Jyoti

Phone: +49 176 756 05496
Mail: jjyoti (at) constructor.university
Position: PhD student

CV

Jyoti has a Master of Science in physics from the University of Delhi, India, focused on the Complex Biological Systems and Networks. In her M.Sc. dissertation, she primarily worked with gene regulatory and metabolic systems.

Research

In her doctoral studies, she is working on investigating the stability of a Microbiome from a Boolean approach. She is also a part of a research project funded by the Federal Office for Radiation Protection (BfS), for analyzing transcriptome and methylation profiles of human cell cultures. Her research intends to answer complex biological questions with the aid of network science and dynamical systems theory.

Recent Publications

5G-exposed human skin cells do not respond with altered gene expression and methylation profiles
Jyoti Jyoti , Isabel Gronau , Eda Cakir , Marc-Thorsten Hütt , Alexander Lerchl , Vivian Meyer
PNAS Nexus, 2025.
Cite

                    @article{10.1093/pnasnexus/pgaf127,
 abstract = {Due to the ever-increasing wirelessly transmitted data, the development of new transmission standards and higher frequencies in the 5G band is required. Despite basic biophysical considerations that argue against health effects, there is public concern about this technology. Because the skin penetration depth at these frequencies is only 1 mm or less, we exposed fibroblasts and keratinocytes to electromagnetic fields up to ten times the permissible limits, for 2 and 48 h in a fully blinded experimental design. Sham-exposed cells served as negative, and UV-exposed cells as positive controls. Differences in gene expression and methylation due to exposure were small and not higher than expected by chance. These data strongly support the assessment that there is no evidence for exposure-induced damage to human skin cells.},
 author = {Jyoti, Jyoti and Gronau, Isabel and Cakir, Eda and Hütt, Marc-Thorsten and Lerchl, Alexander and Meyer, Vivian},
 doi = {10.1093/pnasnexus/pgaf127},
 eprint = {https://academic.oup.com/pnasnexus/article-pdf/4/5/pgaf127/63049291/pgaf127.pdf},
 issn = {2752-6542},
 journal = {PNAS Nexus},
 month = {05},
 number = {5},
 pages = {pgaf127},
 title = {5G-exposed human skin cells do not respond with altered gene expression and methylation profiles},
 url = {https://doi.org/10.1093/pnasnexus/pgaf127},
 volume = {4},
 year = {2025}
}

Evaluating changes in attractor sets under small network perturbations to infer reliable microbial interaction networks from abundance patterns
Jyoti Jyoti , Marc-Thorsten Hütt
Bioinformatics, 2025.
Cite

                    @article{10.1093/bioinformatics/btaf095,
 abstract = {Inferring microbial interaction networks from microbiome data is a core task of computational ecology. An avenue of research to create reliable inference methods is based on a stylized view of microbiome data, starting from the assumption that the presences and absences of microbiomes, rather than the quantitative abundances, are informative about the underlying interaction network. With this starting point, inference algorithms can be based on the notion of attractors (asymptotic states) in Boolean networks. Boolean network framework offers a computationally efficient method to tackle this problem. However, often existing algorithms operating under a Boolean network assumption, fail to provide networks that can reproduce the complete set of initial attractors (abundance patterns). Therefore, there is a need for network inference algorithms capable of reproducing the initial stable states of the system.We study the change of attractors in Boolean threshold dynamics on signed undirected graphs under small changes in network architecture and show, how to leverage these relationships to enhance network inference algorithms. As an illustration of this algorithmic approach, we analyse microbial abundance patterns from stool samples of humans with inflammatory bowel disease (IBD), with colorectal cancer and from healthy individuals to study differences between the interaction networks of the three conditions. The method reveals strong diversity in IBD interaction networks. The networks are first partially deduced by an earlier inference method called ESABO, then we apply the new algorithm developed here, EDAME, to this result to generate a network that comes nearest to satisfying the original attractors.Implementation code is freely available at https://github.com/Jojo6297/edame.git.},
 author = {Jyoti, Jyoti and Hütt, Marc-Thorsten},
 doi = {10.1093/bioinformatics/btaf095},
 eprint = {https://academic.oup.com/bioinformatics/article-pdf/41/4/btaf095/62210140/btaf095.pdf},
 issn = {1367-4811},
 journal = {Bioinformatics},
 month = {03},
 number = {4},
 pages = {btaf095},
 title = {Evaluating changes in attractor sets under small network perturbations to infer reliable microbial interaction networks from abundance patterns},
 url = {https://doi.org/10.1093/bioinformatics/btaf095},
 volume = {41},
 year = {2025}
}