Grausa, K, Mozga, I., Pleiko K., Pentjuss A., (2022) ‘Integrative Gene Expression and Metabolic Analysis Tool IgemRNA’, Biomolecules, 12(4), p. 586. https://doi:10.3390/biom12040586

Petrovs,R., Stalidzans,E. and Pentjuss,A. (2021) IMFLer: A Web Application for Interactive Metabolic Flux Analysis and Visualization. J. Comput. Biol., https://doi.org/10.1089/cmb.2021.0056

Ramata-Stunda,A., Valkovska,V., Borodušķis,M., Livkiša,D., Kaktiņa,E., Silamiķele,B., Borodušķe,A., Pentjušs,A. and Rostoks,N. (2020) Development of metabolic engineering approaches to regulate the content of total phenolics, antiradical activity and organic acids in callus cultures of the highbush blueberry (Vaccinium corymbosum L.). Agron. Res., 18, 1860–1872. https://doi.org/10.15159/AR.20.054

Grausa, K., Komasilovs, V., Brossard, L., Quiniou, N., Marcon, M., Querne, M., Kviesis, A., Bumanis, N., & Zacepins, A. (2020). Usability improvements of the Thermipig model for precision pig farming. Agronomy Research, 18(S2), 1300–1306. https://doi.org/10.15159/AR.20.029

Kalnenieks,U., Balodite,E., Strähler,S., Strazdina,I., Rex,J., Pentjuss,A., Fuchino,K., Bruheim,P., Rutkis,R., Pappas,K.M., et al. (2019) Improvement of Acetaldehyde Production in Zymomonas mobilis by Engineering of Its Aerobic Metabolism. Front. Microbiol., 10. https://doi.org/10.3389/fmicb.2019.02533

Stalidzans,E., Seiman,A., Peebo,K., Komasilovs,V. and Pentjuss,A. (2018) Model-based metabolism design: constraints for kinetic and stoichiometric models. Biochem. Soc. Trans., 10.1042/BST20170263. https://doi.org/10.1042/BST20170263

Bumanis N., Vitols G., Arhipova I., Mozga I. (2017) Mobile ticket lifecycle management: case study of public transport in Latvia. Proceedings of the 16th International Scientific Conference “Engineering for rural development”, Volume 16, pp. 82-87. https://doi.org/10.22616/ERDev2017.16.N015

Rivza, P., Mozga, I., Berzina, L. (2017). Development of a dynamic modelling tool for agricultural production projections in relation to GHG mitigation measures  Vide. Tehnologija. Resursi – Environment, Technology, Resources, 2, pp. 143-146. https://doi.org/10.17770/etr2017vol2.2588

Komasilovs,V., Pentjuss,A., Elsts,A. and Stalidzans,E. (2017) Total enzyme activity constraint and homeostatic constraint impact on the optimization potential of a kinetic model. BioSystems, 162. https://doi.org/10.1016/j.biosystems.2017.09.016

Elsts,A., Pentjuss,A. and Stalidzans,E. (2017) SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments. Bioinformatics, 33. https://doi.org/10.1093/bioinformatics/btx363

Pentjuss,A., Stalidzans,E., Liepins,J., Kokina,A., Martynova,J., Zikmanis,P., Mozga,I., Scherbaka,R., Hartman,H., Poolman,M.G., et al. (2017) Model-based biotechnological potential analysis of Kluyveromyces marxianus central metabolism. J. Ind. Microbiol. Biotechnol., 44(8). https://doi.org/10.1007/s10295-017-1946-8

Stalidzans E., Mozga I., Sulins J., Zikmanis P. (2017) Search for a minimal set of parameters by assessing the total optimisation potential for a dynamic model of a biochemical network. ACM Transactions on Computational Biology and Bioinformatics. Vol.14(4): 978-985.
https://doi.org/10.1109/TCBB.2016.2550451.

Kalnenieks,U., Pentjuss,A., Rutkis,R., Stalidzans,E. and Fell,D.A. (2014) Modeling of Zymomonas mobilis central metabolism for novel metabolic engineering strategies. Front. Microbiol., 5, 42. https://doi.org/10.3389/fmicb.2014.00042

Pentjuss,A. and Kalnenieks,U. (2013) Assessment of Zymomonas mobilis biotechnological potential in ethanol production by flux variability analysis. Biosyst. Inf. Technol., 3, 1–5. https://doi.org/10.11592/bit.140502

Pentjuss,A., Rubenis,O., Bauze,D., Aprupe,L. and Lace,B. (2013) Flux variability analysis approach of autism related metabolism in stoichiometric model of mitochondria. Biosyst. Inf. Technol., 2, 37–42. https://doi.org/10.11592/bit.131102

Pentjuss,A., Odzina,I., Kostromins,A., Fell,D.A., Stalidzans,E. and Kalnenieks,U. (2013) Biotechnological potential of respiring Zymomonas mobilis: A stoichiometric analysis of its central metabolism. J. Biotechnol., 165. https://doi.org/10.1016/j.jbiotec.2013.02.014

Kostromins A., Mozga I., Stalidzans E. (2012) ConvAn: a convergence analyzing tool for optimization of biochemical networks. Biosystems, Volume 108, Issues 1–3, 2012, pp 73-77, ISSN 0303-2647. https://doi.org/10.1016/j.biosystems.2011.12.004

Pentjuss, A. , Odzina, I., Gailums,A. (2012) Multi agent genome-scale metabolic reconstruction modeling software schema. In 11th International Scientific Conference on Engineering for Rural Development. Jelgava, pp. 189–194.

Odzina,I. and Pentjuss,A. (2012) ALGORITHM OF GENOME – SCALE METABOLIC ENGINEERING IMPLEMENTATION. Latvia University of Agriculture, Jelgava, pp. 289–293.

Mozga I., Stalidzans E. (2011) Convergence Dynamics of Biochemical Pathway Steady State Stochastic Global Optimization, 12th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2011 – Proceedings, p. 231-235. https://doi.org/10.1109/CINTI.2011.6108504.

Mozga I., Stalidzans E. (2011) Optimization protocol of biochemical networks for effective collaboration between industry representatives, biologists and modellers. 9th Annual Industrial Simulation Conference “ISC’2011”, p. 91 – 96.

Mozga I., Kostromins A., Stalidzans E. (2011) Forecast of  numerical optimization progress of biochemical networks. 10th International Scientific Conference „Engineering for rural development”, p. 103 – 108.

Odzina,I. and Pentjuss,A. (2011) In silico analysis of steady state mechanisms of metabolic networks in COBRA Toolbox and FBA-SimVis. Res. Rural Dev., 17/2011, 196–201.

Avisans R., Mozga I. (2019) Towards open data: issues of data disclosure in Latvia. 14th International scientific conference “Students on their way to science”: collection of abstracts, p 32. ISSN 2255-9566.

Gailis A., Mozga I. (2019) Detecting and blocking of unwanted domain name system records to prevent tracking and improve security on local computer networks. 14th International scientific conference “Students on their way to science”: collection of abstracts, p 35. ISSN 2255-9566.

Augustine J., Mozga I. (2018) Advanced e-learning system. 13th International scientific conference “Students on their way to science”: collection of abstracts, p 55. ISSN 2255-9566.

Ikarts I., Mozga I. (2018) Traditional weather forecasting comparison and assessment with real weather conditions. 13th International scientific conference “Students on their way to science”: collection of abstracts, p 60. ISSN 2255-9566.

Brūns M., Mozga I. (2018)  Development of tools for cellular data network simulation. 13th International scientific conference “Students on their way to science”: collection of abstracts, p 57. ISSN 2255-9566.

Dominic S.C., Mozga I. (2018) Utilising mobile technology for learning vet anatomy. 13th International scientific conference “Students on their way to science”: collection of abstracts, p 58. ISSN 2255-9566.

Rivza P., Berzina L., Mozga I., Lauva D. (2015) Long-term forecasting of agricultural indicators and GHG emissions in Latvia. Nordic Association of Agricultural Scientists Riga : NJF Latvia, 2015. p.281-286

Mozga I, Stalidzans  E. (2014) Reduction of Combinatorial Space of Adjustable Kinetic Parameters of Biochemical Network Models in Optimisation Task. Baltic J. Modern Computing; Vol. 2(3):150–159

Mozga I. (2013) Determinition of best set of adjustable parameters with full search and limited search method. Biosystems and Information Technology,
2: 11-14, doi: http://dx.doi.org/10.11592/bit.130503

Mozga I., Stalidzans E. (2011) Convergence Dynamics of Biochemical Models To The Global Optimum, 3rd IEEE International Conference on e-Health and Bioengineering, EHB 2011 – Proceedings, p. 227-230.

Mozga I., Grunde-Zeiferts U., Sudars S., Stalidzans E. (2007) Life Cycles and Competition in Modelling of Artificial and Biological Control System. The European Simulation and Modelling Conference, p. 533-536.

Mozga I., Stalidzāns E. (2007) Application of dynamic models of glycolysis developing control system. 6th International Scientific Conference „Engineering for Rural Development”. p. 119 – 123.