Krzysztof Puszyński

574 total citations
21 papers, 337 citations indexed

About

Krzysztof Puszyński is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Krzysztof Puszyński has authored 21 papers receiving a total of 337 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 9 papers in Oncology and 7 papers in Cancer Research. Recurrent topics in Krzysztof Puszyński's work include Gene Regulatory Network Analysis (8 papers), Cancer-related Molecular Pathways (6 papers) and NF-κB Signaling Pathways (5 papers). Krzysztof Puszyński is often cited by papers focused on Gene Regulatory Network Analysis (8 papers), Cancer-related Molecular Pathways (6 papers) and NF-κB Signaling Pathways (5 papers). Krzysztof Puszyński collaborates with scholars based in Poland, France and United States. Krzysztof Puszyński's co-authors include Tomasz Lipniacki, Marek Kimmel, Alberto d’Onofrio, Pawel Paszek, Allan R. Brasier, Alberto Gandolfi, Jarosław Śmieja, Andrzej Świerniak, Katarzyna Jonak and Katarzyna Szołtysek and has published in prestigious journals such as PLoS ONE, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Krzysztof Puszyński

20 papers receiving 327 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Krzysztof Puszyński Poland 10 241 119 74 71 50 21 337
S Michelson United States 13 170 0.7× 148 1.2× 145 2.0× 51 0.7× 17 0.3× 29 410
Benedict Anchang United States 7 253 1.0× 122 1.0× 95 1.3× 31 0.4× 55 1.1× 18 378
Dirk Schumacher Germany 8 272 1.1× 242 2.0× 109 1.5× 31 0.4× 24 0.5× 11 492
Christian T. Hellwig Ireland 10 383 1.6× 120 1.0× 60 0.8× 39 0.5× 100 2.0× 11 517
Xiao‐Kang Lun Switzerland 9 324 1.3× 101 0.8× 61 0.8× 18 0.3× 56 1.1× 11 437
Alvaro Köhn‐Luque Norway 9 108 0.4× 45 0.4× 49 0.7× 59 0.8× 8 0.2× 20 248
Tim Hammonds United Kingdom 9 322 1.3× 193 1.6× 67 0.9× 61 0.9× 49 1.0× 12 516
Andreas U. Lindner Ireland 10 248 1.0× 102 0.9× 62 0.8× 27 0.4× 34 0.7× 21 345
Jonathan Sagotsky United States 6 174 0.7× 73 0.6× 35 0.5× 68 1.0× 5 0.1× 9 298
Yutong Sha United States 8 177 0.7× 85 0.7× 43 0.6× 22 0.3× 25 0.5× 20 247

Countries citing papers authored by Krzysztof Puszyński

Since Specialization
Citations

This map shows the geographic impact of Krzysztof Puszyński's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Krzysztof Puszyński with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Krzysztof Puszyński more than expected).

Fields of papers citing papers by Krzysztof Puszyński

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Krzysztof Puszyński. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Krzysztof Puszyński. The network helps show where Krzysztof Puszyński may publish in the future.

Co-authorship network of co-authors of Krzysztof Puszyński

This figure shows the co-authorship network connecting the top 25 collaborators of Krzysztof Puszyński. A scholar is included among the top collaborators of Krzysztof Puszyński based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Krzysztof Puszyński. Krzysztof Puszyński is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Puszyński, Krzysztof, et al.. (2024). Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology. Scientific Reports. 14(1). 29721–29721.
2.
Puszyński, Krzysztof, et al.. (2024). Comprehensive Bioinformatic Investigation of TP53 Dysregulation in Diverse Cancer Landscapes. Genes. 15(5). 577–577. 5 indexed citations
3.
Manfredi, Piero, et al.. (2022). Multiple epidemic waves as the outcome of stochastic SIR epidemics with behavioral responses: a hybrid modeling approach. Nonlinear Dynamics. 111(1). 887–926. 9 indexed citations
4.
Formanowicz, Piotr, et al.. (2022). Petri nets and ODEs as complementary methods for comprehensive analysis on an example of the ATM–p53–NF-$$\kappa$$B signaling pathways. Scientific Reports. 12(1). 1135–1135. 11 indexed citations
5.
Rusin, Marek, et al.. (2022). Activation of the atypical NF-κB pathway induced by ionizing radiation is not affected by the p53 status. Acta Biochimica Polonica. 69(1). 205–210. 5 indexed citations
6.
Śmieja, Jarosław, et al.. (2022). Application of Sensitivity Analysis to Discover Potential Molecular Drug Targets. International Journal of Molecular Sciences. 23(12). 6604–6604. 3 indexed citations
7.
8.
Jaksik, Roman, et al.. (2019). Nucleotide composition bias in high throughput gene expression measurement methods. 43. 1–6. 1 indexed citations
9.
Puszyński, Krzysztof, et al.. (2017). Influence of parameter perturbations on the reachability of therapeutic target in systems with switchings. BioMedical Engineering OnLine. 16(S1). 77–77. 1 indexed citations
10.
Puszyński, Krzysztof, Alberto Gandolfi, & Alberto d’Onofrio. (2016). The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms. Journal of Pharmacokinetics and Pharmacodynamics. 43(4). 395–410. 3 indexed citations
11.
Jonak, Katarzyna, et al.. (2016). A novel mathematical model of ATM/p53/NF- κB pathways points to the importance of the DDR switch-off mechanisms. BMC Systems Biology. 10(1). 75–75. 25 indexed citations
12.
Puszyński, Krzysztof, et al.. (2016). Application of bifurcation theory and siRNA-based control signal to restore the proper response of cancer cells to DNA damage. Journal of Theoretical Biology. 408. 213–221. 10 indexed citations
13.
Świerniak, Andrzej, et al.. (2016). System Engineering Approach to Planning Anticancer Therapies. PubMed. 36(8). 4374–4374. 13 indexed citations
14.
Puszyński, Krzysztof, et al.. (2014). REVIEW PAPER<br>Deterministic models and stochastic simulationsin multiple reaction models in systems biology. BioTechnologia. 92(3). 265–280. 1 indexed citations
15.
Puszyński, Krzysztof, Alberto Gandolfi, & Alberto d’Onofrio. (2014). The Pharmacodynamics of the p53-Mdm2 Targeting Drug Nutlin: The Role of Gene-Switching Noise. PLoS Computational Biology. 10(12). e1003991–e1003991. 30 indexed citations
16.
Puszyński, Krzysztof, Roman Jaksik, & Andrzej Świerniak. (2012). Regulation of p53 by siRNA in radiation treated cells: Simulation studies. International Journal of Applied Mathematics and Computer Science. 22(4). 1011–1018. 9 indexed citations
17.
Puszyński, Krzysztof, et al.. (2012). Sensitivity analysis of deterministic signaling pathways models. Bulletin of the Polish Academy of Sciences Technical Sciences. 60(3). 471–479. 11 indexed citations
18.
Puszyński, Krzysztof, et al.. (2009). Crosstalk between p53 and nuclear factor-κB systems: pro- and anti-apoptotic functions of NF-κB. IET Systems Biology. 3(5). 356–367. 37 indexed citations
19.
Puszyński, Krzysztof, et al.. (2008). Oscillations and bistability in the stochastic model of p53 regulation. Journal of Theoretical Biology. 254(2). 452–465. 85 indexed citations
20.
Lipniacki, Tomasz, Krzysztof Puszyński, Pawel Paszek, Allan R. Brasier, & Marek Kimmel. (2007). Single TNFα trimers mediating NF-κ B activation: stochastic robustness of NF-κ B signaling. BMC Bioinformatics. 8(1). 376–376. 55 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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