Tag Archives: not imaging

The Bio-protocol experience

A few months ago, I joined Bio-protocol as an associate editor. The first  protocol I edited is now published, so I thought I’ll write about this experience.


Bio-protocol is an open access, peer-reviewed e-journal which specializes, you guessed it, in publishing life-science protocols. Submission is almost exclusively by formal invitation and it is free of charge (i.e. no submission or publishing fees).

Continue reading

The next evolutionary step

Human have always tried to improve on nature, from domestication of plants & animals through directed evolution in the test tube and GMO and up to Craig Venter’s synthetic bacteria and the expansion of the genetic code.
Today, another step was taken towards creating completely artificial life. Continue reading

protocols.io – Life Sciences Protocol Repository – a kickstarter project

The Protocols.io Kickstarter project looks like a very interesting and useful attempt to create an online, easy-to-use and easy to update protocol repository.

They have some great ideas, so I pledged some money for them.

An excerpt from the Kickstarter page:

“A free, up-to-date, crowdsourced protocol repository for the life sciences.

As scientists, we are constantly expanding the frontiers of research and knowledge. Yet, we are also constantly re-discovering knowledge that others have not had the time to publish and improving existing methods without the ability to share the improvements with the world. The mission of ZappyLab is to change this with the protocols.io repository.

Imagine you spend 3 months instead of a week to do a simple DNA transformation in a new species. After much frustration and many cups of coffee later, you discover that you need to do it at room temperature instead of the 42-degree water bath. This is a tiny but critical detail in a common technique. Unfortunately, there is no effective way to let others working with this species know about your discovery.

We started ZappyLab to create a free, central, up-to-date, crowdsourced protocol repository.

We just launched the beta version of protocols.io. This is where you will be able to share and discover changes, corrections, and optimizations of published methods. The trick is to make the sharing easy and crowdsourced. That’s why we have built a protocol checklist app, inside our mobile Bench Tools suite on iOS and Android.”

Go read the whole story and watch the video at their kickstarter page.

FISEB 2014 – day 2

Due to crappy Wi-Fi at hotel, this entry will be short. I’ll try to expand once I get back home.

Anyway, today was very interesting.

At the “early bird” session, I heard about CyTOF. Essentially, instead of using a few fluorescent markers for FACS sorting of different cell types, they offer conjugating the tagging antibodies with rare heavy metal isotopes. they claim that these are not found in cells, so the background should be zero. They have >30 different isotopes they can use, and the detection is by mass spectrometry – so very accurate and distinct identification.

Next was a session on gene expression. I won’t go into details, particularly since much is unpublished yet, but Tzachi Pilpel’s talk was amazing. Who knew tRNA may have anything to do with cancer research?

As per usual, Orna Amster-Choder talked about RNA localization in bacteria with lovely images and great data.

Jeff Gerst from Weizmann discovered a possible new mechanism of mRNA transport in yeast, using the MS2 system in very neat ways.

The next session, called “oral poster 1”  featured short talks. The most interesting to me were about mRNA methylation and about how the DNA sequence surrounding consensus sequence for DNA binding proteins affects this binding. some nice insights.

The last session I attended was about the effect of tumor microenvironment on tumor progression and treatments. Heard some amazing stories. Hope still exist to cure cancer…

Tomorrow is my lecture. Excitement!

FISEB 2014 meeting -day 1

FISEB meeting happens every three years, and it includes participants from 28 different experimental biology societies in Israel. It is the best meeting to learn about biological-medical research performed in Israel at all fields and doctrines.

4 days, 8-10 parallel sessions, hundreds of lectures, >1000 posters, >2200 participants.

The first day started by a plenary lecture by Aryeh Warshel, Nobel lauret. He is really far from my field, and his lecture was very much confusing to me. But he has nice cartoons 🙂 The bottom line – enzymes are able to catalyze reactions due to electrostatic connections that are maintained stable (unlike in water).

From the afternoon sessions, I chose “signaling pathways & networks”. Relevant to this blog:

Yoav Henis from Tel-Aviv Uni. talked about oligomerization of TGF-beta receptors. he used a method he calls “co-patching”, which is essentially IF with two different antibodies for two receptor subunits. homodimerization will yield single color “patch” whereas heterodimerization will yield an overlap of both colors (co-patch). He then looked at the % of co-patch with different receptor subunits with/without ligand, or with mutants.

Maya Schulinder from Weizmann Institute talked about the contacts between mitochondria and other organelles (ER, vacuole) in yeast. These contacts are important for lipid metabolism. She new about the mito-ER contact but found there must be a second contact (bypass mechanism). She used an interesting screen method to find the bypass mechanism to the mito-ER contact: she expressed one of the contact protein as a GFP fusion. She expected that if the bypass mechanism and the mito-ER contact “share the load” of lipid metabolism, then deletion of the bypass will increase the number of the mito-ER contacts to compensate. Using automation, she imaged 6200 deletion mutants (from the yeast deletion library) each expressing this GFP fusion. As expected, she found 4 candidates which turned out to be very interesting.

Roni Seger from Weizmann showed that targeting the nuclear localization signal of ERK can be a novel cure for certain pathologies, including certain types of cancer.

On the other hand, Maya Zigler from the Hebrew Uni. suggested another new idea to cure cancer – by inducing the surrounding immune cells to destroy the tumor.

Ido Amit from Weizmann as well told us that we may not really know all the different types of cells that exist. What most people do, particularly in immunology, is rely on one or two known “markers” and use FACS or other methods to sort the cells based on these markers. However, some of the markers overlap. and there may be cells for which we do not have any markers and they “disappear” in the crowd of unsorted cells. or, the could be further sub-types we do not know about. So he approached the problem in an unbiased way – he took all the cells in the spleen, and did single cell RNA seq to individual cells from the spleen. Thus, each cell type has several hundred/thousand “markers” based on gene expression profiles. Not only did this method agree with the common FACS sorting markers, but he identified several sub-types unknown before.  Expect his paper this month in Science. His paper just got published in Science.

Finally, Yaron Shav-tal from Bar-Ilan Uni. used the MS2 system to study how perturbing the signaling pathway of serum stimulation affects transcription of beta-actin gene. As per usual – very neat job and interesting results.

Sequencing a science fiction writer

This is an irregular post, remotely related to the blog, but for a good cause.

Jay Lake is an award-winning American author of ten science fiction novels and over 300 short stories. He is also one of more than a million Americans who have colon cancer.

His friends started a fundraiser that will allow whole genome sequencing of his cancer (unfortunately, we are close but still not quite at the 1000$-genome goal) . The results of such sequencing has a chance (slight, but more than zero) to suggests new treatment paths not considered by his doctors.

In return for your donation, a bunch of SF writers and friends are going to perform acts of whimsy. Check them out!

Virtual cell biology

Ever wondered how all the processes inside a living cell work together as one system, in real time?  How they all collaborate with each other, affect each other?

Well, wonder no more.

Researchers from Stanford U. and the Craig Venter institute have created a computerized, simulated model of the entire life cycle of the bacterium Micoplasma genitalium (the bacteria with smallest genome [as far as we know]).

M.g. contains only 525 genes [but how many proto-genes?], and data on its genome, transcriptome, proteome and metabolome have been recently acquired. Karr et al. combined data from >900 publications to define >1900 parameters which were related to 28 modules of sub-cellular processes. Each module was first simulated according to the best fitted model (e.g. metabolism was modeled using flux-balance analysis, whereas RNA and protein degradation were modeled as Poisson processes). The difficult part, according to them, was to combine all 28 modules into one system.

They therefore assumed 1) that on short time scales (<1 sec) each module is independent of the others and 2) by running each module in 1 sec loops, they can use the variables created by all the modules from the previous loop to calculate the variables of the ongoing loop. Looping ends when the cell completes one division.

They ran the simulation 128 times, and then validated their model against databases which were not used to construct the model. They claim that the model predictions were significantly close to experimental data.

Now here comes the interesting part – their predictions. I won’t go over all of them – you can read that in the paper. But I do want to discuss a few unexpected predictions.

The metabolism regulates the cell-cycle

The cell-cycle in their model consists of three stages: initiation, replication and cytokinesis (cell division). While running the simulations, they noticed that there is a large variability between cells in the duration of the first and second stages (~65% & 38%), but very little variability in the duration of the last stage (~4%), or in the overall duration of the cell cycle (~9%).  This was strange. Why was there such a variability of the two stages, and how did this variability disappear when looking on the whole cell-cycle?

With respect to replication initiation, they found some correlation to the initial number of a protein called DnaA that must accumulates to a certain threshold at the replication start site in order to recruit DNA polymerase.  They also found that the stochastic nature of the transcription and translation modules creates variability in the number of DnaA monomers also during the simulation, not just when the simulation starts.

About the replication itself – since this is a deterministic process as they call it, they expected to see a straightforward correlation between replication and cell cycle progression. Instead, their model suggests a two-step replication process: the first step is fast. However, as soon as the availability of free deoxy-ribonucleotide-triphosphates (dNTPs) pool decreases beyond a certain threshold, replication slows to a rate equal to the rate of dNTPs production. Hence, the duration of the replication stage in individual cells is related to the free dNTP content at the start of replication than to the dNTP content at the start of the cell cycle. The availability of dNTPs imposes a sort of balance on the duration of the cell cycle – in cels with long initiation phase, the dNTP concentration will be high once replication begins, and replication duration will be short. But, if initiation starts early, then the pool of dNTP will be depleted quickly, thus slowing the replication phase. Yet, the total time of the cell cycle will not vary a great deal among individual cells.

Discovering new role and getting kinetic data for enzymatic activities

After reviewing the behavior of the wild-type cell, they went on to do simulations of single gene mutants for all 525 genes (a total of >3000 runs!).

For each gene they could determine if the cell remains viable. The simulation predictions were ~80% accurate compared to experimental data.

They then went on to experimentally test several mutants. An interesting case was the gene lpdA. The model found its deletion inviable, which was inconsistent with previous data. They therefore tested the viability of this mutant and found the strain to grow 40% slower than the wild type. They then reasoned that another protein must perform a similar task as LpdA. Indeed, they found that another gene, nox, showed similarity to lpdA in terms of sequence and function. Correcting the model with the added new task of Nox yielded a viable simulated cell.

In several other cases, there was a discrepancy between the growth rate of viable mutants in silico and in vivo.  These mutations were in gene coding certain enzymes. They then played with the kinetic parameters of the enzyme so that the outcome in silico, in terms of cell cycle duration, was similar to the outcome in vivo. Thus, they were able to calculate in silico the kinetic parameters of enzymes.

In each of these cases, the new catalytic values are consistent with experimental data.

To summarize:

Biologists are starting to realize more and more the importance of looking at the big picture, beyond their favorite protein or pathway. Systems biology is a field that is developing very fast.

Having a whole-cell in silico model is a dream of every systems biologist, indeed, of every biologist.

The fact that such a model is imperfect allows us to find discrepancies, and from them – new biological knowledge.

This model is a huge step forward in systems biology, and it is only the beginning.

Who knows, this may be the basis for designing new organisms in the future…

ResearchBlogging.orgJonathan R. Karr1, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival Jr., Nacyra Assad-Garcia, John I. Glass, & Markus W. Covert (2012). A Whole-Cell Computational Model Predicts Phenotype from Genotype Cell, 150 (2), 389-401 DOI: 10.1016/j.cell.2012.05.044