Lecture 1: Physics problems in early embryonic development

One of the most beautiful phenomena in nature is the emergence of a fully formed, highly structured organism from a single undifferentiated cell, the fertilized egg. Biologists have shown that in many cases the “blueprint” for the body is laid out with surprising speed and is readable as variations in the expression levels of particular genes. As we try to understand how these molecules interact to form the patterns that we recognize as characteristic of the mature organism, we face a number of physics problems:

How can spatial patterns in the concentration of these molecules scale with the size of the egg, so that organisms of different sizes have similar proportions?

What insures that the spatial patterns are reproducible from one embryo to the next?

Since the concentrations of all the relevant molecules are small, does the random behavior of individual molecules set a limit to the precision with which patterns can be constructed?

Although the phenomena of life are beautiful, one might worry that these systems are just too complicated and messy to yield to the physicists' desire for explanation in terms of powerful general principles. For the past several years, a small group of us have been struggling with these problems in the context of the fruit fly embryo. To our delight, we have been able to banish much of the messiness, and to reveal some remarkably precise and reproducible phenomena. In particular, the first crucial step in the construction of the blueprint really does involve the detection of concentration differences so small that they are close to the physical limits set by the random arrival of individual molecules at their targets. This problem may be so serious that the whole system for constructing the blueprint has to be tuned to maximize how much signal can be transmitted against the inevitable background of noise, and this idea of optimization can be turned into a theoretical principle from which we can actually predict some aspects of how the system works (which carries us into the next lecture).

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To get a good feeling for these problems one needs images, maybe even movies. I will try to have some links ready soon, and will rely on sketches during the lecture.

For me, interest in these problems began with the attempt to understand theoretically what defines the real limits to the precision of signaling in biological systems. The classic work in the field is by Berg and Purcell, who were interested in chemical sensing by bacteria. My colleagues and I have been interested in making their arguments both more rigorous and more general, trying to identify the physical limits that are relevant for the regulation of gene expression.

Physics of chemoreception. HC Berg & EM Purcell, Biohys J 20, 193-219 (1977).

Physical limits to biochemical signaling. W Bialek & S Setayeshgar, Proc Nat’l Acad Sci (USA) 102, 10040-10045 (2005); physics/0301001.

Cooperativity, sensitivity and noise in biochemical signaling. W Bialek & S Setayeshgar, q–bio.MN/0601001 (2006).

Diffusion, dimensionality and noise in transcriptional regulation. G Tkacik & W Bialek, arXiv:0712.1852 [q–bio.MN] (2007).

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The first generation of experiments and analyses were all drawn from Thomas Gregor's thesis:

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\item[]Diffusion and scaling during early embryonic pattern formation. T Gregor, W Bialek, DW Tank, RR de Ruyter van Steveninck, DW Tank \& EF Wieschaus, {\em Proc Nat'l Acad Sci (USA)} {\bf 102,} 18403--18407 (2005).

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%The real experimental leap forward was reported in two papers:

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\item[]Stability and nuclear dynamics of the Bicoid morphogen gradient. T Gregor, EF Wieschaus, AP McGregor, W Bialek \& DW Tank, {\em Cell} {\bf 130,} 141--152 (2007).

\item[]Probing the limits to positional information. T Gregor, DW Tank, EF Wieschaus \& W Bialek, {\em Cell} {\bf 130,} 153--164 (2007).

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In the same way that the initial theoretical work provided motivation for the experiments, the experimental results have sharpened the theoretical questions ...

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\item[]The role of input noise in transcriptional regulation.

G Tka\v{c}ik, T Gregor \& W Bialek, q--bio.MN/0701002 (2007).

\item[]Information flow and optimization in transcriptional regulation. G Tka\v{c}ik, CG Callan Jr \& W Bialek, arXiv:0705.0313 [q--bio.MN] (2007).

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I'm especially excited about the last paper, because it suggests that there may be common principles of optimization in systems as different as the neural coding and embryonic development.