Biology subjects

Mitsos, A.

Publications and source records attributed to Mitsos, A..

3 recordsLinked to original sources

bioRxiv2023

Itaconic acid production by co-feeding of Ustilago maydis: a combined approach of experimental data, design of experiments and metabolic modeling

Itaconic acid is a platform chemical with a range of applications in polymer synthesis and is also discussed for biofuel production. While produced in industry from glucose or sucrose, co-feeding of glucose and acetate was recently discussed to increase itaconic acid production by the smut fungus Ustilago maydis. In this study, we investigate the optimal co-feeding conditions by interlocking experimental and computational methods. Flux balance analysis indicates that acetate improves the itaconic acid yield up to a share of 40 % acetate on a carbon molar basis. A design of experiment results in the maximum yield of 0.14 itaconic acid per carbon source from 100 g L-1 glucose and 12 g L-1 acetate. The yield is improved by around 22 % when compared to feeding of glucose as sole carbon source. To further improve the yield, gene deletion targets are discussed that were identified using the metabolic optimization tool OptKnock. The study contributes ideas to reduce land use for biotechnology, by incorporating acetate as co-substrate, a C2-carbon source that is potentially derived from carbon dioxide.

microbiology↗

bioRxiv2022

Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. (1994), Bulletin of Mathematical Biology, vol. 56, no. 2, pp. 295-321) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsovs model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the models future predictions.

bioinformatics↗

bioRxiv2020

Converting networks to predictive logic models from perturbation signalling data with CellNOpt

MotivationThe molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large data-sets is increasing steadily as new experimental approaches are developed. A useful way to extract mechanistic insight from the data is by integrating them with a prior knowledge network of signalling to obtain dynamic models. Logic models scale better with network size than alternative kinetic models, while keeping the interpretation of the model simple, making them particularly suitable for large datasets. ResultsCellNOpt is a collection of Bioconductor R packages for building logic models from perturbation data and prior knowledge of signalling networks. We have recently developed new components and refined the existing ones. These updates include (i) an Integer Linear Programming (ILP) formulation which guarantees efficient optimisation for Boolean models, (ii) a probabilistic logic implementation for semi-quantitative datasets and (iii) the integration of MaBoSS, a stochastic Boolean simulator. Furthermore, we introduce Dynamic-Feeder, a tool to identify missing links not present in the prior knowledge. We have also implemented systematic post-hoc analyses to highlight the key components and parameters of our models. Finally, we provide an R-Shiny tool to run CellNOpt interactively. AvailabilityR-package(s): https://github.com/saezlab/cellnopt Contactjulio.saez@bioquant.uni-heidelberg.de Supplementary informationSupplemental Text.

bioinformatics↗