Postcard from Bielefeld: modeling selection in cancer evolution

I am currently in a small  city in Germany: Bielefeld, as a guest of the Center for Interdisciplinary Research (ZiF in German) of the University of Bielefeld. During this time I will be working with other ZiF fellows on a couple of projects which I hope to discuss some other time. This work will be within the framework of a ZiF cooperation group called Multiscale Modeling of Tumor Initiation, Growth and Progression.

To kick start this,  the organizers of this cooperation group have also organized a small workshop on mathematical modelling of cancer evolution. This being exactly the topic of my research for the last decade meant I was really excited to participate. The image at the top of this post shows many (but not all) of the participants.

Any interdisciplinary meeting will include people from at least two disciplines with the goal of the meeting being to establish some sort of bridge between them. In this case I could argue that the disciplines were mathematicians working on less applied topics  vs those working on more applied ones (or biologists as one of them referred to us).

But where I stand, the more interesting gap to bridge was between those interested on the genetic aspects of cancer evolution and those (me and Indiana’s James Glazier) which focused instead on the role of selection. If you ask me (and by reading these lines you have implicitly done so) both genetic variation and selection are the two pillars of evolution and should be considered equally important. Nonetheless there seems to be a lot more work done in trying to understand genetic mutations and phylogenetic trees than in trying to see how the microenvironment and interactions between different cells determines which phenotypes will succeed and which ones will not. More importantly, selection is a key aspect of evolution that we need to understand if we want to have any success when applying treatments to anything from cancer to bacteria to viruses. Why are they (apparently) vastly more people working on genetic aspects rather than selection?


I think that this difference between workflows might partly explain why. There is a wealth of biological data, much of which is genetic in nature, which requires a comparatively modest degree of collaboration with experimentalists if you are a modeler. The process is described in the previous figure: use a mathematical model to find out what the data tells you and then generate hypotheses out of it.

Working on selection requires a different approach as there’s not much data available, especially data that could be easily slotted in a mechanistic model of cancer evolution. Here the mathematical model, built after much consultation with domain experts (that is, cancer biologists) is built to explore the biology and yield hypotheses, which, since we involved our experimental partners from the start, will be also explored in vivo or in vitro. This route is harder but also relatively unexplored, want to join?



Again: come join us!

A couple of months ago we got news that the NCI has awarded us a U01 grant to continue our work defining the bone ecosystem and how prostate cancer can come in and disrupt it. Of course this is something that cannot be done in a hurry so we are in this for the long-haul. We also need help. We have Conor Lynch and his lab as the willing partners for this work but we also need you.

And who are you, you might be (weirdly) asking yourself ? A lot of what we need was described in this post. Shall I start with a PhD in physics/maths/CS? the need to be able to think mathematically and (hopefully) be able to develop in a programming language like C++/Java/Python or others of the sort?  Those are a must but  not the most important quality to work in a place like ours.

Basically we want you to be a detective of cancer biology, able to work with other people, happy to discuss your ideas with either mathematicians, biologists and oncologists. Gregarious but also independent you want to try new ways to think about cancer treatment

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That this sound like you? Then email me and also look for job #20125 at

New publication: using integrated computational modeling to improve treatment of metastatic prostate cancer

Quite the title for the post, no? We just had a paper accepted in Nature Scientific Reports entitled Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer and we thought we would share the news with you. This paper is part of our ongoing work with the Lynch lab at Moffitt to better understand the evolutionary dynamics of prostate cancer metastases in the bone. Continuing on our previous integrated computational platform [blog, description, paper] we decided to investigate a novel treatment which is not typically used in the clinic: the inhibition of the key signaling molecule TGF-Beta. If you know anything about prostate cancer research you will find TGF-Beta familiar. It has been studied to death and not much of clinical use has ever been found. The issue is that although we know a lot about it, cancer biologists have not found a model to integrate all those findings. We decided to use a computational agent-based model to integrate a lot of what we know of the biology of TGF-Beta in bone metastatic prostate cancer.

We took our previous model and worked carefully to make sure all the cell types responded to TGF-Beta to the best of the cancer biology community’s knowledge. We then went back and forth between this computational model and a mouse model in order to make sure that we could recapitulate TGF-Beta inhibition in the context of both: normal bone and cancerous bone. As you can see in the figure, one advantage of the computational model is that resolution of information about each cell type in any point of the tumor at any stage of the progression. This information can be contrasted with the relatively scarce experimental one to asses whether there are disagreements that need to be resolved.

Another advantage of the computational model is that, even if it is relatively complex like this one, it can test hypothesis at a much faster rate, for longer periods of time (and in a much cheaper and humane fashion) than mouse models. This allowed us to explore all kinds of schemes for the application of the TGF-Beta inhibitor. Once something promising is found we can then test those schemes experimentally to see how they fare with the mouse model.

What we found is that pre-application of the inhibitor is a much better option than application once the metastasis has established itself in the bone. What this could mean to a potential patient using a TGF-Beta inhibitor (which as said, is not a clinical option at the moment) is that we could give these treatments after the primary tumor has been identified in the prostate but before metastases have been found. It is important to understand though that this is the result of basic research and that this conclusion will have to remain hypothetical while we find how move our approach from a pre-clinical model to a clinical one. More importantly, what we have shown is that the integration of various sources of experimental data into a purposely made mechanistic mathematical model with the right amount of complexity (not too simple, not too complex) allows us to identify and explore novel treatments in ways (and at speeds) that would not be possible otherwise.

Miles for Moffitt 2016

As every year since I arrived to Tampa, I will be running Miles for Moffitt, the charity race that raises fund for cancer research at Moffitt. I am not in great shape, having injured my right calf 10 days ago, but that is clearly not the point and I will run slowly if that is what it takes. So I hope to see some of you this coming Saturday (14th May) and, if you cannot come but want to help cancer research, here’s the fundraising page for our team IMOffitt as well as my personal one.

Templeton invades the World Science Festival (again)

This will not be a problem with the Pint of Science US events (

Why Evolution Is True

Every year the World Science Festival, organized by physicist Brian Greene and CEO Tracy Day, gets a dollop of cash from Templeton (the sponsors are here), and every year it has a few “Big Ideas” Symposia directly sponsored by Templeton. Most of the ones for this year (program here) look fairly tame, but then there’s this one, with the graphic shown below. The indented material is taken from the Science Festival Announcment.



DATE: Thursday, June 2, 2016
TIME: 8:00 PM-9:30 PM
VENUE: NYU Skirball Center for the Performing Arts
PARTICIPANTS: Brian Greene, Leon Wieseltier, and others

As long ago as the early 19th century, the poet Keats bemoaned the washing away of the world’s beauty and mystery in the wake of natural philosophy’s reductionist insights—its tendency to unweave a rainbow.  Two centuries later, the tentacles of science…

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Counting cancer cells with computer vision for time-lapse microscopy — Theory, Evolution, and Games Group

Here is another blog post by Artem on some joint work with Jacob Scott and Andriy Marusyk. Hear hear!

Some people characterize TheEGG as a computer science blog. And although (theoretical) computer science almost always informs my thought, I feel like it has been a while since I have directly dealt with the programming aspects of computer science here. Today, I want to remedy that. In the process, I will share some Python code […]

via Counting cancer cells with computer vision for time-lapse microscopy — Theory, Evolution, and Games Group