on the cellular division plan
Like the human life cycle, which often includes birth, raising offspring and finally death, each bacterium has its own life cycle. The cell cycle of a bacterium begins at the moment of its birth from the mother cell, continues during the growth phase and the phase of reproduction of the genetic material - and ends with the division of the cell into two. The two bacteria born will go through the same life cycle and so on until the end of generations. Unlike humans, bacteria do not decide to become parents, and the scientific community has been trying to understand for a long time what determines when division will occur. In recent years, technology has been developed that makes it possible to follow the progress of the life cycle of a single bacterial cell and all its descendants over many generations. The problem was that the experiments with this method gave rise to conflicting models, which identified different factors as determining the date of the division. BNew research, the scientists of the Weizmann Institute of Science reveal what the most likely distribution model is using a statistical method that makes it possible to identify a cause for a given result even without deciphering the biological mechanisms involved.
Researchers who followed the division of a single bacterial cell in recent years noticed that all its descendants add a constant volume to their size from the stage of the reproduction of the genetic material until the moment of division. They concluded from this that during the replication phase, processes begin that dictate to the cell that it must divide. But in the meantime, it was discovered that even from the moment of birth until the time of division, the cells add a constant volume to themselves. This observation led to the development of a contradictory model, according to which a process that begins at birth determines the time of division; This approach holds that a control protein begins to accumulate in cells from their birth, and when it reaches a threshold level they divide. To bridge the gap, an intermediate model was also proposed, according to which cell division depends on signals that originate both at birth and at the stage of replication of the genetic material.
But how is it even possible that conflicting models developed based all on the results of systematic scientific experiments? Scientists often find in experiments a close connection between two phenomena (correlation or correlation, in the scientific language), even though they do not really affect each other. One of the most quoted phrases in the history of science is said about this - correlation does not imply causation (in English it sounds better: correlation does not imply causation). Therefore, in order to try to decide between the conflicting models, it is necessary to find out whether the DNA replication processes are indeed the cause of the division, or perhaps they are just masking the real cause - protein accumulation that begins at birth. Usually the way to differentiate between correlation and causation is through deciphering and understanding the biological mechanisms involved. But there is also another way.
To decide between the different models for distribution, Prof. Ariel Amir from the Department of Physics of Complex Systems at the Institute and a team of scientists from around the world using a statistical method called "conditional independence tests", which was developed by the Jewish-American Turing Prize winning scientist Yehuda Pearl. The scientists ran the tests on information collected, in collaboration with the University of Tennessee, from growing hundreds of E.coli bacteria at different rates - some of the bacterial cells were given the conditions to grow and divide quickly, while others were grown under conditions that dictated slower growth and division. The information included the timing of different stages in the cell life cycle as well as the size of the cells in each stage.
With the "conditional independence tests" method, the influence of the suspect variable is neutralized by masking the real factor. To this end, the research group, led by research student Prathita Kar from Harvard University, looked each time at a group of bacterial cells whose size at the time of DNA replication was similar, but their size at birth was different. If the model according to which cell division depends only on DNA replication is indeed true, then cells that were the same size at the time of replication will divide at a similar timing - regardless of their size at birth. If the model is wrong and it is precisely a protein that begins to accumulate at birth that determines when the cell will divide, cells born with different sizes will also divide at different times and there will be a correlation between their size at birth and their size at the time of division.
The scientists discovered that surprisingly the growth rate affects the division factor. When bacterial cells were grown at a slow rate, only DNA replication processes determined when the cell would divide. However, when cells were grown at a fast rate, the situation was more complex and it was discovered that both processes that begin at birth and DNA replication processes together determine when the cell will divide. The methods used by the scientists also allowed them to reveal at what stage the division date is finally determined: as soon as the division ring begins to tighten in the center of the cell, its fate to divide into two is sealed.
It is known that the use of conditional independence tests is done in order to disprove accepted but wrong concepts in the scientific community. In this case, the method led the researchers to disprove the currently accepted concept, according to which the time of DNA replication in each generation of bacteria depends only on the time when DNA replication began in the previous generation. They revealed that processes that take place in the mother cell, after the start of replication, may affect the future time when the daughter cells will replicate the DNA themselves.
"The use of statistical methods to confirm a causal relationship allows us to better understand the growth and division processes of bacterial cells," says Prof. Amir. "Conditional independence tests have been known for years in research fields such as epidemiology, economics and more. Now, following our publication, we are seeing more and more teams starting to use these assays in the study of the cell cycle as well. I believe that the ability to characterize the process of growth and reproduction of a variety of disease-causing agents will outline the way for the development of more effective antibiotic drugs in the future."
Dr. Sriram Thiruvadi Krishnan, Prof. Jana Manik and Prof. Jan Manik from the University of Tennessee in Knoxville, USA also participated in the study.
Cliffs, bicycles and intracellular search processes
Prof. Ariel Amir aims to decipher and describe life processes through physics and mathematical models. Since joining the Department of Physics of Complex Systems at the institute, in September 2022, Amir has focused on the research of the life cycle in bacteria and on understanding various biophysical processes that occur inside cells, including protein expression and intracellular search processes. He does this using models of causal inference and statistical physics, on which he has also published a book. His lab also studies microbial evolution using probabilistic models.
Amir did his master's and doctoral theses in the field of theoretical physics, on the subject of the electron glass, under the guidance of Prof. Yuval Org and the late Prof. Yosef Amri from the Institute. Amir went to a post-doctorate at Harvard and was drawn to research in the field of biophysics, first under the guidance of Prof. David Nelson and later in an independent laboratory under his management. In his spare time, Amir climbs cliffs, runs and rides a bicycle. He is married to Lindy and has three daughters.
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