Positional Information, in Bits
Event details
| Date | 26.05.2014 |
| Hour | 11:00 |
| Speaker | Prof. Gašper Tkačik, IST Austria, Klosterneuburg (AT) |
| Location | |
| Category | Conferences - Seminars |
BIOENGINEERING SEMINAR
Abstract:
Cells in a developing embryo have no direct way of “measuring” their physical position, but nevertheless need to make cell fate decisions that are appropriate for specific spatial locations. The conceptual framework that can account for this observation dates back to Wolpert, and decades of research have subsequently uncovered the molecular and cellular basis for establishing and reading out the proposed "positional information," encoded in spatial gradients of gene expression. Despite this progress, we have until recently lacked a mathematical counterpart to Wolpert's conceptual framework, with which we could unambiguously define and measure the amount of positional information in any set of developmental gene expression profiles. Here we show how to measure this information, in bits, using the gap genes in the Drosophila embryo as an example. Individual genes carry nearly two bits of information, twice as much as would be expected if the expression patterns consisted only of on/off domains separated by sharp boundaries. Taken together, four gap genes carry enough information to define a cell’s location with an error bar of ~1% along the anterior/posterior axis of the embryo. This precision is nearly enough for each cell to have a unique identity, which is the maximum information the system can use, and is nearly constant along the length of the embryo. We argue that this constancy is a signature of optimality in the transmission of information from primary morphogen inputs to the output of the gap gene network.
Bio:
Dr. Tkačik joined IST Austria in 2011. Previously he was a postdoc with Vijay Balasubramanian and Phil Nelson at University of Pennsylvania, working on the theory of neural coding and specifically exploring population coding and adaptation in the retina. He finished his PhD at Princeton with Bill Bialek and Curt Callan in 2007, studying how biological networks can reliably transmit and process information in the presence of intrinsic noise and corrupted signals. He is broadly interested in uncovering general principles that underlie efficient biological computation.
Abstract:
Cells in a developing embryo have no direct way of “measuring” their physical position, but nevertheless need to make cell fate decisions that are appropriate for specific spatial locations. The conceptual framework that can account for this observation dates back to Wolpert, and decades of research have subsequently uncovered the molecular and cellular basis for establishing and reading out the proposed "positional information," encoded in spatial gradients of gene expression. Despite this progress, we have until recently lacked a mathematical counterpart to Wolpert's conceptual framework, with which we could unambiguously define and measure the amount of positional information in any set of developmental gene expression profiles. Here we show how to measure this information, in bits, using the gap genes in the Drosophila embryo as an example. Individual genes carry nearly two bits of information, twice as much as would be expected if the expression patterns consisted only of on/off domains separated by sharp boundaries. Taken together, four gap genes carry enough information to define a cell’s location with an error bar of ~1% along the anterior/posterior axis of the embryo. This precision is nearly enough for each cell to have a unique identity, which is the maximum information the system can use, and is nearly constant along the length of the embryo. We argue that this constancy is a signature of optimality in the transmission of information from primary morphogen inputs to the output of the gap gene network.
Bio:
Dr. Tkačik joined IST Austria in 2011. Previously he was a postdoc with Vijay Balasubramanian and Phil Nelson at University of Pennsylvania, working on the theory of neural coding and specifically exploring population coding and adaptation in the retina. He finished his PhD at Princeton with Bill Bialek and Curt Callan in 2007, studying how biological networks can reliably transmit and process information in the presence of intrinsic noise and corrupted signals. He is broadly interested in uncovering general principles that underlie efficient biological computation.
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