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Math Graduate needs "Cancer Biology for Dummies"

by dkotschessaa
Tags: graduate, math
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dkotschessaa
#1
May22-14, 09:03 AM
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So I Just got involved in a fantastic internship opportunity doing mathematical oncology. My background is in pure math (just got my B.A.)

My project will involve doing some differential equations to model tumor growth.

I have literally NO background in biology. I'm told (by math types who do this kind of thing) that this isn't really a problem. You learn what you need to make the model.

Aside from re-familiarizing myself with differential equations (it's been awhile) I need to learn some biology, specifically that which is needed for tumor growth. Does anybody have any resources they'd recommend? (Sorry if this is an ill formed question, but such is the case when you are starting from scratch).

I have access to a great library, so am open to book recommendations. But I could also use the stimulation of videos and such as are found online.

Thanks!

-Dave K
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Ryan_m_b
#2
May22-14, 02:30 PM
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I have some friends with just maths backgrounds that are now doing PhDs modelling biological phenomenon. The extent to which they need to expand their biology knowledge is a constant source of conversation, their mathematician coworkers have a variety of opinions ranging from just pick it up to do whatever to learn basic biology as well as what you're working on. From my perspective as a biologist (which may be wrong) I don't see how you could hope to do good work in a biological field without having a decent understanding of biology. I may be a bit bias but I've attended talks by modellers who knew very little and without coworkers to correct them ended up making beautiful models that don't match reality at all. Often because they've made a series of simplifying assumptions that have added up to a model that doesn't take into account heterogeneity, stochastic phenomenon or even just basic things like cellular density (an example of the latter was modelling cells as though they were perfect spheres).

Having said all that I'm not entirely sure where would be appropriate for you to start having not jumped from something else to biology myself. I'd suggest starting from something basic like the central dogma (which roughly explains the relationship of DNA, RNA and protein) and read on to understand DNA regulation, gene expression and cellular bio. Those are all very relevant to cancer. Hopefully others will have more helpful advice but as a start I suggest flicking through this nature introduction to cellular biology:
http://www.nature.com/scitable/ebook...ology-14749010
dkotschessaa
#3
May22-14, 03:34 PM
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P: 604
Quote Quote by Ryan_m_b View Post
I have some friends with just maths backgrounds that are now doing PhDs modelling biological phenomenon. The extent to which they need to expand their biology knowledge is a constant source of conversation, their mathematician coworkers have a variety of opinions ranging from just pick it up to do whatever to learn basic biology as well as what you're working on. From my perspective as a biologist (which may be wrong) I don't see how you could hope to do good work in a biological field without having a decent understanding of biology. I may be a bit bias but I've attended talks by modellers who knew very little and without coworkers to correct them ended up making beautiful models that don't match reality at all. Often because they've made a series of simplifying assumptions that have added up to a model that doesn't take into account heterogeneity, stochastic phenomenon or even just basic things like cellular density (an example of the latter was modelling cells as though they were perfect spheres).
You have a complete right to say all that. Would any decent biologist think otherwise?

I am the first to admit that I'm in over my head. Fortunately this is undergrad level work.

Though I still do have a strong believe that being steeped in mathematics gives you the thinking tools to catch up quickly in some aspects. Though certainly, without being steeped in biology and doing labwork and such, I will not have the same intuitions.



Having said all that I'm not entirely sure where would be appropriate for you to start having not jumped from something else to biology myself. I'd suggest starting from something basic like the central dogma (which roughly explains the relationship of DNA, RNA and protein) and read on to understand DNA regulation, gene expression and cellular bio. Those are all very relevant to cancer. Hopefully others will have more helpful advice but as a start I suggest flicking through this nature introduction to cellular biology:
http://www.nature.com/scitable/ebook...ology-14749010
That's a great resource. Thanks very much.

-Dave K

finnk
#4
May24-14, 04:46 AM
P: 16
Math Graduate needs "Cancer Biology for Dummies"

Maybe, start from here :
http://www.cancer.gov/cancertopics/u...gcancer/cancer

The best book for cancer biology :
The Biology of Cancer by Robert A. Weinberg

This is the bible :
Devita, Hellman, and Rosenberg's Cancer : Principles and Practice of Oncology

Cell biology reference :
Alberts Molecular Biology of The Cell

Recommended Book :
The Emperor of All Maladies by Siddharta Mukherjee

Hope that helps :)
dkotschessaa
#5
May28-14, 06:23 AM
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P: 604
Quote Quote by finnk View Post
Maybe, start from here :
http://www.cancer.gov/cancertopics/u...gcancer/cancer

The best book for cancer biology :
The Biology of Cancer by Robert A. Weinberg

This is the bible :
Devita, Hellman, and Rosenberg's Cancer : Principles and Practice of Oncology

Cell biology reference :
Alberts Molecular Biology of The Cell

Recommended Book :
The Emperor of All Maladies by Siddharta Mukherjee

Hope that helps :)
Thanks. I am looking into the last one already as more "casual" reading.

I also found "New Biology for Engineers and Computer Scientists." I figure "mathematicians" could be added in that title as well. It says "Taking a system approach to expose modern biology, this book presents the fundamental system principles and parameters common to all living species."

I'm not sure that I know the difference between this type of approach and a more traditional one, only that the way it's written seems to work for me.

I'll also look into Weinberg.

Dave K
finnk
#6
May29-14, 11:43 AM
P: 16
I'm not familiar with that book, but skimming through the table of contents, all it covers will be covered by Alberts text already. If that book works for you, then by no mean go for it. Honestly, i think reading some text in biology won't be any use for you / is not a good use of your time. The books i mentioned above (Weinberg for cancer biology, Devita for clinical oncology, Alberts for cell biology) were meant to be references. All your reading should come from primary source literature, depending on what kind of specific model you are working at (tumor growth is too broad, what kind of tumor ? where ?). Try to ask your project supervisor or senior for recommended reading, a relevant paper or perhaps some paper they published in the past. And if you haven't already try working through mathematical modelling text (for ex. Leah Edelstein, Murray, etc).
dkotschessaa
#7
Jun4-14, 01:00 PM
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P: 604
Thanks finnk. Since I last posted I've gotten my stuff together a bit more.

I have the original paper that we are working (a published article that needs a bit of revision), a past paper that provides some bakcground, a book on mathematical modeling in matlab (which is what we are using) and I'm still working on getting a few more cancer biology books and such, as recommended by my mentor.

I was plodding along with the aforementioned biology book. Kind of dull and unproductive. Then I remembered "mind maps" and started to use that approach. I started generally with "tumor growth" and just learned generally how that happens (from some youtube videos), then mapped it to some stuff about modelling tumor growth (in general) from a paper that my mentor had contributed too.

Then I created a more specific one about TNF, which is what this research is about. I learned more in a few hours with this approach than I did in two weeks of screwing around with textbooks!

Now I'm on to looking at the MATLAB code and the Diff EQ models. The hard part is tying together all the knowledge into one big understanding of the research. Still feels overwhelming and I feel a little bit stupid and under qualified, but I'll keep at it.

-Dave K
atyy
#8
Jun17-14, 08:27 PM
Sci Advisor
P: 8,636
Quote Quote by Ryan_m_b View Post
I have some friends with just maths backgrounds that are now doing PhDs modelling biological phenomenon. The extent to which they need to expand their biology knowledge is a constant source of conversation, their mathematician coworkers have a variety of opinions ranging from just pick it up to do whatever to learn basic biology as well as what you're working on. From my perspective as a biologist (which may be wrong) I don't see how you could hope to do good work in a biological field without having a decent understanding of biology. I may be a bit bias but I've attended talks by modellers who knew very little and without coworkers to correct them ended up making beautiful models that don't match reality at all. Often because they've made a series of simplifying assumptions that have added up to a model that doesn't take into account heterogeneity, stochastic phenomenon or even just basic things like cellular density (an example of the latter was modelling cells as though they were perfect spheres).
Quote Quote by dkotschessaa View Post
You have a complete right to say all that. Would any decent biologist think otherwise?

I am the first to admit that I'm in over my head. Fortunately this is undergrad level work.

Though I still do have a strong believe that being steeped in mathematics gives you the thinking tools to catch up quickly in some aspects. Though certainly, without being steeped in biology and doing labwork and such, I will not have the same intuitions.
I think one aspect of modelling is knowing how much detail to leave out, depending on what aspects of the phenomena are important to you. So I don't think modelling cells as perfect spheres is necessarily terrible.

Of course, knowing that cells are not perfect spheres, one doesn't want important predictions of the model to depend on one having modelled cells as perfect spheres. But the idea that a model should be robust against experimental uncertainties (or indicate what important parameters experimentalists need to constrain more tightly) is contained even in classical ideas that a good model should be "well-posed", one condition of which is that a small change in parameters usually leads to a small change in predictions.
gravenewworld
#9
Jun17-14, 11:50 PM
P: 1,408
Textbooks usually have outdated information. Just read highly cited Cancer papers you find in a database. Topics to look up:
"Cancer stem cells"
"Epithelial to mesenchymal transition (EMT)"
"Metastatic cascade"


There should be tons of reviews out there on those important topics. Go through and read them. Underline and look up every term you do not understand until you do. Become familiar with which aspects are most relevant clinically. For example, by the time a patient is even able to see a doctor who can diagnose metastatic cancer, several steps of the metastatic cascade have already occurred. Therefore, research that focuses on parts of the cascade that are most relevant clinically is going to be much more useful than research focused on steps of the metastatic cascade that have very little probability of being targeted in time.
dkotschessaa
#10
Jun19-14, 07:28 AM
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P: 604
I think I've find a good stride. For anyone's future reference, I've done the following:

Starting from scratch:
I watched a very basic video on the biology of cancer: https://www.youtube.com/watch?v=46Xh7OFkkCE
I took notes (actually created a mindmap in Freemind) and any terms that I wanted more background on I looked in various places (biology textbokos, etc)

Although Weinberg's book "The Biology of Cancer" came highly recommended, I wasn't sure that trying to slog through a whole textbook would work in my time frame. Weinberg's book for a general audience "One Renegade Cell: How Cancer Begins" has been *excellent* reading, however. Maybe the other book next summer. :)

Digging in:
Here are the papers I am working with:
"Mathematical Oncology: How Are the Mathematical and Physical Sciences Contributing to the War on Breast Cancer?" Arnaud H. Chauviere, Haralampos Hatzikirou, John S. Lowengrub, Hermann B. Frieboes, Alastair M. Thompson, Vittorio Cristini.

Available at http://link.springer.com/article/10....609-010-0020-6

Mathematical Modeling of Tumor Growth and Treatment
http://europepmc.org/abstract/MED/24283955

Properties of tumor spheroid growth exhibited by simple mathematical models.
http://www.ncbi.nlm.nih.gov/pubmed/23508803


And the most important one for me right now:
Therapeutic Non-Toxic Doses of TNF Induce Significant Regression in TNFR2-p75 Knockdown Lewis Lung Carcinoma Tumor Implants

http://www.plosone.org/article/info%...l.pone.0092373

Basically as gravnewworld said I am looking up anything I do not understand.

It's going pretty well. My biggest job at the moment is catching up on Matlab!

Thanks all,

Dave K


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