Biophysics of information processing in gene regulation

Project Period: 2016-01-01 – 2018-12-31
Externally Funded
Principal Investigator
Gasper Tkacik
Tkacik Group
When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy by analyzing information processing in biochemical “hardware” using Shannon’s information theory. Gene regulation is then viewed as a transmission channel operating under restrictive constraints set by intracellular noise. While the properties of gene expression noise have been carefully quantified in the last decade, its functional impact remains unclear. To address this, we propose to develop a predictive theory of genetic regulatory circuits, grounded at the interface of biophysics and information theory. The basic intuition is that some regulatory networks—both in terms of their interaction topology as well as the “numbers on the arrows”—will make best use of their limited resources to achieve reliable regulation despite noise in gene expression. These optimal networks can be derived from first principles and compared to data. Such an ab initio prediction approach is clearly different from the traditional modeling approaches in systems biology, where a postulated mathematical model is fitted to a particular dataset. If successful, a real prediction of a regulatory network, which so far has not been achieved for any regulatory system, would show that even complex biological functions can be derived from appropriately formulated fundamental principles, and are thus likely not just an evolutionary historical contingency. Moreover, we would also be provided with a compelling functional answer for why a particular network is observed in nature, thus going beyond a mathematical summary of how a network might work, as provided by model fits. Specifically, we propose the following: First, we will derive and analyze optimal small genetic regulatory networks that maximize information transmission under resource and noise constraints, in a biophysically realistic setup that can be connected to data. We will consider networks with arbitrary interactions (including feedback loops), coupled networks that can collectively respond to spatial input signals, and networks that operate in or out of steady state. Second, we will examine whether optimal regulatory interactions can evolve in known promoter/ enhancer architectures on typical speciation timescales. We will use a thermodynamic model of gene regulation to construct a genotype-phenotype-fitness map and (in the low mutation limit) compute the evolutionary rates for the emergence of regulatory functions, selecting either for the optimal regulatory function or high information transmission directly. Third, we will formulate (and possibly extend) our theory for a particular experimental system whose properties enable us to put the theory to a quantitative test. We will predict the network structure and spatial expression profiles of gap genes in Drosophila melanogaster, and compare them to high-quality quantitative data of our collaborator, Thomas Gregor.
Grant Number
Funding Organisation

12 Publications

2019 | Journal Article | IST-REx-ID: 5945
Optimal decoding of cellular identities in a genetic network
M.D. Petkova, G. Tkacik, W. Bialek, E.F. Wieschaus, T. Gregor, Cell 176 (2019) 844–855.e15.
View | Files available | DOI
2017 | Journal Article | IST-REx-ID: 665
Biased partitioning of the multidrug efflux pump AcrAB TolC underlies long lived phenotypic heterogeneity
T. Bergmiller, A.M. Andersson, K. Tomasek, E. Balleza, D. Kiviet, R. Hauschild, G. Tkacik, C.C. Guet, Science 356 (2017) 311–315.
View | Files available | DOI
2016 | Journal Article | IST-REx-ID: 1270   OA View | Files available | DOI
2017 | Journal Article | IST-REx-ID: 613   OA
Shaping bacterial population behavior through computer interfaced control of individual cells
R.P. Chait, J. Ruess, T. Bergmiller, G. Tkacik, C.C. Guet, Nature Communications 8 (2017).
View | Files available | DOI
2018 | Research Data | IST-REx-ID: 5587   OA
View | Files available | DOI
2017 | Journal Article | IST-REx-ID: 943   OA
Decoding of position in the developing neural tube from antiparallel morphogen gradients
M.P. Zagórski, Y. Tabata, N. Brandenberg, M. Lutolf, G. Tkacik, T. Bollenbach, J. Briscoe, A. Kicheva, Science 356 (2017) 1379–1383.
View | DOI | Download (ext.) | PubMed | Europe PMC
2016 | Journal Article | IST-REx-ID: 1358
Intrinsic limits to gene regulation by global crosstalk
T. Friedlander, R. Prizak, C.C. Guet, N.H. Barton, G. Tkacik, Nature Communications 7 (2016) 12307.
View | Files available | DOI
2017 | Journal Article | IST-REx-ID: 955
Evolution of new regulatory functions on biophysically realistic fitness landscapes
T. Friedlander, R. Prizak, N.H. Barton, G. Tkacik, Nature Communications 8 (2017).
View | Files available | DOI
2016 | Journal Article | IST-REx-ID: 1242   OA
Extending the dynamic range of transcription factor action by translational regulation
T.R. Sokolowski, A. Walczak, W. Bialek, G. Tkacik, Physical Review E Statistical Nonlinear and Soft Matter Physics 93 (2016).
View | DOI | Download (ext.)
2018 | Journal Article | IST-REx-ID: 281   OA
Distributed and dynamic intracellular organization of extracellular information
A. Granados, J. Pietsch, S.A. Cepeda Humerez, I. Farquhar, G. Tkacik, P. Swain, PNAS 115 (2018) 6088–6093.
View | DOI | Download (ext.)
2019 | Thesis | IST-REx-ID: 6071   OA
Coevolution of transcription factors and their binding sites in sequence space
R. Prizak, Coevolution of Transcription Factors and Their Binding Sites in Sequence Space, IST Austria, 2019.
View | Files available | DOI
2018 | Journal Article | IST-REx-ID: 161
Statistical mechanics for metabolic networks during steady state growth
D. De Martino, A.A. Mc, T. Bergmiller, C.C. Guet, G. Tkacik, Nature Communications 9 (2018).
View | Files available | DOI