Costas D. Maranas

21.7k total citations · 3 hit papers
239 papers, 13.9k citations indexed

About

Costas D. Maranas is a scholar working on Molecular Biology, Biomedical Engineering and Control and Systems Engineering. According to data from OpenAlex, Costas D. Maranas has authored 239 papers receiving a total of 13.9k indexed citations (citations by other indexed papers that have themselves been cited), including 189 papers in Molecular Biology, 71 papers in Biomedical Engineering and 21 papers in Control and Systems Engineering. Recurrent topics in Costas D. Maranas's work include Microbial Metabolic Engineering and Bioproduction (131 papers), Biofuel production and bioconversion (63 papers) and Gene Regulatory Network Analysis (51 papers). Costas D. Maranas is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (131 papers), Biofuel production and bioconversion (63 papers) and Gene Regulatory Network Analysis (51 papers). Costas D. Maranas collaborates with scholars based in United States, Bulgaria and United Kingdom. Costas D. Maranas's co-authors include Anthony P. Burgard, Priti Pharkya, Christodoulos A. Floudas, Ali R. Zomorrodi, Anshuman Gupta, Patrick F. Suthers, Sridhar Ranganathan, Vinay Kumar, Anupam Chowdhury and Madhukar S. Dasika and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Costas D. Maranas

235 papers receiving 13.6k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization

2003 913 citations Costas D. Maranas et al. profile →
Multiple spillovers from humans and onward transmission of SARS-CoV-2 in white-tailed deer

2022 124 citations Suresh V. Kuchipudi, Meera Surendran Nair et al. Proceedings of the National Academy of Sciences profile →
CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters

2025 22 citations Costas D. Maranas et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Costas D. Maranas United States	69	10.1k	4.1k	1.7k	989	945	239	13.9k
Christodoulos A. Floudas United States	89	4.4k 0.4×	2.7k 0.7×	12.2k 7.2×	681 0.7×	4.9k 5.2×	379	25.9k
William E. Hart United States	30	6.0k 0.6×	358 0.1×	698 0.4×	161 0.2×	3.0k 3.1×	139	14.3k
Gregory Stephanopoulos United States	97	29.5k 2.9×	9.6k 2.3×	1.7k 1.0×	1.5k 1.5×	315 0.3×	440	37.5k
Joshua Knowles United Kingdom	41	3.8k 0.4×	903 0.2×	717 0.4×	79 0.1×	4.6k 4.9×	110	12.7k
Marianthi Ierapetritou United States	60	1.3k 0.1×	1.6k 0.4×	5.1k 3.0×	220 0.2×	1.2k 1.3×	325	11.8k
Krist V. Gernaey Denmark	60	2.9k 0.3×	3.2k 0.8×	2.1k 1.3×	375 0.4×	229 0.2×	465	12.8k
Pródromos Daoutidis United States	50	1.2k 0.1×	1.1k 0.3×	4.1k 2.4×	410 0.4×	426 0.5×	283	7.9k
Julio R. Banga Spain	47	3.6k 0.4×	443 0.1×	1.8k 1.1×	100 0.1×	979 1.0×	214	7.3k
Paul I. Barton United States	50	983 0.1×	1.8k 0.4×	3.8k 2.2×	388 0.4×	1.5k 1.6×	257	8.1k
James C. Liao United States	87	17.0k 1.7×	8.3k 2.0×	424 0.3×	2.6k 2.7×	203 0.2×	277	22.9k

Countries citing papers authored by Costas D. Maranas

Since Specialization

Citations

This map shows the geographic impact of Costas D. Maranas'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 Costas D. Maranas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Costas D. Maranas more than expected).

Fields of papers citing papers by Costas D. Maranas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Costas D. Maranas. 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 Costas D. Maranas. The network helps show where Costas D. Maranas may publish in the future.

Co-authorship network of co-authors of Costas D. Maranas

This figure shows the co-authorship network connecting the top 25 collaborators of Costas D. Maranas. A scholar is included among the top collaborators of Costas D. Maranas 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 Costas D. Maranas. Costas D. Maranas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Tu, Yuming, Himanshu Joshi, Lynnicia Massenburg, et al.. (2024). Dehydrated Biomimetic Membranes with Skinlike Structure and Function. ACS Applied Materials & Interfaces. 16(16). 20865–20877. 6 indexed citations

Maris, Antonius J. A. van, et al.. (2023). A detailed genome-scale metabolic model of Clostridium thermocellum investigates sources of pyrophosphate for driving glycolysis. Metabolic Engineering. 77. 306–322. 9 indexed citations

Chothe, Shubhada K., Santhamani Ramasamy, Abhinay Gontu, et al.. (2023). Little Brown Bats (Myotis lucifugus) Support the Binding of SARS-CoV-2 Spike and Are Likely Susceptible to SARS-CoV-2 Infection. Viruses. 15(5). 1103–1103. 2 indexed citations

Wu, Zong‐Yen, Wan Sun, Yihui Shen, et al.. (2023). Metabolic engineering of low-pH-tolerant non-model yeast, Issatchenkia orientalis, for production of citramalate. Metabolic Engineering Communications. 16. e00220–e00220. 14 indexed citations

Foster, Charles, Satyakam Dash, Saratram Gopalakrishnan, et al.. (2022). Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model. Metabolic Engineering. 69. 286–301. 10 indexed citations

Amiour, Nardjis, et al.. (2021). Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions. Journal of Experimental Botany. 73(1). 275–291. 17 indexed citations

Chowdhury, Ratul, Victoria S. Cavener, Ruth H. Nissly, et al.. (2021). Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2. Proceedings of the National Academy of Sciences. 118(42). 58 indexed citations

Landa, Marine, et al.. (2021). Elucidation of trophic interactions in an unusual single-cell nitrogen-fixing symbiosis using metabolic modeling. PLoS Computational Biology. 17(5). e1008983–e1008983. 12 indexed citations

Wang, Zhiqing, et al.. (2020). Engineering sensitivity and specificity of AraC-based biosensors responsive to triacetic acid lactone and orsellinic acid. Protein Engineering Design and Selection. 33. 12 indexed citations

10.

Foster, Charles, Lin Wang, Hoang V. Dinh, Patrick F. Suthers, & Costas D. Maranas. (2020). Building kinetic models for metabolic engineering. Current Opinion in Biotechnology. 67. 35–41. 38 indexed citations

11.

Hendry, John I., Hoang V. Dinh, Charles Foster, et al.. (2020). Metabolic flux analysis reaching genome wide coverage: lessons learned and future perspectives. Current Opinion in Chemical Engineering. 30. 17–25. 6 indexed citations

12.

Dash, Satyakam, Daniel G. Olson, Siu Hung Joshua Chan, et al.. (2019). Thermodynamic analysis of the pathway for ethanol production from cellobiose in Clostridium thermocellum. Metabolic Engineering. 55. 161–169. 37 indexed citations

13.

Chan, Siu Hung Joshua, Elliot S. Friedman, Gary D. Wu, & Costas D. Maranas. (2019). Predicting the Longitudinally and Radially Varying Gut Microbiota Composition using Multi-Scale Microbial Metabolic Modeling. Processes. 7(7). 394–394. 17 indexed citations

14.

Throckmorton, Kurt, Ratul Chowdhury, Marc G. Chevrette, et al.. (2019). Directed Evolution Reveals the Functional Sequence Space of an Adenylation Domain Specificity Code. ACS Chemical Biology. 14(9). 2044–2054. 19 indexed citations

15.

Samineni, Laxmicharan, Boya Xiong, Ratul Chowdhury, et al.. (2019). 7 Log Virus Removal in a Simple Functionalized Sand Filter. Environmental Science & Technology. 53(21). 12706–12714. 18 indexed citations

16.

Chowdhury, Ratul, Tingwei Ren, Manish Shankla, et al.. (2018). PoreDesigner for tuning solute selectivity in a robust and highly permeable outer membrane pore. Nature Communications. 9(1). 3661–3661. 55 indexed citations

17.

Chan, Siu Hung Joshua, Margaret Simons, & Costas D. Maranas. (2017). SteadyCom: Predicting microbial abundances while ensuring community stability. PLoS Computational Biology. 13(5). e1005539–e1005539. 144 indexed citations

18.

Chan, Siu Hung Joshua, Jingyi Cai, Lin Wang, Margaret Simons, & Costas D. Maranas. (2017). Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models. Bioinformatics. 33(22). 3603–3609. 71 indexed citations

19.

Dash, Satyakam, Chiam Yu Ng, & Costas D. Maranas. (2016). Metabolic modeling of clostridia: current developments and applications. FEMS Microbiology Letters. 363(4). fnw004–fnw004. 40 indexed citations

20.

Soo, Valerie W. C., Michael J. McAnulty, Arti Tripathi, et al.. (2016). Reversing methanogenesis to capture methane for liquid biofuel precursors. Microbial Cell Factories. 15(1). 11–11. 107 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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