About

selected publications

1. 

   Neural circuits for dynamics-based segmentation of time series

   Tesileanu, T., Golkar, S., Nasiri, S., Sengupta, A.M., and Chklovskii, D.B.

   arXiv 2021

   Abs Bib PDF

   The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on data sets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series.

   @article{Tesileanu2021,
     image = {mypub_imgs_seq_seg.png},
     bibtex_show = {true},
     selected = {true},
     archiveprefix = {arXiv},
     arxivid = {2104.11852},
     author = {Tesileanu, Tiberiu and Golkar, Siavash and Nasiri, Samaneh and Sengupta, Anirvan M. and Chklovskii, Dmitri B.},
     doi = {10.1162/neco_a_01476},
     journal = {arXiv},
     eprint = {2104.11852},
     pages = {1--34},
     title = {{Neural circuits for dynamics-based segmentation of time series}},
     keywords = {biowta},
     url = {http://arxiv.org/abs/2104.11852},
     pdf = {https://arxiv.org/pdf/2104.11852.pdf},
     year = {2021}
   }

2. 

   Protein Sectors: Statistical Coupling Analysis versus Conservation

   Tesileanu, T., Colwell, L.J., and Leibler, S.

   PLOS Computational Biology 2015

   Abs Bib PDF

   Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed “sectors”. The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by equence conservation.

   @article{Tesileanu2015,
     image = {mypub_imgs_protein_sectors.png},
     bibtex_show = {true},
     selected = {true},
     author = {Tesileanu, Tiberiu and Colwell, Lucy J. and Leibler, Stanislas},
     doi = {10.1371/journal.pcbi.1004091},
     journal = {PLOS Computational Biology},
     number = {2},
     pages = {e1004091},
     title = {{Protein Sectors: Statistical Coupling Analysis versus Conservation}},
     keywords = {protalign},
     url = {http://dx.plos.org/10.1371/journal.pcbi.1004091},
     pdf = {https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004091&type=printable},
     volume = {11},
     year = {2015}
   }

3. 

   Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay

   Melnikov, A., Murugan, A., Zhang, X., Tesileanu, T., Wang, L. et al.

   Nature Biotechnology Feb 2012

   Abs Bib

   Learning to read and write the transcriptional regulatory code is of central importance to progress in genetic analysis and engineering. Here we describe a massively parallel reporter assay (MPRA) that facilitates the systematic dissection of transcriptional regulatory elements. In MPRA, microarray-synthesized DNA regulatory elements and unique sequence tags are cloned into plasmids to generate a library of reporter constructs. These constructs are transfected into cells and tag expression is assayed by high-throughput sequencing. We apply MPRA to compare ≥27,000 variants of two inducible enhancers in human cells: a synthetic cAMP-regulated enhancer and the virus-inducible interferon-β enhancer. We first show that the resulting data define accurate maps of functional transcription factor binding sites in both enhancers at single-nucleotide resolution. We then use the data to train quantitative sequence-activity models (QSAMs) of the two enhancers. We show that QSAMs from two cellular states can be combined to design enhancer variants that optimize potentially conflicting objectives, such as maximizing induced activity while minimizing basal activity.

   @article{Melnikov2012,
     image = {mypub_imgs_mpra.png},
     bibtex_show = {true},
     selected = {true},
     author = {Melnikov, Alexandre and Murugan, Anand and Zhang, Xiaolan and Tesileanu, Tiberiu and Wang, Li and Rogov, Peter and Feizi, Soheil and Gnirke, Andreas and Callan, Curtis G. and Kinney, Justin B. and Kellis, Manolis and Lander, Eric S. and Mikkelsen, Tarjei S.},
     doi = {10.1038/nbt.2137},
     journal = {Nature Biotechnology},
     month = feb,
     number = {3},
     pages = {271--277},
     pmid = {22371084},
     publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
     shorttitle = {Nat Biotech},
     keywords = {transcription},
     title = {{Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay}},
     url = {https://www.nature.com/articles/nbt.2137},
     volume = {30},
     year = {2012}
   }