Biology subjects

Baldazzi, V.

Publications and source records attributed to Baldazzi, V..

4 recordsLinked to original sources

bioRxiv2026

Pareto Suboptimal Resource Allocation and Microbial Growth

Microbial growth has often been analyzed under the assumption that microorganisms have evolved to optimize phenotypic characteristics of interest, such as growth rate and growth yield. This assumption has been useful, for example, for the genome-scale modeling of metabolism and the study of the allocation of cellular resources to physiological processes. In many experimental situations of interest, however, microorganisms are found to be suboptimal with respect to phenotypic characteristics that are thought to be favorable in that situation. Whereas a strong theoretical framework exists to mathematically relate cellular resource allocation strategies to Pareto optimality of microbial growth and other biological processes, much less is known about the consequences of Pareto suboptimality. We extend the framework to the latter case and show that a given Pareto suboptimal phenotype can be explained by a range of underlying resource allocation strategies, each corresponding to a different growth physiology and biomass composition. We test the predictions with the help of a coarse-grained model of microbial growth and published experimental data, which relate Pareto suboptimal rate-yield phenotypes of Escherichia coli and the microalga Tisochrysis lutea to the macromolecular composition of the cells (storage metabolite and total protein contents). Changing the focus from Pareto optimality to suboptimality provides interesting leads to exploring the diversity of growth strategies that can support a given phenotype. This change of perspective is of practical interest, because some of the growth physiologies within this range may be important for biotechnological applications

systems biology↗

bioRxiv2023

Assessing Inter-Individual Genetic Variability in PeachSugar Metabolism through Reliable Parameter Estimation of a Kinetic Model

Integrating genetic information into dynamical models is key to understand variations among genotypes and improve crop performances face to current agronomic and ecological constraints. A first and necessary step in building gene-to-phenotype models is the calibration of a large number of genotypes. We compared here two different strategies for the calibration of an Ordinary Differential Equations (ODE) kinetic model simulating the accumulation of different sugars during peach fruit development. First, the model was calibrated for each genotype independently using a Genotype-Based (GB) strategy. Two formulations of the problem have been tested, either as a Single-Objective Optimization (GBS) problem or as a Multi-Objective Optimization (GBM) problem. Second, the model was calibrated for all genotypes simultaneously using a Population-Based (PB) strategy. The two strategies were first applied to a set of simulated data and then to a real dataset derived from an interspecific population of 106 peach genotypes. Results showed that the GB strategy allowed for a high goodness of fit for most genotypes, especially in the GBS formulation. However, the estimated parameters suffered from a lack of practical identifiability as independent repetitions of the estimation algorithm did not always converge to the same value for most genotypes. The PB calibration strategy overcame this issue showing a good identifiability of the population parameter values, a goodness of fit comparable to the one obtained with the GB strategy and a good characterisation of parameter variations within the progeny, which is a key to assess the inter-individual genetic variability. These results are an important step towards the development of reliable gene-to-phenotype models.

plant biology↗

bioRxiv2022

Resource allocation accounts for the large variabilityof rate-yield phenotypes across bacterial strains

Different strains of a microorganism growing in the same environment display a wide variety of growth rates and growth yields. We developed a coarse-grained model to test the hypothesis that different resource allocation strategies, corresponding to different compositions of the proteome, can account for the observed rate-yield variability. The model predictions were verified by means of a database of hundreds of published rate-yield and uptake-secretion phenotypes of Escherichia coli strains grown in standard laboratory conditions. We found a very good quantitative agreement between the range of predicted and observed growth rates, growth yields, and glucose uptake and acetate secretion rates. These results support the hypothesis that resource allocation is a major explanatory factor of the observed variability of growth rates and growth yields across different bacterial strains. An interesting prediction of our model, supported by the experimental data, is that high growth rates are not necessarily accompanied by low growth yields. The resource allocation strategies enabling high-rate, high-yield growth of E. coli lead to a higher saturation of enzymes and ribosomes, and thus to a more efficient utilization of proteomic resources. Our model thus contributes to a fundamental understanding of the quantitative relationship between rate and yield in E. coli and other microorganisms. It may also be useful for the rapid screening of strains in metabolic engineering and synthetic biology.

systems biology↗

bioRxiv2018

Organ-wide and ploidy-dependent regulations both contribute to cell size determination: evidence from a computational model of tomato fruit

The development of a new organ is the result of coordinated events of cell division and expansion, in strong interaction with each other. This paper presents a dynamic model of tomato fruit development that includes cells division, endoreduplication and expansion processes. The model is used to investigate the interaction among these developmental processes, in the perspective of a neo-cellular theory. In particular, different control schemes (either cell-autonomous or organ-controlled) are tested and results compared to observed data from two contrasted genotypes. The model shows that a pure cell-autonomous control fails to reproduce the observed cell size distribution, and an organ-wide control is required in order to get realistic cell sizes. The model also supports the role of endoreduplication as an important determinant of the final cell size and suggests a possible interaction through carbon allocation and metabolism.

plant biology↗