeted ways of intervening and create drugs to treat the disease with minimal toxicity,” she said.

Chan is using RNA interference to interrupt the protein production of potential genetic culprits and studying the results. “The basic idea is to alter the genes that you determine may be involved in the diseases through the systems biology approaches and then experimentally validate if the outcome is what you expect,” she said.

Eventually, she would like to manipulate the disease-causing genes to prevent diabetes and Alzheimer’s, even in the presence of genetic predisposition and environmental causative agents.

In addition to the NIH, Chan’s research is funded by the National Science Foundation, the Environmental Protection Agency and the Whitaker Foundation.

Engineer joins fight against Alzheimer's, diabetes

Edited by maestro949, 09 December 2006 - 01:31 PM.

[olaf.larsson]

Are you a bioinformatican? If so search pubmed. I have seen articles about this specific question.

[Ghostrider]

Really? In pubmed, humm...I will check it out. No, I am not a bioinformatician, just interested in the field.

[olaf.larsson]

www.pubmed.org

Standard database for literature search. Create a list of keywords and search pubmed.org with them. Collect intressting articles in .pdf format in you computer and read them instead of watching TV. It will help you if you are at an university so that you can freely access the many articles you have to pay for otherwise.

[eldar]

Are you a bioinformatican? If so search pubmed. I have seen articles about this specific question.

Here is one. Haven't got around to read it yet so can't say much about it.

"How bioinformatics can help reverse engineer human aging." João Pedro de Magalhães, Olivier Toussaint

http://jp.senescence...areer/arr04.pdf

I'm myself comtemplating whether to specialize in bioinformatics so the question is of great interest to me too.
Doesn't seem to be too many bioinformaticians/bioinf wannabes here though.

[maestro949]

That's a good one ceth.

Also here's an example of using network analysis to hunt for aging genes..

I thought this paragraph summed up the challenges we face...

The decline in physiological function observed during aging differs from that associated with disease: Taffet (2002) lists 147 major physiological parameters that decline with age in 22 body systems. Furthermore, the decline is progressive and gradual, initially affecting only physiological reserves (Taffet, 2002). There is no disease that has such a widespread effect on the biological function of an organism (Braunwald et al., 2001). In diseases, some organs and functions are usually affected to a major extent and others only secondarily and in a minor way (Braunwald et al., 2001). Aging-related dysfunction has therefore special properties: it is global (because of the large number of physiological functions declining), generalized (no specific function predominates) and gradual (distributed over a considerable portion of life span). No disease possesses these properties to the same extent.We suggest that aging is the biological dysfunction where network level properties of the genes have the greatest importance. In contrast, disease-related dysfunctions are likely to preferentially involve specific portions of a biological network.

Summary: Aging is the failure of a network of interrelated body systems driven by a network of gene expression. Software engineering will need to be applied in many ways to find and catalogue the genomic and proteomic data and engineer solutions.

Attached Files

•  A_more_efficient_search_strategy_for_aging_genes.pdf   103.67KB   36 downloads

Edited by maestro949, 12 December 2006 - 12:25 PM.

[olaf.larsson]

Doesn't seem to be too many bioinformaticians/bioinf wannabes here though.

I dont know if im the only one at Imminst. I have studied bioinformatics together with classic molecular biology at my local univeristy.
The jobmarket isn't to good today though. Go to www.bioplanet.com to learn more about bioinfo.

[maestro949]

I dont know if im the only one at Imminst. I have studied bioinformatics together with classic molecular biology at my local univeristy.

So what's the path forward then? Do you see where computer science and biology can be utilized to fight aging in any meaningful way in the nearterm? Do you have any interest in leading or participating in any type of for-profit, nonprofit or research effort where informatics is applied to aging? You've posted on AI, do you think that AI is the most efficient use of engineering effort?

[olaf.larsson]

Do you see where computer science and biology can be utilized to fight aging in any meaningful way in the nearterm? Do you have any interest in leading or participating in any type of for-profit, nonprofit or research effort where informatics is applied to aging? You've posted on AI, do you think that AI is the most efficient use of engineering effort?

I think that computer simulationt could make a significant contribution to understand complex pathways. Suppose you have a description of a pathway; A gives B, B gives C, C inhibits A etc. Very soon a human looses sight of what is accually happening. Computers could help us very much to "see" by doing a model of the pathway what is acctually going on. I also belive in biology research could very well done from the desk alone this days. Just by collecting and reading articles and trying to make the puzzle fit together a person could have the same chances to make a significant contribution to aging research as a person that spends all day in a lab.
Considering the long term future development with robot researchers and other fantastic things I dont have much more ability to answer such questions then anyone else.

[maestro949]

I tend to agree. There are now 968 biological databases and this is just the tip of the iceberg. Unfortunately there are 0 databases dedicated to aging data other than information compilations such as Joao Pedro de Magalhaes' site (very good btw), the info scattered throughout the immInst forums, and difficult to access journal articles. We really need to normalize the scientific aging data better. Even if there isn't that much of it b/c once there's a glut of it, it will be too late.

[Mind]

Programming Biology

The little b project is an effort to provide an open source language which allows scientists to build mathematical models of complex systems. The initial focus is systems biology. The goal is to stimulate widespread sharing and reuse of models.

The little b language to allow biologists to build models quickly and easily from shared parts, and to allow theorists to program new ways of describing complex systems. Currently, libraries have been developed for building ODE models of molecular networks in multi-compartment systems such as cellular epithelia.

Aneil Mallavarapu is the author and inventor of little b, and runs the project. Little b is based in Common Lisp and contains mechanisms for rule-based reasoning, symbolic mathematics and object-oriented definitions. The syntax is designed to be terse and human-readable to facilitate communication. The environment is both interactive and compilable

[Connor MacLeod]

I'm myself comtemplating whether to specialize in bioinformatics so the question is of great interest to me too.

You could try www.scholar.google.com. Here's one I found using the keywords "bioinformatics" and "aging":

http://www.sciencedi...0215a3c14929d23

How bioinformatics can help reverse engineer human aging João Pedro de Magalhães and Olivier Toussaint

Unit of Cellular Biochemistry and Biology (URBC), Department of Biology, University of Namur (FUNDP), Rue de Bruxelles 61, B-5000, Namur, Belgium
Received 5 March 2003; Revised 28 August 2003; accepted 29 August 2003. Available online 6 February 2004.

Abstract

To study human aging is an enormous challenge. The complexity of the aging phenotype and the near impossibility of studying aging directly in humans oblige researchers to resort to models and extrapolations. Computational approaches offer a powerful set of tools to study human aging. In one direction we have data-mining methods, from comparative genomics to DNA microarrays, to retrieve information in large amounts of data. Afterwards, tools from systems biology to reverse engineering algorithms allow researchers to integrate different types of information to increase our knowledge about human aging. Computer methodologies will play a crucial role to reconstruct the genetic network of human aging and the associated regulatory mechanisms.

How's your knowledge of math, statistics and computer programming/algorithm development? I'm fairly strong in these areas - it was ultimately the biology and chemistry that turned me off.

[mike250]

I'm myself comtemplating whether to specialize in bioinformatics so the question is of great interest to me too.

You could try www.scholar.google.com. Here's one I found using the keywords "bioinformatics" and "aging":

http://www.sciencedi...0215a3c14929d23

How bioinformatics can help reverse engineer human aging João Pedro de Magalhães and Olivier Toussaint

Unit of Cellular Biochemistry and Biology (URBC), Department of Biology, University of Namur (FUNDP), Rue de Bruxelles 61, B-5000, Namur, Belgium
Received 5 March 2003; Revised 28 August 2003; accepted 29 August 2003. Available online 6 February 2004.

Abstract

To study human aging is an enormous challenge. The complexity of the aging phenotype and the near impossibility of studying aging directly in humans oblige researchers to resort to models and extrapolations. Computational approaches offer a powerful set of tools to study human aging. In one direction we have data-mining methods, from comparative genomics to DNA microarrays, to retrieve information in large amounts of data. Afterwards, tools from systems biology to reverse engineering algorithms allow researchers to integrate different types of information to increase our knowledge about human aging. Computer methodologies will play a crucial role to reconstruct the genetic network of human aging and the associated regulatory mechanisms.

How's your knowledge of math, statistics and computer programming/algorithm development? I'm fairly strong in these areas - it was ultimately the biology and chemistry that turned me off.

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

[maestro949]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

[mike250]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Edited by mike250, 21 August 2008 - 07:09 PM.

[Connor MacLeod]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Statistics and probability are pretty central to bioinformatics. There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Programming is easy in the sense that once you've learned one language its pretty straightfoward to pick up another. Mastering algorithmic concepts on the other hand is significantly more challenging. The book I mentioned above discusses some algorithms relevant to bioinformatics; a more general text that is commonly used in universities is "Introduction to Algorithms" by Cormen, et. al.

[maestro949]

There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Your book probably isn't all that dated. Many of the fundamental algorithms and models within bioinformatics haven't really changed all that much in the past decade or so. What has changed is the quantity and quality of the datasets that are emerging.

[mike250]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Statistics and probability are pretty central to bioinformatics. There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Programming is easy in the sense that once you've learned one language its pretty straightfoward to pick up another. Mastering algorithmic concepts on the other hand is significantly more challenging. The book I mentioned above discusses some algorithms relevant to bioinformatics; a more general text that is commonly used in universities is "Introduction to Algorithms" by Cormen, et. al.

thanks for that info. I will be sure to check those books out. do you know of any well-grounded book that covers the statistical and probability backgrounds needed in bioinformatics

[Connor MacLeod]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Statistics and probability are pretty central to bioinformatics. There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Programming is easy in the sense that once you've learned one language its pretty straightfoward to pick up another. Mastering algorithmic concepts on the other hand is significantly more challenging. The book I mentioned above discusses some algorithms relevant to bioinformatics; a more general text that is commonly used in universities is "Introduction to Algorithms" by Cormen, et. al.

thanks for that info. I will be sure to check those books out. do you know of any well-grounded book that covers the statistical and probability backgrounds needed in bioinformatics

I'm sure there are some books out there for people looking to get up to speed on the probability and statistics specifically used in bioinformatics, but I am just not familiar very with this. I could recommend some general probability and stats books depending on your background, but that's probably not the best way to go if you are primarily interested in bioinformatics.

What is your background in mathematics? What are your goals, e.g. are looking to apply for a undergraduate or graduate program, career change, etc.?

[mike250]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Statistics and probability are pretty central to bioinformatics. There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Programming is easy in the sense that once you've learned one language its pretty straightfoward to pick up another. Mastering algorithmic concepts on the other hand is significantly more challenging. The book I mentioned above discusses some algorithms relevant to bioinformatics; a more general text that is commonly used in universities is "Introduction to Algorithms" by Cormen, et. al.

thanks for that info. I will be sure to check those books out. do you know of any well-grounded book that covers the statistical and probability backgrounds needed in bioinformatics

I'm sure there are some books out there for people looking to get up to speed on the probability and statistics specifically used in bioinformatics, but I am just not familiar very with this. I could recommend some general probability and stats books depending on your background, but that's probably not the best way to go if you are primarily interested in bioinformatics.

What is your background in mathematics? What are your goals, e.g. are looking to apply for a undergraduate or graduate program, career change, etc.?

well I'm applying to bioinformatics next year. I've studied MATH 160, 170 and MATH 252. 160 is basically half calculus and half algebra. differentiation,vectors, integration, matrices, polynomials etc..... 170 involved linear transformations, Eigenvalues and eigenvectors, Taylor series etc...
Math 252 is just calculus. Partial derivatives, Gradient vectors, Steepest ascents, repeated and double integrals etc....

[Connor MacLeod]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Statistics and probability are pretty central to bioinformatics. There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Programming is easy in the sense that once you've learned one language its pretty straightfoward to pick up another. Mastering algorithmic concepts on the other hand is significantly more challenging. The book I mentioned above discusses some algorithms relevant to bioinformatics; a more general text that is commonly used in universities is "Introduction to Algorithms" by Cormen, et. al.

thanks for that info. I will be sure to check those books out. do you know of any well-grounded book that covers the statistical and probability backgrounds needed in bioinformatics

I'm sure there are some books out there for people looking to get up to speed on the probability and statistics specifically used in bioinformatics, but I am just not familiar very with this. I could recommend some general probability and stats books depending on your background, but that's probably not the best way to go if you are primarily interested in bioinformatics.

What is your background in mathematics? What are your goals, e.g. are looking to apply for a undergraduate or graduate program, career change, etc.?

well I'm applying to bioinformatics next year. I've studied MATH 160, 170 and MATH 252. 160 is basically half calculus and half algebra. differentiation,vectors, integration, matrices, polynomials etc..... 170 involved linear transformations, Eigenvalues and eigenvectors, Taylor series etc...
Math 252 is just calculus. Partial derivatives, Gradient vectors, Steepest ascents, repeated and double integrals etc....

Is this a Masters or PhD program? With your math background I'd say that one of Sheldon Ross' books, either "A first course in probability" or "Introduction to probability models" would be pretty decent for general probability; both of these books cover calculus based probability but the later is probably at a somewhat more sophisticated level. As far as general stats I think "Statistical Inference" by Casella and Berger is good, as well as "Introduction to Mathematical Statistics" by Hogg and Craig. None of these books are likely to be easy reading, but with some effort they would likely be within your reach. I suggest you read up on probability first.

[mike250]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Statistics and probability are pretty central to bioinformatics. There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Programming is easy in the sense that once you've learned one language its pretty straightfoward to pick up another. Mastering algorithmic concepts on the other hand is significantly more challenging. The book I mentioned above discusses some algorithms relevant to bioinformatics; a more general text that is commonly used in universities is "Introduction to Algorithms" by Cormen, et. al.

thanks for that info. I will be sure to check those books out. do you know of any well-grounded book that covers the statistical and probability backgrounds needed in bioinformatics

I'm sure there are some books out there for people looking to get up to speed on the probability and statistics specifically used in bioinformatics, but I am just not familiar very with this. I could recommend some general probability and stats books depending on your background, but that's probably not the best way to go if you are primarily interested in bioinformatics.

What is your background in mathematics? What are your goals, e.g. are looking to apply for a undergraduate or graduate program, career change, etc.?

well I'm applying to bioinformatics next year. I've studied MATH 160, 170 and MATH 252. 160 is basically half calculus and half algebra. differentiation,vectors, integration, matrices, polynomials etc..... 170 involved linear transformations, Eigenvalues and eigenvectors, Taylor series etc...
Math 252 is just calculus. Partial derivatives, Gradient vectors, Steepest ascents, repeated and double integrals etc....

Is this a Masters or PhD program? With your math background I'd say that one of Sheldon Ross' books, either "A first course in probability" or "Introduction to probability models" would be pretty decent for general probability; both of these books cover calculus based probability but the later is probably at a somewhat more sophisticated level. As far as general stats I think "Statistical Inference" by Casella and Berger is good, as well as "Introduction to Mathematical Statistics" by Hogg and Craig. None of these books are likely to be easy reading, but with some effort they would likely be within your reach. I suggest you read up on probability first.

these papers are undergraduate courses. thanks for those books. I think I might go through all of them so as to get a thorough understanding.

[Connor MacLeod]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Statistics and probability are pretty central to bioinformatics. There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Programming is easy in the sense that once you've learned one language its pretty straightfoward to pick up another. Mastering algorithmic concepts on the other hand is significantly more challenging. The book I mentioned above discusses some algorithms relevant to bioinformatics; a more general text that is commonly used in universities is "Introduction to Algorithms" by Cormen, et. al.

thanks for that info. I will be sure to check those books out. do you know of any well-grounded book that covers the statistical and probability backgrounds needed in bioinformatics

I'm sure there are some books out there for people looking to get up to speed on the probability and statistics specifically used in bioinformatics, but I am just not familiar very with this. I could recommend some general probability and stats books depending on your background, but that's probably not the best way to go if you are primarily interested in bioinformatics.

What is your background in mathematics? What are your goals, e.g. are looking to apply for a undergraduate or graduate program, career change, etc.?

well I'm applying to bioinformatics next year. I've studied MATH 160, 170 and MATH 252. 160 is basically half calculus and half algebra. differentiation,vectors, integration, matrices, polynomials etc..... 170 involved linear transformations, Eigenvalues and eigenvectors, Taylor series etc...
Math 252 is just calculus. Partial derivatives, Gradient vectors, Steepest ascents, repeated and double integrals etc....

Is this a Masters or PhD program? With your math background I'd say that one of Sheldon Ross' books, either "A first course in probability" or "Introduction to probability models" would be pretty decent for general probability; both of these books cover calculus based probability but the later is probably at a somewhat more sophisticated level. As far as general stats I think "Statistical Inference" by Casella and Berger is good, as well as "Introduction to Mathematical Statistics" by Hogg and Craig. None of these books are likely to be easy reading, but with some effort they would likely be within your reach. I suggest you read up on probability first.

these papers are undergraduate courses. thanks for those books. I think I might go through all of them so as to get a thorough understanding.

You're welcome. Here's a couple others you might want to look into at some point (probably after you've read a bit of probability)

http://www.amazon.co...e...3791&sr=1-2

http://www.amazon.co.../ref=pd_sim_b_2

Markov chains are very important in bioinformatics and also play a key role in Bayesian statistics (Markov chain monte carlo) which is a very hot area.

[Ghostrider]

I am fairly good in chemistry and biology but weaker in the other areas. I'm trying to catch-up however. Next year I will be hoping to start in the field.

The bioinformatics-aging field is very much a multi-discipline field. Frustratingly so. Besides the grounding you need in the various subfields of molecular biology, you really need to draw on math, statistics, and a handful of subfields of computer science. I'm not sure I could think of a more challenging cluster of fields. As a longevity-focused bioinformatician you may still die, but it won't be of boredom

I have found that there is plenty of programming to go through. statistics seem to pop-up here and there but the computer science section is quite fascinating and challenging to go through.

Statistics and probability are pretty central to bioinformatics. There's a nice book I bought a number of years ago: "Biological Sequence Analysis" by Durbin, Eddy, Krogh, and Mitchinson. Its probably a little dated by now, but still a nice reference. In any case, lots of stuff there on HMMs (hidden markov models), stochastic context free grammars, etc. Nice stuff, but to properly understand and use these models you need a solid background in stats and probability.

Programming is easy in the sense that once you've learned one language its pretty straightfoward to pick up another. Mastering algorithmic concepts on the other hand is significantly more challenging. The book I mentioned above discusses some algorithms relevant to bioinformatics; a more general text that is commonly used in universities is "Introduction to Algorithms" by Cormen, et. al.

thanks for that info. I will be sure to check those books out. do you know of any well-grounded book that covers the statistical and probability backgrounds needed in bioinformatics

I'm sure there are some books out there for people looking to get up to speed on the probability and statistics specifically used in bioinformatics, but I am just not familiar very with this. I could recommend some general probability and stats books depending on your background, but that's probably not the best way to go if you are primarily interested in bioinformatics.

What is your background in mathematics? What are your goals, e.g. are looking to apply for a undergraduate or graduate program, career change, etc.?

well I'm applying to bioinformatics next year. I've studied MATH 160, 170 and MATH 252. 160 is basically half calculus and half algebra. differentiation,vectors, integration, matrices, polynomials etc..... 170 involved linear transformations, Eigenvalues and eigenvectors, Taylor series etc...
Math 252 is just calculus. Partial derivatives, Gradient vectors, Steepest ascents, repeated and double integrals etc....

Is this a Masters or PhD program? With your math background I'd say that one of Sheldon Ross' books, either "A first course in probability" or "Introduction to probability models" would be pretty decent for general probability; both of these books cover calculus based probability but the later is probably at a somewhat more sophisticated level. As far as general stats I think "Statistical Inference" by Casella and Berger is good, as well as "Introduction to Mathematical Statistics" by Hogg and Craig. None of these books are likely to be easy reading, but with some effort they would likely be within your reach. I suggest you read up on probability first.

these papers are undergraduate courses. thanks for those books. I think I might go through all of them so as to get a thorough understanding.

You're welcome. Here's a couple others you might want to look into at some point (probably after you've read a bit of probability)

http://www.amazon.co...e...3791&sr=1-2

http://www.amazon.co.../ref=pd_sim_b_2

Markov chains are very important in bioinformatics and also play a key role in Bayesian statistics (Markov chain monte carlo) which is a very hot area.

Well, bioinformatics is related to machine learning and artificial intelligence. I can't think of more mathematically difficult topics in CS than AI and machine learning. I really wanted to take classes in these areas, but quite frankly, I think I come up 10 to 20 IQ points too short. It's not the background knowledge that I am missing, it's the ability to write up mathematical proofs, the "show this..." or "prove this..." type questions rather than the "find this..." or "calculate this...". Anyway, the lack of mathematical problem solving ability seems to be my main roadblock from entering AI / bioinformatics or computational biology as it also known. That's kind of depressed me lately, but the point of all this is checkout the program and make sure you have the adequate background before making any major life changes.

[JLL]

You don't have to be the best mathematician/programmer/whatever to do well, you just have to surround yourself with the best ones. For example, start your own business and hire them.