Presentation Information
[SS20-04]Controlling dynamics of complex biological systems based on network topology
*Atsushi Mochizuki1, Kenji Kobayashi2, Yutaka Satou2, Masato Ishikawa1, Yuhei Yamauchi1, Yusuke Tarumoto1, Kosuke Yusa1, Mototsugu Eiraku1 (1. Institute for Life and Medical Sciences, Kyoto University (Japan), 2. Graduate School of Science, Kyoto University (Japan))
Keywords:
Gene Regulatory Network,Controllability,Observability,Feedback Vertex Set,Cell fate control
By the success of modern biology, we have many examples of large networks that describe regulatory interactions between biomolecules. Dynamics of molecular activities based on such networks are the origin of biological functions. However, we have a limited understanding for the dynamics based on such complex systems. To overcome the problem, we developed Linkage Logic theory by which important aspects of dynamical properties are determined from topology of the regulatory network alone. The theory assures that i) any long-term dynamical behavior of the whole system can be identified/controlled by a subset of nodes in a regulatory network, and that ii) the subset is determined solely from the topology of the network as a feedback vertex set (FVS). We applied the theory to the gene regulatory network for cell-fate specification of ascidian embryo, which includes more than 90 genes. From the analysis, dynamical attractors possibly generated by the network should be identified/controlled by only 6 genes. We verified our prediction by experimentally manipulating activities of these genes using real ascidian embryos. We confirmed that each of all seven tissues could be deterministically induced respectively.
In order to expand the practical power of Linkage Logic, we have also developed a new method for gene regulatory networks called RENGE. RENGE is a method for estimating gene regulatory networks from data where genes are perturbed comprehensively and the resulting changes in gene expression are measured as a time series. This method is characterized by an estimation algorithm that captures the process of the effects of the perturbations propagating stepwise through the network as dynamics. This makes it possible to distinguish between the direct regulation and indirect effects of regulations. This method was applied to human iPS cells, and a gene network containing 103 genes important for maintaining pluripotency was estimated. We are currently working on actual cell fate control by manipulating the determined FVS based on the estimated network. By combining RENGE and Linkage Logic, it will be possible to control various cell types freely.
References:
[1] Mochizuki, A., Fiedler, B. et al. (2013) J. Theor. Biol., 335, 130-146.
[2] Kobayashi., et al. (2018) iScience 4, 281–293
[3] Kobayashi., et al. (2021) Sci. Rep. 11, 4001
[4] Ishikawa., et al. (2023) Commn. Biol. 6, 1290
In order to expand the practical power of Linkage Logic, we have also developed a new method for gene regulatory networks called RENGE. RENGE is a method for estimating gene regulatory networks from data where genes are perturbed comprehensively and the resulting changes in gene expression are measured as a time series. This method is characterized by an estimation algorithm that captures the process of the effects of the perturbations propagating stepwise through the network as dynamics. This makes it possible to distinguish between the direct regulation and indirect effects of regulations. This method was applied to human iPS cells, and a gene network containing 103 genes important for maintaining pluripotency was estimated. We are currently working on actual cell fate control by manipulating the determined FVS based on the estimated network. By combining RENGE and Linkage Logic, it will be possible to control various cell types freely.
References:
[1] Mochizuki, A., Fiedler, B. et al. (2013) J. Theor. Biol., 335, 130-146.
[2] Kobayashi., et al. (2018) iScience 4, 281–293
[3] Kobayashi., et al. (2021) Sci. Rep. 11, 4001
[4] Ishikawa., et al. (2023) Commn. Biol. 6, 1290