000156541 001__ 156541
000156541 005__ 20251017144555.0
000156541 0247_ $$2doi$$a10.1016/j.csbj.2025.03.040
000156541 0248_ $$2sideral$$a143844
000156541 037__ $$aART-2025-143844
000156541 041__ $$aeng
000156541 100__ $$aLázaro, Jorge$$uUniversidad de Zaragoza
000156541 245__ $$aMulti-scale design and optimization of antibody production via flexible nets
000156541 260__ $$c2025
000156541 5060_ $$aAccess copy available to the general public$$fUnrestricted
000156541 5203_ $$aAntibodies are therapeutic proteins with many applications in medicine, such as treating viral infections, different types of cancer, and common diseases such as psoriasis and multiple sclerosis. Chinese Hamster Ovary (CHO) cells are the most widely used cells for antibody production due to their well-established use and favorable features. However, the current design of antibody production systems often relies on a “trial and error” approach to manipulate CHO cells. This approach is time-consuming and costly, and can lead to suboptimal process performance. The use of mathematical models has the potential to greatly accelerate and improve the design and optimization of antibody production. Starting from a systematic and formal approach, the aim is to achieve an automatic design of the whole process that allows optimal productivity to be reached. To this end, we develop mathematical models and methods for the design and optimization of antibody manufacturing systems. The mathematical models are based on Flexible Nets (FNs), a modeling formalism that accommodates uncertain parameters and nonlinear dynamics. FNs enable the development of comprehensive models that encompass both the metabolic network of CHO cells and the dynamics of the bioreactor in which the cells are cultured. Thus, by integrating macroscopic variables (e.g. dilution rate, substrate concentration, cell density, etc.) with microscopic variables (such as intracellular metabolic fluxes), our model represents a multi-scale system and facilitates global optimization.
000156541 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-130449B-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-113969RB-I00
000156541 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000156541 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000156541 700__ $$aJoven, Teresa
000156541 700__ $$aSzéliová, Diana
000156541 700__ $$aZanghellini, Jürgen
000156541 700__ $$0(orcid)0000-0002-7093-228X$$aJúlvez, Jorge$$uUniversidad de Zaragoza
000156541 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000156541 773__ $$g27 (2025), 1498-1510$$pComput. struct. biotechnol. j.$$tComputational and Structural Biotechnology Journal$$x2001-0370
000156541 8564_ $$s1374718$$uhttps://zaguan.unizar.es/record/156541/files/texto_completo.pdf$$yVersión publicada
000156541 8564_ $$s2592872$$uhttps://zaguan.unizar.es/record/156541/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000156541 909CO $$ooai:zaguan.unizar.es:156541$$particulos$$pdriver
000156541 951__ $$a2025-10-17-14:13:04
000156541 980__ $$aARTICLE