Quantitative biology: from genes, cells to networks

Zhen Xie

PDF(281 KB)
PDF(281 KB)
Quant. Biol. ›› 2014, Vol. 2 ›› Issue (4) : 151-156. DOI: 10.1007/s40484-014-0038-2
MEETING REPORT
MEETING REPORT

Quantitative biology: from genes, cells to networks

Author information +
History +

Cite this article

Download citation ▾
Zhen Xie. Quantitative biology: from genes, cells to networks. Quant. Biol., 2014, 2(4): 151‒156 https://doi.org/10.1007/s40484-014-0038-2

References

[1]
Hammar, P., Walldén, M., Fange, D., Persson, F., Baltekin, O., Ullman, G., Leroy, P. and Elf, J. (2014) Direct measurement of transcription factor dissociation excludes a simple operator occupancy model for gene regulation. Nat. Genet., 46, 405–408
CrossRef Pubmed Google scholar
[2]
Stormo, G. D. (2013) Modeling the specificity of protein-DNA interactions. Quant. Biol., 1, 115–130
CrossRef Pubmed Google scholar
[3]
Stormo, G.D., Zuo, Z. and Chang, Y.K. (2014). Spec-seq: determining protein-DNA-binding specificity by sequencing. Brief. Funct. Genomics, doi: 10.1093/bfgp/elu043
[4]
Zuo, Z. and Stormo, G. D. (2014) High-resolution specificity from DNA sequencing highlights alternative modes of Lac repressor binding. Genetics, 198, 1329–1343
CrossRef Pubmed Google scholar
[5]
Hao, Y., Zhang, Z. J., Erickson, D. W., Huang, M., Huang, Y., Li, J., Hwa, T. and Shi, H. (2011) Quantifying the sequence-function relation in gene silencing by bacterial small RNAs. Proc. Natl. Acad. Sci. USA, 108, 12473–12478
CrossRef Pubmed Google scholar
[6]
Bu, P., Chen, K. Y., Chen, J. H., Wang, L., Walters, J., Shin, Y. J., Goerger, J. P., Sun, J., Witherspoon, M., Rakhilin, N., et al. (2013) A microRNA miR-34a-regulated bimodal switch targets Notch in colon cancer stem cells. Cell Stem Cell, 12, 602–615
CrossRef Pubmed Google scholar
[7]
Bi, S., Yu, D., Si, G., Luo, C., Li, T., Ouyang, Q., Jakovljevic, V., Sourjik, V., Tu, Y. and Lai, L. (2013) Discovery of novel chemoeffectors and rational design of Escherichia coli chemoreceptor specificity. Proc. Natl. Acad. Sci. USA, 110, 16814–16819
CrossRef Pubmed Google scholar
[8]
Kiviet, D. J., Nghe, P., Walker, N., Boulineau, S., Sunderlikova, V. and Tans, S. J. (2014) Stochasticity of metabolism and growth at the single-cell level. Nature, 514, 376–379
CrossRef Pubmed Google scholar
[9]
Lee, T. J., Yao, G., Bennett, D. C., Nevins, J. R. and You, L. (2010) Stochastic E2F activation and reconciliation of phenomenological cell-cycle models. PLOS Biol., e1000488
CrossRef Pubmed Google scholar
[10]
Lan, G., Sartori, P., Neumann, S., Sourjik, V. and Tu, Y. (2012) The energy-speed-accuracy tradeoff in sensory adaptation. Nat. Phys., 8, 422–428
CrossRef Pubmed Google scholar
[11]
Lan, G., and Tu, Y. (2013). The cost of sensitive response and accurate adaptation in networks with an incoherent type-1 feed-forward loop. J. R. SOC. Interface, doi:10.1098/rsif.2013.0489
[12]
Dufour, Y. S., Fu, X., Hernandez-Nunez, L. and Emonet, T. (2014) Limits of feedback control in bacterial chemotaxis. PLOS Comput. Biol., 10, e1003694
CrossRef Pubmed Google scholar
[13]
Monds, R. D., Lee, T. K., Colavin, A., Ursell, T., Quan, S., Cooper, T. F. and Huang, K. C. (2014) Systematic perturbation of cytoskeletal function reveals a linear scaling relationship between cell geometry and fitness. Cell Reports, 9, 1528–1537
CrossRef Pubmed Google scholar
[14]
Frankel, N.W., Pontius, W., Dufour, Y.S., Long, J., Hernandez-Nunez, L., and Emonet, T. (2014) Adaptability of non-genetic diversity in bacterial chemotaxis. eLife, 3, e03526
[15]
Neher, R.A., Russell, C.A., and Shraiman, B.I. (2014). Predicting evolution from the shape of genealogical trees. eLife,3, e03568
[16]
Liu, C., Fu, X. and Huang, J. D. (2013) Synthetic biology: a new approach to study biological pattern formation. Quant. Biol., 1, 246–252
CrossRef Google scholar
[17]
Soroldoni, D., Jörg, D. J., Morelli, L. G., Richmond, D. L., Schindelin, J., Jülicher, F. and Oates, A. C. (2014) A Doppler effect in embryonic pattern formation. Science, 345, 222–225
CrossRef Pubmed Google scholar
[18]
Heidari, N., Phanstiel, D. H., He, C., Grubert, F., Jahanbani, F., Kasowski, M., Zhang, M. Q. and Snyder, M. P. (2014) Genome-wide map of regulatory interactions in the human genome. Genome Res., 24, 1905–1917
CrossRef Pubmed Google scholar
[19]
Chen, W., Liu, Y., Zhu, S., Green, C. D., Wei, G. and Han, J. D. (2014) Improved nucleosome-positioning algorithm iNPS for accurate nucleosome positioning from sequencing data. Nat. Commun., 5, 4909
CrossRef Pubmed Google scholar
[20]
Zhang, W., Liu, Y., Sun, N., Wang, D., Boyd-Kirkup, J., Dou, X. and Han, J. D. (2013) Integrating genomic, epigenomic, and transcriptomic features reveals modular signatures underlying poor prognosis in ovarian cancer. Cell Reports, 4, 542–553
CrossRef Pubmed Google scholar
[21]
Liu, R., Aihara, K. and Chen, L. (2013) Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes. Quant. Biol., 1, 105–114
CrossRef Google scholar
[22]
Bialek, W., Cavagna, A., Giardina, I., Mora, T., Pohl, O., Silvestri, E., Viale, M. and Walczak, A. M. (2014) Social interactions dominate speed control in poising natural flocks near criticality. Proc. Natl. Acad. Sci. USA, 111, 7212–7217
CrossRef Pubmed Google scholar
[23]
Shu, J., Wu, C., Wu, Y., Li, Z., Shao, S., Zhao, W., Tang, X., Yang, H., Shen, L., Zuo, X., et al. (2013) Induction of pluripotency in mouse somatic cells with lineage specifiers. Cell, 153, 963–975
CrossRef Pubmed Google scholar
[24]
Li, Y., Jiang,<?Pub Caret?> Y., Chen, H., Liao, W., Li, Z., Weiss, R. and Xie, Z. (2015) Modular construction of mammalian gene circuits using TALE transcriptional repressors. Nat. Chem. Biol., doi: 10.1038/nchembio.1736
Pubmed

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(281 KB)

Accesses

Citations

Detail

Sections
Recommended

/