Biology subjects

Valderrama-Gomez, M. A.

Publications and source records attributed to Valderrama-Gomez, M. A..

5 recordsLinked to original sources

bioRxiv2023

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

systems biology↗

bioRxiv2021

Phenotype-Centric Modeling for Rational Metabolic Engineering

Phenotype-centric modeling enables a paradigm shift in the analysis of kinetic models. It brings the focus to a networks biochemical phenotypes and their relationship with measurable traits (e.g., product yields, system dynamics, signal amplification factors, etc.) and away from computationally intensive parameter sampling and numerical simulation. Here, we explore applications of this new modeling strategy in the field of Rational Metabolic Engineering using the amorphadiene biosynthetic network as a case study. Our phenotype-centric approach not only identifies known beneficial intervention strategies for this network, but it also provides an understanding of mechanistic context for the validity of these predictions. Additionally, we propose a set of hypothetical strains with the potential to outperform reported production strains and enhance the mechanistic understanding of the amorphadiene biosynthetic network. We believe that phenotype-centric modeling can advance the field of Rational Metabolic Engineering by enabling the development of next generation kinetics-based algorithms and methods that do not rely on a priori knowledge of kinetic parameters but allow a structured, global analysis of the design space of parameter values.

bioengineering↗

bioRxiv2021

Molecular Systems Predict Equilibrium Distributions of Phenotype Diversity Available for Selection

Two long standing challenges in theoretical population genetics and evolution are predicting the distribution of phenotype diversity generated by mutation and available for selection and determining the interaction of mutation, selection, and drift to characterize evolutionary equilibria and dynamics. More fundamental for enabling such predictions is the current inability to causally link population genetic parameters, selection and mutation, to the underlying molecular parameters, kinetic and thermodynamic. Such predictions would also have implications for understanding cryptic genetic variation and the role of phenotypic robustness. Here we provide a new theoretical framework for addressing these challenges. It is built on Systems Design Space methods that relate system phenotypes to genetically-determined parameters and environmentally-determined variables. These methods, based on the foundation of biochemical kinetics and the deconstruction of complex systems into rigorously defined biochemical phenotypes, provide several innovations that automate (1) enumeration of the phenotypic repertoire without knowledge of kinetic parameter values, (2) representation of phenotypic regions and their relationships in a System Design Space, and (3) prediction of values for kinetic parameters, concentrations, fluxes and global tolerances for each phenotype. We now show that these methods also automate prediction of phenotype-specific mutation rate constants and equilibrium distributions of phenotype diversity in populations undergoing steady-state exponential growth. We introduce this theoretical framework in the context of a case study involving a small molecular system, a primordial circadian clock, compare and contrast this framework with other approaches in theoretical population genetics, and discuss experimental challenges for testing predictions.

evolutionary biology↗

bioRxiv2020

TaxisPy: A Python-based Software for the Quantitative Analysis of Bacterial Chemotaxis

Several species of bacteria are able to modify their swimming behavior in response to chemical attractants or repellents. Methods for the quantitative analysis of bacterial chemotaxis such as quantitative capillary assays are tedious and time-consuming. Computer-based video analysis of swimming bacteria represents a valuable method to directly assess their chemotactic response. Even though multiple studies have used this approach to elucidate various aspects of the bacterial chemotaxis, to date, no computer software for such analyses is freely available. Here, we introduce TaxisPy, a Python-based software for the quantitative analysis of bacterial chemotaxis. The software comes with an intuitive graphical user interface and can be easily accessed through Docker on any operating system. Using a video of freely swimming cells as input, TaxisPy estimates the cultures average tumbling frequency over time. We demonstrate the utility of the software by assessing the effect of different concentrations of the attractant shikimate on the swimming behavior of Pseudomonas putida F1 and by capturing the adaptation process that Escherichia coli undergoes after being exposed to L-aspartate.

microbiology↗

bioRxiv2020

Mechanistic Modeling of Biochemical Systems Without A Priori Parameter Values Using the Design Space Toolbox v.3.0

Mechanistic models of biochemical systems provide a rigorous kinetics-based description of various biological phenomena. They are indispensable to elucidate biological design principles and to devise and engineer systems with novel functionalities. To date, mathematical analysis and characterization of these models remain a challenging endeavor, the main difficulty being the lack of information for most system parameters. Here, we introduce the Design Space Toolbox v.3.0 (DST3), a software implementation of the Design Space formalism that enables mechanistic modeling of complex biological processes without requiring previous knowledge of the parameter values involved. This is achieved by making use of a phenotype-centric modeling approach, in which the system is first decomposed into a series of biochemical phenotypes. Parameter values realizing phenotypes of interest are predicted in a second step. DST3 represents the most generally applicable implementation of the Design Space formalism to date and offers unique advantages over earlier versions. By expanding the capabilities of the Design Space formalism and streamlining its distribution, DST3 represents a valuable tool for elucidating biological design principles and guiding the design and optimization of novel synthetic circuits.

systems biology↗