Journal covers

During my PhD, 1999-2003, most scientific articles I needed for my research where only available in print. I’d go down to the library and take hours photocopying articles from piles of journals.
I also listened to a lot of CDs and I loved the cover art. I haven’t picked up a CD in a while and I’m pretty sure new CDs are still made, but I don’t know who buys them. The art from new covers goes unnoticed – I haven’t seen one from the last 5 years or more.
Journals still get printed too, but like songs we get the articles online. Grad students don’t spend hours copying in the library, which is great. But some nice covers will go unnoticed.

Covers from the October 15 issue of Cancer Research (collaboration with Richard White's lab) and the December 2015 about issue of Applied and Environmental Microbiology (collaboration with Lars Dietrich's lab and Soren Sorens's lab).

Covers from the October 15 issue of Cancer Research (collaboration with Richard White’s lab) and the December 2015 issue of Applied and Environmental Microbiology (collaboration with Lars Dietrich’s lab and Soren Sorensen’s lab).

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Systems Biology talk at the MSKCC presidential seminars this week

Evolutionary Tradeoffs and the Geometry of Gene Expression Space
Uri Alon, PhD
Senior Scientist, Department of Molecular Cell Biology and Department Physics of Complex Systems
Weizmann Institute of Science
Rehovot, Israel

October 21, 2015 at 4:30 PM
Host: GSK Graduate Students
ZRC Auditorium

This talk is followed by a special seminar “The Emotional Sides of Science: a Guitar Talk”

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Taking statistical pictures of metastasis using zebrafish

Metastasis, the spread of cancer from its primary site to other parts of the body, is when cancer gets really bad. But this process is poorly understood, in large part because it is stochastic. Like the dispersal of plant seeds across a fertile field, metastatic cells could end up in many different places so knowing where metastases will form exactly may seem impossible.
What we need to study physical phenomena that are stochastic is lots of samples and statistics. Studying metastasis across large samples can be very difficult however. Investigating patterns of metastasis in the human body would require compiling dozens of cases while controlling for factors that could influence metastasis in unknown ways like patient age, body-mass-index, exposure to carcinogens, etc. We could use animal models and control for these factors, but common models in cancer research like mice and rats are still quite expensive to run experiments with dozens of samples.

“The zebrafish” (2015) by Silja Heilmann

Enter the glorious zebrafish. The zebrafish is already a powerful model for genetics and development and it is gaining increasing importance in cancer biology. Our lab collaborates with the lab of Richard White in the program for Cancer Biology and Genetics to investigate metastatic spread across dozens of zebrafish. For the past three years Silja Heilmann has been working closely with Rich, Kajan and other members of the While lab to develop protocols and methods for the quantitative analysis of metastasis. The model is a transparent zebrafish called Casper that is great for imaging. In Rich’s lab, they developed a zebrafish melanoma cell line called Zmel1 that expresses GFP. Once injected into adult casper zebrafish, Zmel1 forms primary tumors that later on produce metastasis and we can visualize the process using microscopy.
Silja developed image analysis algorithms that resize and align many pictures of fish together. This procedure allows building a statistical picture of metastatic growth across the whole animal. The detailed picture reveals indeed the strong stochastic nature of metastatic spread, but some patterns start to emerge. Advancements such as these may one day enable a better understanding of metastasis and help in the development of anti-metastasis treatments.

Read the paper:

A quantitative system for studying metastasis using transparent zebrafish
Silja Heilmann, Kajan Ratnakumar, Erin Langdon, Emily Kansler, Isabella Kim, Nathaniel R Campbell, Elizabeth Perry, Amy McMahon, Charles Kaufman, Ellen van Rooijen, William Lee, Christine Iacobuzio-Donahue, Richard Hynes, Leonard Zon, Joao Xavier, and Richard M White. Cancer Research
[PDF]

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Lessons from a frinkandel-shaped cell

Organisms can vary a lot in body size among species, but within species they are very similar

Organisms can vary a lot in body size among species, but within species they are very similar

Organisms of different species have different shapes and sizes and still, within the same species, shape and size are practically constant. For example, elephants can be 250,000 larger in volume than mice, yet when we compare elephants of the same species and controlling for age they shouldn’t  differ by more than 2x (I’m is guessing – I couldn’t find any reference for this). How do organisms within a species maintain relatively constant shapes and sizes when it’s at least physically possible to have wide variations?
This seems a question for developmental biologists, but microbial cell biologists are very interested in their version of the same problem. Bacterial species can vary enormously in cell shape and size. One of the largest, Thiomargarita namibiensis (100–300 µm), can be 1000x longer than one of the smallest known Mycoplasma genitalium (~300 nm). Nonetheless, within a species, their sizes are often constant and, we imply, very well regulated. There was a recent burst of papers where different groups across the US measured with unprecedented precision the distribution of cell lengths in the rod shaped bacteria E. coli, Calaubacter crescentus and Bacilus subtilis. From this collective work a simple mechanism seems to emerge: Rod shaped bacteria decide when to divide by determing that they added a fixed increment to their cell length. This so called “incremental rule” (a.k.a. “adder model” or “constant size extension“) had been proposed earlier but lacked empirical support. It provides a simple explanation for why cell populations maintain a narrow size distribution – I strongly reccomend reading those papers.
We added additional evidence for the incremental model by showing that the pathogen Pseudomonas aeruginosa, our model organism, also follows the incremental rule. Our interest in this problem came by accident when we isolated a mutant with abnormally long cell from our hyperswarming experiment. In our new paper we analyze the length distribution in wild-type Pseudomonas aeruginosa and in this mutant which we call frik, not only because it is a freak but also because it reminds us of the tasty dutch snack frinkandel.

frik cell on the left and the dutch frikandel on the right. Remarkable similarity.

frik cell on the left and the dutch frikandel on the right: A striking resemblance.

The frikandel is a meat based rod of mysterious composition. It’s probably made with left over meat, but it’s hard to tell exactly what its composition is from it’s spicy taste and spongy texture (hmmm…). Similarly, exactly why the frik cell is abnormally shaped is somewhat a mystery to us. We resequenced the genome of the frik mutant and we found two candidate mutations. We were capable of ruling out one of those two using microbial genetics. The second one, however, is really hard to clone and we could not confirm nor refute that it is the cause of the frik phenotype. One explanation is that the mutation is in a gene (PA14_65570) that may be essential which would make it harder to clone.
Still, our paper extends the reach of the elegant incremental model by showing that it also applies to P. aeruginosa, a common cause of opportunistic infections. We also show that the frik cells are more sensitive to some antibiotics, suggesting that affecting cell size regulation could have implications for improved antimicrobial therapy.

Read our paper:
Cell size homeostasis and the incremental rule in a bacterial pathogen
Maxime Deforet*, Dave van Ditmarsch* and João B. Xavier. Biophysical Journal
[PDF]

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When do bacteria decide to cooperate?

Bacteria don’t think since they’re just single celled organisms. But they do make decisions. Take the lac operon, arguably the best studied molecular system that enables an organism to adapt to changing conditions. When bacteria have plenty of a nutrient that they like, glucose, they don’t express the genes to eat up another nutrient that they like less, lactose. When glucose runs out, glucose starvation sends a signal as a series of molecular processes inside the cell that end in the expression of the genes in the lac operon. The enzymes encoded in those genes help the cell break down lactose into glucose and galactose and the cell is happy again.

The lac operon is a cellular decision-making system, but one that is pretty individualistic. When a cell makes a decision to express genes based on whether it lacks a nutrient, the decision affects that cell first and foremost (even though decisions to eat new sugars from the environment can always also affect others that compete for the same food). But bacteria can make decisions that are more social.

Mutants that don't produce surfactants (rhlA- here in green) swarm using the surfactants produced by wilt-type bacteria (in red)

Mutants that don’t produce surfactants (rhlA- here in green) swarm using the surfactants produced by wilt-type bacteria (in red)

In our lab we study how cells make decisions to cooperate with other bacteria. Bacteria have many collective traits like biofilms and swarming, which can only happen when there are many cells in a community. One thing in common for these traits to happen is that many individual bacteria need to come together and start producing substances that accumulate in the extracellular space. To build a biofilm, bacteria must secrete large amounts of polymers and make an extracellular matrix. In order to swarm, bacteria must secrete lots of rhamnolipid surfactants and lubricate the surface of a Petri dish. Each cell must spend resources to produce their individual share of a common good. Without these individual contributions the social trait could happen. Cheaters within the population (like the rhlA- strain in this picture) could take advantage of the public good without themselves contributing.

P. aeruginosa decides to cooperate only when they are in a crowd (quorum sensing) and have enough carbon source due to growth limitation by some other nutrient (iron, in this case)

P. aeruginosa decides to cooperate only when they are in a crowd (quorum sensing) and have enough carbon source due to growth limitation by some other nutrient (iron, in this case)

How can cooperating cells prevent cheating? One way is to invest in cooperation only when there are plenty of resources to do so. We uses quantitative experiments and mathematical modeling to analyze how bacteria do this. We saw that bacteria decide to express genes to make the surfactants needed to swarm when they have more than enough carbon rich nutrients, a process that we call metabolic prudence. However, they also count their neighbors using a process called quorum sensing. This way, each individual bacterium checks if it has enough nutrients and enough neighbors for swarming before cooperating. At any point in this process, if the bacterium gets starved too severely it shuts down cooperation, possibly to dedicate resources in preparing for survival.

Check out our paper, out this week in PLoS Computational Biology.

Integration of Metabolic and Quorum Sensing Signals Governing the Decision to Cooperate in a Bacterial Social Trait
Kerry E. Boyle, Hilary Monaco, Dave van Ditmarsch, Maxime Deforet, Joao B. Xavier

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2015 lab mug

Here’s the 2015 lab MUG designed by Silja Heilmann. Great for warm coffee or tea on a cloudy New York Sunday.

labmug2015

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Shapes in math

Mathematical biology has always been obsessed with shapes, specially because many that look so complicated can actually be created with simple rules. Take the Turing patterns – these are regular shapes that obey the same type of rules but can make spots, stripes, labyrinths or holes by changing its parameters.

patterns

Turing-like patters in a model of biofilm, from Xavier et al (2009) Am Nat

Some years ago I read a paper by ecologists Rietkerk, Dekker, de Ruiter and van de Koppel that clicked for me. They explained that patterns in arid vegetation can have a simple explanation similar to Turing patterns. Plants in arid regions require moister (the growth limiting resource) and  benefit from having other plants close by because neighbors provide shade and reduce water evaporation from the soil. However, too many neighbors means there is less moister to go around. In their words “vegetation patterns are the result of fine-scale positive feedback and coarse-scale negative feedback.” This means that having a few neighbors close by is good, but too many neighbors is bad. They presented a very simple cellular automata model that could reproduce the patterns. The cellular automaton is like a simple iterative game where the elements on a square grid follow a simple set of rules at every turn. Every element in the cellular automaton does this:

  1. count the number of close neighbors and multiply that number by a positive value b, which produces the total benefit of having close neighbors provide shade (B)
  2. count the number of neighbors over a wider range and multiply by a negative value –a, which produces the total cost of having neighbors that compete for water (-C)

In the supporting material for their paper they show a convolution kernel:

convolution

The kernel is an intuitive way to think about the cellular automaton rule. A focal bush located at the center of the kernel (represented by *) “looks” at the space around it and adds the value “b” or “-a” depending on whether that patch of land is occupied by another bush. If the final result is greater or equal to 0 then the focal bush gets to live and play another round. If the value is 0 then the bush dies and that grid element becomes free. The model is explained in detail the supporting material of the paper by Rietkerk et al. But the really striking result is that this very simple model produces a diversity of shapes depending on the value of a and b. The patterns can be made larger by using a larger kernel and also by changing the relative sizes of the “good” and “bad” neighborhoods.

We recently applied a similar idea to explain branching patterns of swarming colonies. Branching is a different process, we think, because it happens as a population spreads in space, but the patterns may have an equally simple explanation. In our case, a colony of Pseudomonas aeruginosa spreads across a petri dish and branches along the way suggesting that having too many neighbors is bad. We did some experiments that show support for these rules. For example, if we put two colonies in the same petri dish they repel each other suggesting the long range negative feedback (see video). Our model and experiments show that like the patterns in arid vegetation the branching in swarming can be caused by a short range positive feedback and a long range negative feedback (see video of model SIMSWARM).

Read the paper
The ecological basis of morphogenesis: branching patterns in swarming colonies of bacteria
Deng P, de Vargas Roditi L, van Ditmarsch D, Xavier JB. New Journal of Physics [Article: open access]

The paper  has been selected to appear in the New Journal of Physics “Highlights of 2014” collection and recommended by Rob Palmer at the Faculty of 1000

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