Биоинформатика и математическо моделиране

Биоинформатиката и в по общ план математическото моделиране представляват ключови дейности в изследванията, ориентирани към геномиката и системната биология, и са ключови за всички експериментални отдели в ЦРСББ. Този отдел ще оказва подкрепа на експерименталните отдели в ЦРСББ във всички основни аспекти, свързани с анализа на набори от “омични” данни (включително например данни от секвениране от следващо поколение, например от RNA-seq, ChIP-seq или секвениране на цял геном; данни от профила на първични и вторични метаболити и други).

Публикации:

2025

Arend M., Paulitz E., Hsieh Y.E., Nikoloski Z.  (2025)

• Scaling metabolic model reconstruction up to the pan-genome level: A systematic review and prospective applications to photosynthetic organisms. Metabolic Engineering. 90: 67-77. (IF 6.8)

• Yuan Y., Iannetta A.A., Kim M., Sadecki P.W., Arend M., Tsichla A., Ruiz-Sola M.A., Kepesidis G., Falconet D., Thevenon E., Tardif M., Brugière S., Couté Y., Kleman J.P., Sizova I., Schilling M., Jouhet J., Hegemann P., Li-Beisson Y., Nikoloski Z., Bastien O., Hicks L.M., Petroutsos D. (2025)
  • Phototropin connects blue light perception to starch metabolism in green algae. Nature Communications. 16(1): 2545. (IF 14.7)

2024

• Gupta S., Petrov V., Garg V., Mueller-Roeber B., Fernie A.R., Nikoloski Z., Gechev T. (2024)
  • The genome of Haberlea rhodopensis provides insights into the mechanisms for tolerance to multiple extreme environments. Cellular and Mоlecular Life Sciences. 81: 117. (IF 8)

2023

• Arend M., Yuan Y., Ruiz-Sola M.Á., Omranian N., Nikoloski Z., Petroutsos D. (2023)
  • Widening the landscape of transcriptional regulation of green algal photoprotection. Nature Communications. 14(1): 2687. (IF 17.694)

• Córdoba S.C., Tong H., Burgos A., Zhu F., Alseekh S., Fernie A.R., Nikoloski Z. (2023)
  • Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism. Nature Communications. 4(1): 4897. (IF 17.694)

• Tong H., Laitinen R.A.E., Nikoloski Z. (2023)
  • Predicting plasticity of rosette growth and metabolic fluxes in Arabidopsis thaliana. New Phytologyst. 40(1): 426-438. (IF 10.323)

2022

• Mbebi A.J., Breitler J.C., Bordeaux M., Sulpice R., McHale M., Tong H., Toniutti L., Castillo J.A., Bertrand B., Nikoloski Z. (2022)
  • A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids. Genes Genomes Genetics. 12(9): jkac170. (IF 3.542)

• Tong H., Nankar A.N., Liu J., Todorova V., Ganeva D., Grozeva S., Tringovska I., Pasev Gancho., Radeva-Ivanova V., Gechev T., Kostova D., Nikoloski Z. (2022)
  • Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits. Horticulture Research. Horticulture Research 9: uhac072. (IF 7.3) (Q1)

• Zhu F., Alseekh S., Koper K., Tong H., Nikoloski Z., Naake T., Liu H., Yan J., Brotman Y., Wen W., Maeda H., Cheng Y., Fernie A.R. (2022)
  • Genome-wide association of the metabolic shifts underpinning dark-induced senescence in Arabidopsis. The Plant Cell. 34(1): 557-578. (IF 11.277)

2021

• Aarabi F., Rakpenthai A., Barahimipour R., Gorka M., Alseekh S., Zhang Y., Salem M. A., Brückner F., Omranian N., Watanabe M., Nikoloski Z., Giavalisco P., Tohge T., Graf A., Fernie A.R., Hoefgen R. (2021)
  • Sulfur deficiency-induced genes affect seed protein accumulation and composition under sulfate deprivation. Plant Physiology. 187(4): 2419–2434. (IF 8.340)

• Gechev T., Lyall R., Petrov V., Bartels D. (2021)
  • Systems biology of resurrection plants. Cellular and Molecular Life Sciences. 78: 6365–6394. (IF 9.261)

• Duarte G. T., Pandey P. K., Vaid N., Alseekh S., Fernie A.R., Nikoloski Z., Laitinen. R. (2021)
  • Plasticity of rosette size in response to nitrogen availability is controlled by an RCC1-family protein. Plant cell and environment. 44(10) :3398-3411. (IF 7.228)

• Mbebi AJ, Tong H, Nikoloski Z. (2021)
  • L2,1-norm regularized multivariate regression model with applications to genomic prediction. Bioinformatics. 37(18): 2896-904. (IF 6.937)

• Moreno J. C., Vicente R., Bruno E. R., Gorka M., Kosmacz M., Peralta-Ariza J. S., Zhang Y., Saleh A., Dorothee C., Luzarowski M., Nikoloski Z., Hartman M. D., Figueroa C. M., Iglesias A. A., Fernie A.R., A. Skirycz (2021)
  • Tyr-Asp inhibition of glyceraldehyde 3-phosphate dehydrogenase affects plant redox metabolism. EMBO Journal. 40(15): e106800. (IF 11.598)

• Omranian S., Angeleska A., Nikoloski Z. (2021)
  • PC2P: Parameter-free network-based prediction of protein complexes. Bioinformatics. 37(1): 73–81. (IF 6.937)

• Tong H., Nikoloski Z. (2021)
  • Machine learning approaches for crop improvement: leveraging phenotypic and genotypic big data. Journal of Plant Physiology. 257: 153354. (IF 3.549)

• Tong H., Küken A., Razaghi-Moghadam Z., Nikoloski Z. (2021)
  • Characterization of effects of genetic variants via genome-scale metabolic modelling. Cellular and Molecular Life Sciences. 78: 5123–5138. (IF 9.261)

2020

• Correa S.M., Alseekh S., Atehortúa L., Brotman Y., Ríos-Estepa R., Fernie A.R., Nikoloski Z. (2020)
  • Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways. The Plant Journal. 104(1): 76-95. (IF 6.417)

• Correa S. M., Fernie A.R., Nikoloski Z., Brotman Y. (2020)
  • Towards model-driven characterization and manipulation of plant lipid metabolism. Progress in Lipid Research. 80: 101051. (IF 16.195)

• Lyall R., Gechev T. (2020)
  • Multi-omics insights into the evolution of angiosperm resurrection plants. Annual Plant Reviews Online. 3(1): 77-110.

• Lyall R., Nikoloski Z., Gechev T. (2020)
  • Comparative analysis of ROS network genes in extremophile eukaryotes. International Journal of Molecular Sciences. 21: 9131. (IF 5.923)

• Omidbakhshfard M.A., Sujeeth N., Gupta S., Omranian N., Guinan K.J., Brotman Y., Nikoloski Z., Fernie A.R., Mueller-Roeber B., Gechev T. (2020)
  • A biostimulant obtained from the seaweed Ascophyllum nodosum protects Arabidopsis thaliana from severe oxidative stress. International Journal of Molecular Sciences. 21(2): 1–26. (IF 5.923)

• Tong H., Küken A., Nikoloski Z. (2020)
  • Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth. Nature Communications 11: 2410. (IF 14.919)

• Tong H., Nikoloski Z. (2021)
  • Machine learning approaches for crop improvement: leveraging phenotypic and genotypic big data. Journal of Plant Physiology. 257: 153354. (IF 3.549)

2019

• Pandey P.K., Yu J., Omranian N., Alseekh S., Vaid N, Fernie A.R., Nikoloski Z., Laitinen R.A.E. (2019)
  • Plasticity in metabolism underpins local responses to nitrogen in Arabidopsis thaliana populations. Plant Direct. 3(11): e00186. (IF 1.725)

• Nunes-Nesi A., Alseekh S., de Oliveira Silva F., Omranian N., Lichtenstein G., Mirnezhad M., Gonzalez R., Sabio Y Garcia J., Conte M., Leiss K., Klinkhamer P., Nikoloski Z., Carrari F., Fernie A.R. (2019)
  • Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds. Metabolomics. 15(4): 46. (IF 3.167)