Tuesday, December 26, 2006

Gatenby and Smallbone: Glycolysis and tumour invasion. Two papers

Why do cancers have high aerobic glycolysis? R Gatenby, R. Gillies. Nature reviews cancer, Vol. 4, November 2004, pp 891-899.

The role of acidity in solid tumour growth and invasion. K. Smallbone, D. Gavaghan, R. Gatenby and P. Maini. Journal of Theoretical Biology 235 (2005) pp 476-484.

Gatenby and Gillies present a review paper in which they explain that the glycolytic metabolism is a requirement for a tumour to progress into a cancer. Cancer cells tend, at least by the time they become invasive, to have an altered glucose metabolism. This metabolism has drawbacks when compared with the conventional glucose metabolism in at least two senses. First of all the glycolytic metabolism is less efficient since it produces 2 ATP (the cell's energy currency) compared with 38 ATP that result from normal metabolism. Second of all, as a by product of this metabolism lactic acid is produced. Lactic acid increases the pH of the microenvironment of the cell and when it reaches a given threshold results in apoptosis or necrosis. As a consequence of this the switch from conventional to glycolytic metabolism does not happen under normal circumstances but under hypoxia, that is, when there is insufficient access to oxygen. In those circumstances the inefficient glycolytic metabolism, which does not need oxygen, represents a significant advantage. Hypoxia is a normal event in a growing tumour since there will always come a point in which the tumour cells are far from blood vessels carrying the needed oxygen. Periodic moments of hypoxia select for cells with the glycolytic metabolism that have adapted to acid environments by, for instance, resistance to apoptosis or by reducing intracellular acidity by pumping it out. The end result of this selection is that a tumour will have a group of cells that, despite having a less efficient metabolism, are capable of harming other cells and also of degrading the ECM (Extra Cellular Matrix, that hold tissue cells together) so the next thing you know is that your tumour cells are capable of invasion and metastasis.

In the second paper, Smallbone and colleagues introduce a mathematical model to study the possibility that tumour invasion and growth could be the result, not of genetic changes, but of changes in the tumour environment. This is, of course, a mathematical formalisation of the hypothesis presented in the other paper. They decided to take multicellular spheroids and produce an ODE model that describes tumour growth and progression as travelling waves: the one for the increased microenvironmental acidity and the second one with the tumour cells invading normal tissue. This is a clearly a quantitative model that, unfortunately, has not yet been validated by in vitro experiments although it looks to me that its design has been done with care so it would be feasible to do so. The model predicts among other things that avascular tumours will have higher acidity than vascular ones (which makes sense since blood vessels can be used to take part of this acidity out of the microenvironment) and that tumour necrosis could potentially be explained without the need to talk about cell starvation or overcrowding but by the acidification of the environment. They make a good case for antiangiogenic therapies since blood vessels can contribute to a decrease of microenvironment acidity that could be sufficient for tumour cells to survive but not for healthy cells that have a lower threshold of acidity resistance. They also suggest a treatment in which the membrane pumps that transport the acidity from within the cell to the outside environment, would be somehow blocked so glycolytic cells would literally poison themselves to death without changing the microenvironment for the healthy tissue cells.

Wednesday, December 20, 2006

Quote of the week

Maybe this will start a new trend in this blog: quotes. I was recently rereading some old issues of Nature and found this article in the careers section. It is a nice article in which the authors give an alternative view to that of this other article of how long and hard should a PhD student work. According to the original view a researcher should work all the time. According to this other alternative view some scientists have many of the weaknesses commonly associated with humans and thus this approach is likely to be useless.

They cite this anecdote from Ernest Rutherford. It seems that he found a student working late on an evening and asked him if he also worked in the mornings. The student answered that that is what he usually did and Rutherford then asked "But, when do you think?". Some times I think to myself that my best ideas usually come in the most unexpected places and circumstances. Now I think that I never had any good idea while in the office in front of the computer. Since now I am off for Xmas holidays there is a chance I will have time for some good ideas.

Wednesday, December 13, 2006

Evolution and creationism in Europe

Many people in the European academic community have a condescending view on the lack of scientific knowledge of the American population in general and also of their political class. While probably these concerns are fair, it would be useful to redirect part of it towards our own citizens and politicians here in Europe.

The Nature issue of a couple of weeks ago reports about how creationism is on the increase in Europe (at least the public awareness of what used to be a dormant line of 'thought'). They also have an interview with the leader of a European creationist group (who has a PhD in astrophysics and whose photo in the article seems to be taken by one of his enemies). Nature has also recently published the letter of Maciej Giertych, an MEP and scientist, with a PhD in population genetics, of the Polish Academy of Sciences in which he criticises evolution. The publication of this letter in a journal with the reputation of Nature has generated a considerable amount of controversy as can be seen in the letters send to the editor and published in the last issue.

Monday, December 11, 2006

Anderson et al: Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment

A. Anderson, A. Weaver, P. Cummings and V. Quaranta. Tumor morphology and phenotypics evolution driven by selective pressure from the microenvironment. Cell 127, 905-915, December 2006.

This is the paper I mentioned in my previous post. It is not that usual to find a mathematical model in a journal like Cell so I hope that this is part of a growing trend.

The paper investigates how the microenvironments helps to drive cancer evolution. To do so they use a hybrid cellular automata model in which cells live in the discrete lattice and the microenvironment (oxygen concentration, extra cellular matrix macromolecule concentration and matrix degrading enzyme) is modeled using continuous variables. The cells are characterised by a number of parameters that determine their behaviour with respect to proliferation, cell-cell adhesion, oxygen consumption, haptotaxis or production of matrix degradation enzymes. Cells follow a life cycle and only proliferate when they reach a certain age. That age depends on the cell's phenotype. During mitosis a cell might alter its phenotype and change the values of proliferation, adhesion, oxygen consumption, etc.

With heterogeneous microenvironment and cell behaviour you get different patterns of tumour growth, some of them favouring agressive invading phenotypes and some of them favouring the coexistance of all sorts of phenotypes. Having the model they described it is possible to study who different microenvironmental factors determine evolution. The results show that harsh environments (little oxygen) select for aggressive phenotypes whereas in milder environments allow for the coexistance of a much bigger range of phenotypes and that these tumours are unlikely to be invasive.

The model is very interesting and the conclusions seem pretty reasonable: Tough microenvironments lead to aggressive tumours. My intuition tells me that on the other hand, heterogeneous populations are more likely to be able to cope with an external aggression which would imply that a less aggressive but more diverse tumour would not respond well to therapies that target any specific kind of behaviour. The main problem with the paper is that the model is fairly complicated for clinical validation.

Tuesday, December 05, 2006

We are big news!

We are big news. Ok, I am personally not included but it seems that the theorical medicine community starts to be noticed.

I browse a tech-news website, called slashdot, very often. One of the entries today is the following: "Computer simulations of cancer growth". There they talk about research performed by Sandy Anderson (Dundee, part of the Marie Curie Network in which I am involved), Vito Quaranta (Vanderbilt, I talked about him in my post on the Lyon workshop in late September) and colleagues. They have just got a paper published in Cell of which I will talk about it in a later post.

At any rate, it looks impressive than sites such as Slashdot report on mathematical and computational models of cancer research. The audience of Slashdot is reputed to be very competent in matters of IT but I could see that some of them are medically competent too (I mean, enough to convince a computer scientist like myself, not necessarily more than that), even if, as in many news in Slashdot, people tend to concentrate on what they want to say regardless of the news they are supposed to comment on.

Friday, December 01, 2006

Cancer and development

Reading a just outdated issue of The Economist, I find this article in the science section about how HIV treatments could be used to treat cancer.

One of the things many people interested in biology but without a background in biology believe (I hope I am not just describing myself here) is that information goes only in one direction: genes - mRNA - proteins. Actually the opposite is true. Enzymes such as reverse transcriptase can copy can include fragments of RNA into DNA. This is of course a technique used by viruses in order to alter the genetic programme of a cell to produce more copies of the virus. This system is also used to change the genetic programme of a cell during development so if the work of the enzyme is hindered so is development (at least in some crucial steps).

It seems that cancer cells have a lot of reverse transcriptase (this is, unfortunately, not explained in the article) and thus treatments used to prevent viral diseases could be used to hinder tumour growth. In vivo experiments with mice transplanted with human cancer cells show that there is a correlation between tumour growth and the use of HIV treatments that hinder the reverse transcriptase enzymes.

It is one more example of how development and cancer are connected (my take, and I don't claim to be the first one with this insight) is that we would not have cancer if we were not the result of developmental processes.

Tuesday, November 28, 2006

Speakers in Step conference

It has been a while since I came back from the Step conference in Brussels and I guess it is time to say something about some of the speakers I had the chance of listening to. Probably the most interesting ones from my highly subjective point of view were James Bassingthwaighte (University of Washington), Brian Goodwin (former Santa Fe Institute Faculty, now at Schumacher College) and Denis Noble (Oxford).

This Step conference was not meant to be about science per se so the talks were definitely not of a technical nature. James introduced the Physiome project which, as you might know, is about putting together all the current and future knowledge about the human phisiology with
the aim of improving health care. The ideal result would be a giant simulation of the human phisiology that could behave like a real whole organism. Such system would allow physicians and other researchers to test therapies quickly and without nasty side effects
and study 'what if' scenarios.What James thinks we need are:

* Training (No use of sophisticated systems if physicians don't use them)
* Databasing
* Standards (Too many groups out there and no way to compare or integrate their work)
* Modelling archives (I got a nice model, where do I put it for other people to play with?)
* Modelling tools

All in all a nice and light introductory talk. Everything he mentioned is quite reasonable although I am not sure if it is realistic to expect any of these things happening in the short term. People so far seem to be happy happy to come with their own models and not much effort is done to see if the results of one model are consistent with the results of the model of a different group.

Next talk came from Brian Goodwin who, although use to be in the Santa Fe Institute is know a professor of 'holistic science' (which looks quite a scary name for a professorship). The theme of his talk? Computational biology: a clash of cultures. The part of the talk which I found more interesting was when he dealt with the ambiguity of languages. Human languages are ambiguous and the meaning of a sentence gets shaped as we speak. This seems to be a good analogy to understand the language of genes which is also ambiguous (which is nice if you want to evolve it). In his view both human and gene languages have the property that are the best compromise between the effort that the speaker has to make to convey a message and the
effort of the listener. This is an interesting idea although I guess that proving it might be quite complicated (note to my self, should take a look at what has been published about this).

The talk from Denis Noble was also interesting despite the fact that his major point was: I have a new book ("The music of life") go and buy it (which I might do). He made a number of points:

1. There is no gene for function (no objections to that)
2. Transmission of information is not just one way (same here)
3. DNA is not the only transmitter of inheritance (heard that before)
4. Law of relativity in biology: there is no privileged level of causality. Message to Dawkins: the gene is not that important.
5. There is no genetic programme (message to Monod this time).
6. Actually there are no programmes at any level
7. ...and that means not even at the brain level

Thursday, November 23, 2006

Cancer and stem cells

Canadian and Italian scientists have just came with research that adds further strength to the idea that mutations to stem cells are the main driving force driving tumour growth and ultimately cancer. Stem cells are non differentiated cells that can replicate indefinitely. When a stem cell duplicates this can lead to two stems cells or to a stem cells and a differentiated cell. These differentiated cells can perform useful things such as become muscle cells, breast cells, epithelial cells, etc. As opposed to stem cells, these differentiated cells lose the capability of limitless replication. Every time a differentiated cell divides, the telomerase needed at the ends of the chromosomes gets shorter. Eventually there is not enough for replication and the cell undergoes apoptosis. That is one reason why many tissues have a pool of stem cells that keep producing differentiated while needed.

The researchers tried to find out how relevant stem cells are for cancer growth. They show than when performing animal experiments (much more convincing than in vitro), animals with injected colon stem cancer cells are more likely to develop cancer than those in which non-stem cancer cells are used.

It all sounds reasonable to me: one of the capabilities that tumour cells have to acquire for the tumour to become a cancer is limitless replicative potential. If you inject into an animal cells that already have that capability, that should make it easier for the cancer to appear. Also, it is known that some tumour cells, as they mutate, might revert to an undifferentiated state with stem-like behaviour. Therapies that specifically target stem-cell cancer cells should be the next step since stem cells amount to a small proportion of the cells in the body but seem to have such a great potential in cancer initiation.

Monday, November 20, 2006

Evolution on a chip

Via Nature research highlights I found an intersting article in PNAS about how a group of researchers in Princeton are studying evolution in silico...for real!

Normally, when theoretical biologists talk about biology in silico they are thinking of computer models of biology, but this time the in silico referes to silicon chips that have been used to create patched environments, each one representing a different microenvironment (the main difference between the patches being the availability of nutrients). In these patches they placed colonies of E. coli and let them grow. The bacteria were allowed to move from one patch to the next using narrow corridors.

Interestingly but maybe not surprisingly, the bacteria move towards more promising neighbouring patches and some times, adapt, genetically and physiologically to the environment. Asides from some interesting experiments, the guys have been kind enough to produce some mathematical model to study the evolution in silico as well as analysis of what is the evolution of bacterial density in a patch as nutrient availability gets depleted and competition gets tougher.

It is really interesting stuff but it seems that they need to complicate a little bit more the patches in order to get more adaptation to the environment and less motility to the greener grass.

Thursday, November 09, 2006

The Step conference

I have just returned from my trip to Brussels for the Step conference in which a Physiome project was discussed. We had talks from some very nice speakers about which I will write in another post.

The Physiome project (or at least what I understood about it after being exposed to the idea for the very first time during this conference) is a highly ambitious project (and that is probably an understatement) whose aim is to integrate all the current and future knowledge about the human physiology. The idea is thus a multiscale modular framework in which all the models about the different parts of the human physiology could be integrated. Such a model would have a tremendous impact on our understanding of physiology, let alone the potential benefits for pharmaceutical companies. For all of you who have any experience doing modeling of biological processes I guess I don't need to tell you how (let's understate it once again) challenging this could be. In any case I am fine with any (extremly) difficult project as long as the intermediate steps are worth something.

In my opinion, the guys in the Step project should aim at something quite modest such as some system by which modelers can integrate just a few models together so different groups can check the consistency of their models and their assumptions. This process will probably take a long while but eventually most modellers will be used to think of their models not in isolation but as something that has to make sense in the context of all the models being developed elsewhere. There should be some infrastructure so the models can be shared between researchers and some protocols and interfaces between models at different scales or across the same scale (say molecular, cellular or tissue) so there can be integration.

One of the speakers mentioned that the keywords in this project are multiscale and modularity. I suggest taking a look at the field of software engineering in which different groups and companies work in different modules and at different levels of abstraction. The software produced is expected to work with other software modules. Of course the complexity to manage is different in the Physiome project but I still think it would be a good starting point.

Thursday, November 02, 2006

Off to Brussels

I will be away for a few days. I will be attending the Euroconference organised by EuroPhysiome whose aim is to build a virtual physiological human.

The website of the conference is here.

Will report back at the end of next week

Friday, October 27, 2006

brief introduction to cancer research using game theory

I gave yesterday a small and informal presentation on the research done in cancer using game theory. For whoever might be interested, the results are here.

Wednesday, October 25, 2006

Interview with mathematical biologist Luigi Preziosi

I guess it will be irrelevant for those of you that don't speak Spanish but the Spanish news paper El Pais has interviewed the mathematical biologist Luigi Preziosi. Luigi happens to be one of the coordinators of the EU Marie Curie Network in which I am involved.

In the interview he argues that biology is geting more mathematical, explains how mathematicians and life scientists and physicians collaborate, how the mathematical models can help to explain medical phenomena and hint to innovative therapies. He also says that traditional biologists are still necessary (as if that was not obvious: theoretical physicists still need experimentalist to work with so they can validate their theories, it should not be different in the life sciences, there would not be biology without people that know how to perform experiments).

Tuesday, October 24, 2006

Bach et al: An evolutionary-game model of tumour-cell interactions

L.A. Bach, S.M. Bentzen, J. Alsner and F.B. Christiansen. An evolutionary-game model of tumour-cell interactions: possible relevance to gene therapy. European Journal of Cancer 27 (2001) 2116-2120.

Bach et al have taken the work from Tomlinson and Bodmer (which I reviewed a few day ago) in which angiogenesis is studied with the help a Game Theory. In the previous case the game involved two players who could chose to produce angiogenic factors or to do nothing. As long as one of the players is willing to shoulder on the cost of producing the factor, both players get the benefit. As expected the result is a polymorphism in which both types of strategies coexist. I said that this polymorphism is to be expected since if there were only factor producing cells then non cooperating cells will have an advantage (since they get the benefits without paying the costs) whereas if the population is made of non cooperating cells then factor producing cells will have an advantage (as long as the benefit of producing the factor is higher than the cost).

The revision of the model proposed by Bach et al considers the implications of extending the game to three players if the benefits of angiogenesis appear only if two out of the three players cooperate to produce the factor.

The payoff table would look like this:

A+,A+ A+,A- A-, A-
A+ 1-i+j 1-i+j 1-i
A- 1+j 1+j 1

where A+ means factor producing and A- means that is not factor producing. Also i is the cost of producing the factor and j is the benefit.

Bach et al analyse how these cost and benefit parameters affect the equilibria and the existence of polymorphism. For this they use computer simulations and find that when the benefit is three times the cost something interesting happens. In that case the final composition of the tumour population would be a consequence of the composition of the original population. Most likely this finding has limited (if any) consequences in potential therapies since physicians have not got the ability to change the initial composition of phenotypes in a tumour. Still, the (qualitative) results can be used as a guide for gene therapies.

Tumour supressor gene involved in virus protection

Got this from mainstream media but the paper itself has been published in the EMBO journal in September. Researchers at the Madrid based Centre for Oncological Research have found a gene (for those interested in the gene itself: ARF) that is implicated in both tumour supression and protection from viral infection.

I guess this amounts to another cellular mechanism that has been successfully evolved to adopt an extra new function. The results of evolution can be messy but I'm still impressed.

Thursday, October 19, 2006

Darwin online

Heard it on the radio this morning while listening to the BBC: the University of Cambridge has digitised the text and images of thousands of pages from the publications of Charles Darwin. The website can be found here: http://darwin-online.org.uk/.

Included in the collection are works on other members of the Darwin family (such as the almost-as-famous-as-his-grandson Erasmus Darwin) as well as mp3 audio books for those who would like something more intellectual to entertain their daily jog or gym workout.

Wednesday, October 18, 2006

Evolution and cancer news

Every morning I check a couple of sites in order to get an overview of what is going on in the world. Today I found a couple of curious things:

On the more serious (and cancer related) front, A group of researchers at the University of Missouri-Columbia have found a way to find out the spread of skin cancer cells through the blood. The technique relies on the fact that the vibrations produced by a laser into a melanoma cell is different from that of other cells like red blood cells and plasma.

More information can be obtained here (for those with a subscription to the journal of optics letters).
A different kind of research has been noticed by the bigger media (BBC, Telegraph) and it seems to illustrate how not to use the theory of evolution. Researchers at the Darwin centre at the London School of Economics have seen the future and came back to tell us: Mankind will split into two separate species: the clever and beautiful and the dim-witted goblin-like. I have already heard people labelling these groups as the Macs and the PCs.

Sunday, October 15, 2006

Turning on genes again

I confess to read The Economist newspaper more than occasionally and I find the Science and Technology section readable but rigorous.In this week's issue there is an article about a new class of drugs that has recently gained approval in the US.

One of the things I have learned reading this article is about how mutations might occur that could lead to tumour formation. Up to now, as far as I was concerned, a mutation is a mutation is a mutation, that is, something that just happens and, ooops, one more ticket for the lottery of cancer. Well, it seems that a curious way to turn off large parts of the DNA is by increasing the packing density of the DNA so it becomes effectively unreadable. It seems that cancer cells can use this mechanism to avoid expressing genes so they can proliferate faster as well as avoid apoptosis and senescence. Given that the drug that has just been granted regulatory approval works best with leukemia it seems that the mechanisms employed by tumour cells to deactivate certain cell mechanisms varies from cancer to cancer. Still, having cancer cells with the capability of turning off significant parts of the DNA is such an evolutionary advantage that it should not be surprising if equivalent mechanisms are found in other types of cancer.

Saturday, October 14, 2006

Another article on Richard Dawkins

An interview of Richard Dawkins in Salon. It is possible to read the full article if you click on the sponsor link.

Salon has a series of interviews about science and religion and this time they decided to interview Prof. Dawkins who, they claim, is second to the late Stephen Jay Gould in popularising evolutionary biology (I would claim that here in Europe Dawkins is probably better known).

Usual readers of this blog (hi mum!) know that I am a fan of Prof. Dawkins and share some (if not most) of his ideas about evolution and its lack of compatibility with established religions (the interview, and his new book - The god of delusion - deal mostly with the three big monotheistic religions although touches too any faith-based religion...which I guess means any). Reading this wont give you a new insight on cancer. Moreover, if you are a religious person, it is unlikely that you will be transformed into an unapologetic atheist, but it still makes an interesting read IMHO.

For the record, the book of Richard Dawkins is The god of delusion. For an alternative view from another famous scientist (Stephen Jay Gould, probably second to Dawkins popularising evolutionary biology) take a look at Rock of Ages. His view is that science and religion belong to different realms and should not be intermixed.The links are to wikipedia.

Friday, October 13, 2006

Tomlinson and Bodmer: Modelling the consequences of interactions between tumour cells

I.P.M. Tomlinson and W.F. Bodmer. British Journal of Cancer 75 (2) 157-160, 1997.

Again Tomlinson, this time working with Bodmer, taking a couple of simple but interesting examples of the use of game theory for cancer research. This time they work on two different games: angiogenesis and apoptosis. In the first game angiogenic factors might be produced by tumour cells with the result that the cell producing and its neighbours reap the benefits of increased access to nutrients. In the second game cells might produce factors to escape angiogenesis which might or might not benefit the neighbours.

As it is usual in these cases the model assumes a large population of tumour cells, asexual reproduction and a population site that does not need to be constant since the object of the study is the frequency of particular phenotypes, not their absolute number. Further assumptions: the population of tumour cells is genetically diverse and this diversity is distributed homogeneously.

In the first game: angiogenesis, there are two strategies: either to produce or not to produce angiogenic factors. There is a cost associated to producing then and also a payoff. If you are a non producing tumour cell and you interact with a producing tumour cell you get the same benefits but none of the costs of a factor producing tumour cell. The result is that there are equilibria in which both phenotypes coexist as long as the cost of producing angiogenic factors is outweighed by the benefit.

The second game is more sophisticated. In this case we have three different strategies: either we produce factors to help neighbouring cells avoid apoptosis (paracrine factors), or we produce autocrine factors that help us avoid apoptosis or, alternatively, we might save ourselves all that trouble and do nothing. As usual there is a payoff table in which the different parameters represent the costs of producing paracrine and autocrine factors and the benefits they provide to whoever is the target of the factor. Tomlinson and Bodmer use GT to study different types of equilibria.

In this case it is easy to see that the first strategy is not viable since any group of cells doing nothing and reaping the benefits of endocrine factor producing cells would drive them to extintion. On the other hand if the cost of producing autocrine factors is smaller than the benefit of avoiding apoptosis then there will be a selection for cells capable of producing those autocrine factors.

The conclusion of the paper is that a tumour cell population might not adopt a strategy that would help the whole but does not confer any advantage to the individual that brings it (which is a reasonably safe proposition to make to people studying evolution). The question is again if there could be therapies designed to exploit the fact that tumour cells can stop cooperating among each other.

Thursday, October 12, 2006

Article in Nature: Driven to Market

Nature has a reputation for publishing academic articles of high-impact (that is, loads of people read nature so articules in nature are widely read and are more likely to be cited by other people which means that Nature becomes better ranked which means that more people buy it which means...), but also they have some nice articles that are more accessible to non specialists and that are written by freelance writers.

Yesterday I was reading one in last week's (I am always late with my issue) entitled "Driven to Market" by Jonah Lehre. The article is about a relatively new field called neuroeconomics which combines both psychology and economics. That does not seem related to the topic of this blog but what it is interesting (to me) is that one of the assumptions that is prevalent in economics and that the practitioners of neuroeconomics bring to question is that humans act guided by reason in order to maximise their own benefit. This is also one of the main assumptions in Game Theory (which is a common tool in Economics). Ironically one of the criticisms that opponents of evolutionary game theory have is that animals are not rational but it seems that reason is an even weaker predictor of the behaviour of humans. One nice example to illustrate that is the game called ultimatum. In this game one player is given, say, 10 euros and told that it has to share it with someone else in such a way that if any of the players is unhappy with the way the money has been split then no one gets anything. If people were rational the first player will always offer 1 euro to the second one knowing that the second one would take whatever he or she is offered since the alternative is to get nothing at all. It seems though that when this game is played by people, deals that seem to be too unfair are always rejected even if that rejection means that the money is lost.

University rankings

As long as you don't take them seriously it is amusing to take a look at university rankings once in a while. This one comes from the The Times Higher Education Supplement.

1 Harvard University
2 Cambridge University
3 Oxford University
4= Massachusetts Institute of Technology
4= Yale University
6 Stanford University
7 California Institute of Technology
8 University of California, Berkeley
9 Imperial College London
10 Princeton University
11 University of Chicago
12 Columbia University
13 Duke University
14 Beijing University
15 Cornell University
16 Australian National University
17 London School of Economics
18 Ecole Normale Supérieure, Paris
19= National University of Singapore
19= Tokyo University
21 McGill University
22 Melbourne University
23 Johns Hopkins University
24 ETH Zurich
25 University College London
26 Pennsylvania University
27 University of Toronto
28 Tsing Hua University
29= Kyoto University
29= University of Michigan
31 University of California, Los Angeles
32 University of Texas at Austin
33= Edinburgh University
33= University of Hong Kong
35= Carnegie Mellon University
35= Sydney University
37 Ecole Polytechnique
38 Monash University
39 Geneva University
40 Manchester University
41 University of New South Wales
42 Northwestern University
43 New York University
44 University of California, San Diego
45 Queensland University
46= Auckland University
46= King's College London
48= Rochester University
48= Washington University, St Louis
50= University of British Columbia
50= Chinese University of Hong Kong
52 Sciences Po
53 Vanderbilt University
54= Brown University
54= Copenhagen University
56 Emory University
57 Indian Institutes of Technology
58= Heidelberg University
58= Hong Kong University Sci & Technol
60 Case Western Reserve University
61= Dartmouth College
61= Nanyang Technological University
63 Seoul National University
64= Bristol University
64= Ecole Polytech Fédérale de Lausanne
66 Boston University
67 Eindhoven University of Technology
68 Indian Institutes of Management
69 Amsterdam University
70= School of Oriental and African Studies
70= Osaka University
72 Ecole Normale Supérieure, Lyon
73 Warwick University
74 National Autonomous Univ of Mexico
75 Basel University
76 Catholic University of Louvain (French)
77 University of Illinois
78 Trinity College Dublin
79= Otago University
79= University of Wisconsin
81 Glasgow University
82= Macquarie University
82= Technical University Munich
84 Washington University
85 Nottingham University
86 Delft University of Technology
87 Vienna University
88 Pittsburgh University
89 Lausanne University
90= Birmingham University
90= Leiden University
92 Erasmus University Rotterdam
93= Lomonosov Moscow State University
93= Pierre and Marie Curie University
95 Utrecht University
96 Catholic University of Leuven (Flemish)
97 Wageningen University
98 Munich University
99= Queen Mary, University of London
99= Pennsylvania State University
101 University of Southern California
102= Georgetown University
102= Rice University
102= Sheffield University
105= University of Adelaide
105= Humboldt University Berlin
105= Sussex University
108 National Taiwan University
109= St Andrews University
109= Zurich University
111= Maryland University
111= Uppsala University
111= Wake Forest University
111= University of Western Australia
115 University of Twente
116= Fudan University
116= Helsinki University
118 Tokyo Institute of Technology
119 Hebrew University of Jerusalem
120 Keio University
121 Leeds University
122 Lund University
123 University of North Carolina
124= University of Massachusetts Amherst
124= York University
126 Aarhus University
127 Purdue University
128= Kyushu University
128= Nagoya University
130= Tufts University
130= Virginia University
132 Durham University
133= University of Alberta
133= Brussels Free University (Flemish)
133= Hokkaido University
133= Newcastle upon Tyne University
137 Nijmegen University
138 Vienna Technical University
139 Liverpool University
140 Cranfield University
141= University of California, Santa Barbara
141= Cardiff University
141= Ghent University
141= Southampton University
145 Georgia Institute of Technology
146 RMIT University
147= Chalmers University of Technology
147= Tel Aviv University
148 Free University Berlin
150= Korea University
150= Texas A&M University
152 Notre Dame University
153 Bath University
154 City University of Hong Kong
155 McMaster University
156= Curtin University of Technology
156= Göttingen University
158= Technion -- Israel Inst of Technology
158= University of Ulm
158= Waseda University
161= Chulalongkorn University
161= University Louis Pasteur Strasbourg
163 Michigan State University
164 Saint Petersburg State University
165= Brussels Free University (French)
165= China University of Sci & Technol
165= State Univ of New York, Stony Brook
168= George Washington University
168= Tohoku University
170= University of California, Davis
170= University of Tubingen
172= Aachen RWT
172= Maastricht University
172= Royal Institute of Technology
172= Yeshiva University
176 Queen's University
177 Oslo University
178 University of Bern
179 Shanghai Jiao Tong University
180 Nanjing University
181= Kobe University
181= Université de Montréal
183= Jawaharlal Nehru University
183= Free University of Amsterdam
185 University of Kebangsaan Malaysia
186 Innsbruck University
187= Brandeis University
187= Frankfurt University
187= University of Minnesota
190= University of Barcelona
190= Reading University
192= Malaya University
192= Queensland University of Technology
194 Technical University of Denmark
195 Aberdeen University
196 University of Wollongong
197 La Sapienza University, Rome
198= University of California, Irvine
198= Korea Advanced Inst Science & Technol
200 University of Paris-Sorbonne (Paris IV)

Tuesday, October 10, 2006

I.P.M. Tomlinson: Game theory models of interactions between tumour cells

Review of EJC 35-9 (1997) 1495-1500.

This is the first paper I am aware of that uses game theory to study a problem in the field of cancer research. The paper is only from 1997 so it is easy to see that the field is ripe for further development.

The advantage of being the first to use a given tool in any area is that you can chose the problem and come with an simple and elegant study. Tomlinson has used a simple system in which tumour cells can adopt a number of strategies such as producing cytotoxic substances and cytotoxic resistance. The hypothesis is that some tumour cells attempt to gain advantage by actively harming neighbouring cells. This initial hypothesis is studied considering a number of different scenarios in which different phenotypes are combined. Initially there are three types of phenotypes or strategies: cells that can produce cytotoxic substances, cells that can produce factors that protect them from cytotoxic substances and finally cells that do none of this. Tomlinson presents a payoff table in which the interactions between the different phenotypes are presented in a parametrised way so he can hypothesise different values of the cost it represents to produce the toxin or the cost of producing the resistance or the benefit conferred to harm a player by doing so. Once the game is properly defined he goes on to study the potential equilibria by running simulations on a computer and varying the different parameters of the payoff table.

He also studies alternative strategies such as phenotypes that produce both the cytotoxic substance and its resistance and flexible strategies that behave differently according to the phenotype of the player they compete against. The conclusions he obtains are that several phenotypes can coexist simultaneously in a tumour (since there are configurations of parameters of the payoff table that lead to equilibrium), that flexible strategies are better than fixed ones (unless the cost of flexibility is too high and that therapies could be designed that could exploit the fact that tumour cells can harm other tumour cells under some circumstances (maybe promoting competition and not collaboration among them).

Of course the model is very very simple and the conclusions should be taken with some care (there are no spacial considerations, no hints of what the parameters of the payoff table could be, the results are not surprising). Still, this model gives some theoretical backing to these conclusions and suggests some ideas on how to design a therapy which is quite nice.

Friday, October 06, 2006

Recap from Lyon (II)

Philip Maini is one of the most entertaining speakers (entertaining in the good sense, of course) in the European biomathematical community. Prof. Maini is the director of the Centre of Mathematical Biology at Oxford University and gave in Lyon a talk entitled "Modeling aspects of cancerous tumour dynamics".

The modeling aspects he mentions are three different projects:

1) The first project, in which he collaborates with people like Gatenby (Arizona) and Gavaghan (Oxford) studies the acid mediated invasion hypothesis.
According to (my interpretation of) this hypothesis, when tumour cells lack oxygen and start to starve then a mutation might appear that would make some cancer cells switch to what is called glycolitic phenotype. This means that these cells have an alternative metabolism that works without oxygen and that is not as efficient as the regular one. The reason why this alternative phenotype has a chance of success is because the waste produced (galatic acid) can be used to degrade the extra cellular matrix and lead to invasion of other tissue. Gatenby, Gavaghan and Maini came with a model in which tumours contain cells with the glycolitic phenotype. The results is that tumours are not benign and that an possible explanation for the existence of necrotic cores (material generated when cells die disorderly because of starvation) can be the result of too much acidification of the environment, even for acid-resistant glycolitic-type tumour cells.

2) Metabolic changes during carcinogenesis. Also with Gavaghan and Gatenby and referring to research covered by a paper in Nature reviews cancer (vol 4, 891-889, 2004). They study somatic evolution in a system in which tumour cells can be of one of three different types: hyperplastic, glycolitic or acid-resistant. These cells inhabit the space of a 2D lattice in which there is oxygen, glucose and hydrogen that diffuse in a continuous manner. Altering the reach and concentration of these elements leads to different numbers of cells displaying one or the other phenotype.

For me this is a good place in which to see how game theory could be used to study the interactions of different players (cancer cells) using different strategies (the different phenotypes) to maximise their payoff from the environment (O,H,glucose).

3) Together with Benjamin Ribba (Lyon, organiser of the workshop and one guy I am working with as of lately) Maini works on a multiscale model on which to study the differences between the vasculature generated by the normal process of vasculogenesis and the ones generated by tumour cells capable of angiogenesis. One of the conclusions he mentioned: don't trust parameters.

Thursday, October 05, 2006

Mansury, Diggory and Deisboeck: Evolutionary game theory in an agent based brain tumor model: exploring the 'genotype-phernotype' link

Mansury et al. JTB 238 (2006) 146-156.

One of the things I had in mind when I started this blog is that I could use it to force me to write reviews about some of the most relevant papers that I often read for my own 'dirty' purposes. Usual reasons apply: it is good to write about what you read since synthesis helps understanding.

In any case, as you know, one of the topics I am interested on is cancer research using evolutionary game theory and although evolution is not what these people have studied the other important keywords are present in this paper.

Mansury et al have devised a nice spatial (2D lattice) agent based (Cellular Automata style) system in which tumour cells inhabit a space with nutrients. Tumour cells can be found in two varieties: A (proliferative) and B (migratory). Non evolutionary game theory is used to analyse the interactions between cells that have different phenotypes and how those interactions reflect on the payoffs of the individual cells and on the tumour as a whole. The payoffs in this game are slightly more complicated (and according to the authors, more realistic) than those of other games. The payoff of a cells is made of three different factors: communication payoff, proliferation payoff and migration payoff.

For the simulations (since it is quite difficult to come with a nice analytical study) they run CAs with 500x500 lattices in which nutrients are diffused from the centre and the middle. From here they study how changing the payoff table results in different velocity of tumour growth, different tumour surface roughness (useful to analyse the malignancy of a tumour) and the numbers of both tumour populations with time

From my point of view, the most significant shortcoming of an otherwise interesting piece of research (and acknowledged by the authors) is the lack of evolution in the model. With evolution out of the equation the condition under which phenotypes emerge and take over the original population cannot be studied. One of the nice features of game theory is that it can be used to study the equilibrium states of tumour cell populations when those tumours are studied as composed of individual cells (or agents in Mansury's et al model). Since the author's know this I am looking forward their next paper to see how the improved model can be used to study carcinogenesis.

Tuesday, October 03, 2006

Richard Dawkins interview

Nice interview in BBC of Richard Dawkins. Here he talks about his latest book The god delusion. Will be buying it as soon as it comes to Dresden (which is unlikely to be any time soon :().

Find the interview here

Recap from Lyon (I)

As I mentioned in a previous post one of the nice talks in Lyon came from Vito Quaranta, from Vanderbilt University in Nashville, USA.

He is and MD collaborating with researchers in the States and Europe (eg Sandy Anderson from Dundee) to develop models on tumour invasion. That is the defining feature that separates tumours from benign to malign (eg cancer).

In order to know if a cancer is invasive MDs tend to look at the way the tumour grows. A tumour with a smooth margin is unlikely to be invasive whereas one with fingering is likely to be so.

Of course I am interested to know if there are alternative studies that could be used to predict the evolution of the cancer that are not based on how the tumour shape looks like. First because in many cases physicians don't have accurate images of the contour of tumours (or sometimes haven't got enough information about what is the result of tumour growth). Second, and maybe most important, because the current shape of a tumour doesn't say much about the potential evolution of it towards malignancy. Maybe a different measure (based on the phenotypic composition of tumour cells) could help not only to tell if a tumour is malignant or benign but if the chances of becoming invasive are high or not.

In any case his presentation showed some interesting results on how the microenvironment affects the evolution of the cancer. Homogeneous microenvironments, that is, those in which space can be created with the same ease everywhere, lead to smooth contours whereas inhomogeneous ones lead to fingering. Interestingly these inhomogeneous microenvironments tend to lead to tumours with little diversity in terms of phenotype: when it is difficult for a tumour cell to create space only invasive phenotypes tend to survive.

Wednesday, September 27, 2006

Reporting from Lyon

Still in Lyon after attending the cancer modeling workshop mentioned in my previous post.

From a couple of very brief escapades, Lyon seems to be quite a pleasant town but the workshop has been interesting enough that I didn't had a lot of time for tourism. Nice talks from the likes of Philip Maini from Oxford and Vito Quaranta from Nashville and chats with Benjamin Ribba from Lyon have kept me entertained. The word from modelers: multiscale modeling. Lots of researchers producing models studying cancer at all sorts of scales from molecular to tissue and from seconds to years and we still have not got the way to integrate them.

Tomorrow back to Dresden

Friday, September 22, 2006

off to France again

It seems that September is my French month: I am off to an interesting workshop in Lyon. The topic is Cancer Modelling and Therepeutic Innovation. Although I won't be presenting I hope to meet physicians and discuss one of my models.

The Workshop URL is : http://www.spc.univ-lyon1.fr/workshop-modcan

Friday, September 15, 2006

I am off!!

Only until next Wednesday. The Marie Curie Training Network that sponsors my research here in Dresden is organising a meeting of all the scientists involved in the different projects it manages. The meeting is this monday in Paris!!

It will not be my first time in Paris but I am still looking forward spending the weekend there and meeting some friends.

Thursday, September 14, 2006

Review: "From artificial evolution to computational evolution: a research agenda"

A group of people whose work I respect (Dr. Miller was examiner at my PhD viva and I met Prof. Banzhaf in EA conferences) have recently written a paper entitled "From artificial evolution to computational evolution: a research agenda" that was published in the latest Nature Review Genetics (vol 7, page 729): http://www.nature.com/nrg/journal/v7/n9/full/nrg1921.html

Despite the journal in which they decided to publish it, the paper is addressed mainly to computer scientists working in the field of evolutionary computing. Researchers in this field use algorithms inspired by evolution in order to solve problems of optimisation in all sorts of field of engineering. Say you have to find the parameters that optimise a set of equations. If you encode these parameters into a string of number and create a bunch of these strings initialising them with random values you can use selection and crossover to find values that optimise the equation. Since not all the strings will produce the same results in the equation, we can discard the worst performing ones and fill the space they left with variations of the best performing ones. If we iterate this algorithm a number of times, thus producing successive generations of the initial population of strings, we are likely to obtain sets of parameters that, if not optimal, will likely to be fairly close to it.

This neat idea of using evolution in engineering (or even in art! I know a few examples of people that have used evolution inspired algorithms to produce music or paintings) has produced some interesting results but several people have already found that the very simplistic interpretation of evolution that computer scientists and engineers use in their algorithms is no match for the real thing. Real natural evolution (as opposed to artificial one) is both creative and open-ended.

Banzhaf et al identifies some of the shortcomings of traditional evolution-inspired algorithms and proposes a number of improvements framed in the new context of Computational Evolution (CE). This seems to consist, mainly, on adding extra bits of reality in the abstraction of evolution used by engineers and computer scientists in order to provide evolution with some complexity to play with. By doing this, for instance embedding it into analogue electronic circuits, they hope that artificial evolution will be successful were it was not before: solving ill-defined open-ended problems.

While I entirely agree with the idea that the original evolution-inspired algorithms could be significantly improved by enriching the stuff on which evolution works more complex and life-like, I am not sure that what they suggest is so ground breaking as to call the field a different name. Besides, many of the suggestions mentioned have been in use by computer scientists (especially the authors of the article) for some time (for example: more biologically plausible genotype-phenotype mappings, on which people like Peter Bentley or Julian Miller have done very useful work).

In any case I would not like to sound as if I did not like the paper. I did and I think that despite some further objections (such as: why don't they explain why the bits of nature that they decided to pick are the really necessary ones?). Actually I would like to join my voice to theirs and suggest a further use of computational evolution - A more realistic model of evolution can be used not only to do engineering but also to study evolution per se. I would definitely be interested (and in a way that is what I do for a living) in using Computational Evolution to study evolution from a theoretical perspective.

Friday, September 08, 2006

Genes and cancer

I start the week with a post about something really exciting that I read in Science. Unfortunately my institution does not have access to articles in Science published online before they have been printed on paper so I had to be satisfied for the time being with the reports pusblished by conventional media like the Washington Post.

It seems that researchers have screened for and found 189 genes that are altered in colon and breast cancers. Although we are talking about only two types of cancer, breast and colon cancer are two of the most diagnosed cancers in the western hemisphere. It is remarkable that both types of cancer share very few cancer-related genes and that most of the genes discovered to have a role in these cancers have not been known to be so before.

Tumour supressor gene and aging

Read at the NYT: Researchers at the universities of North Carolina, Michigan and Harvard have found that p16 gradually inhibits the proliferation capabilities of stem cells when they reach certain age. The mechanism is useful to prevent the proliferation of cells that, due to their age, have a significantly increased probability of creating tumours.

The paper reporting the research will be published in Nature. One interesting comment by one of the authors is that in his opinion aging is not random but an anticancer mechanism. I find this observation plausible but having an interest in evolution I cannot help thinking that the reason for aging could also be that once an organism has fulfilled its replication duties, its evolutionary-shaped genetic program does not care much for the long term survival of the individual. In other words, evolution does not favour individuals who are good at surviving for ever but that are good at surviving for long enough as to have lots of equally successful offspring.

Thursday, September 07, 2006

Article in high-performance computing magazine

It seems that the research at our group has been noticed by a news site specialised on high performance computing. Oh well, I guess it helps that our group (BIOS) is hosted in a department of high performance computing and that TU Dresden has just hosted a major conference on parallel computing.

For those interested, here is the link: http://www.hoise.com/primeur/06/articles/live/LV-PL-06-06-21.html

Tuesday, September 05, 2006

Scientists find molecule that tricks cancer cells into dying

Taken from The Guardian, 28th August. It's molecular biology but still interesting: Scientists at the University of Illinois at Urbana-Champaign have found the way to restore apoptotic capabilities to tumour cells. It is known that tumour cells tend to have a defective apoptotic mechanism so they do not die when they should (eg. when the DNA repair mechanism is rendered useless).

One way to give back apoptotic capabilities to tumour cells is to provide the cell with a synthetic molecule that reactivates the production of enzymes involved in apoptosis. This is what Paul Hergenrother and fellow researchers seem to have acomplished.

Thursday, August 31, 2006

Nature papers and reviews

For those of you interested in Cancer (and I assume that if you read this blog then that is probably the case), check out Nature's special "New horizon's in cancer". Sounds interesting and hopefully it will be as good as Science's Cancer research special back in May. It looks a little bit too centered in molecular biology but that is what most of the readership of Nature would want.

The website is here: http://www.nature.com/nature/focus/cancerhorizons/index.html

For those of you without access to Nature's subscription-only website, here is a free article in the collection: http://www.nature.com/nature/journal/vaop/ncurrent/full/nature05085.html

*additionally* you might want to take a look at some article that some guys in London and I have worked on. It is not about cancer but about evolution: how evolution can bring about robustness.

The name of the paper is "The evolution of robust homeostasis and stem cell-like behaviour in artificial multicellular organisms" and you can find it here:

Take a look if you have the time and even a mild interest in computational evolution. It is also written in a very readable style (thanks to Buzz).

Friday, August 25, 2006

cancer and evolution

What does cancer have to do with evolution?

Quite a lot: evolution is one of the defining features of cancer. In multicellular organisms cells cooperate in order to achieve a common goal: to preserve the organism. In a tissue containing a tumour the situation changes: mutations bring about a diversity of genotypes in which some cells will have a higher potential to reproduce and survive than others. In effect an ecosystem is created in which different individuals compete for a limited amount of resources, often to the point of destroying the ecosystem, that is, the organism.

This evolutionary nature of cancer has consequences that affect potential treatments. Since any given tumour will contain a number of different tumour cells, a therapy that is successful at eradicating one type of tumour cell might only make it easier for a different type to spread through. For a treatment to be successful its creators should consider how the ecosystem will be altered and what kind of evolutionary dynamics will be favoured.

Understanding the driving forces of cancer evolution is likely to be a necessary step to understand (and hopefully deal with) cancer. For that reason it would be interesting to see how models of what is beginning to be called computational evolution (nice article in Nature review Genetics, vol 7, september 2006, pp729-735) can be used to study evolution in the context of cancer.

Tuesday, August 22, 2006

hi again

This is really the second post. I thought it would be a sensible idea to rename the blog I created yesterday (the cancer game) since its name might be misunderstood. My aim is to study carcinogenesis (the process by which healthy cells are transformed into cancer cells) using a number of mathematical tools such as Cellular Automata and Game Theory, thus the name 'the cancer game'. Of course (and I repeat myself) I am not implying that cancer could be considered a game in the sense of something funny or amusing but in the sense that game theory could be used to study the dynamics of cancer progression.

More to come soon (hopefully not in the front of 'apologies').