Biology subjects

Karr, J. R.

Publications and source records attributed to Karr, J. R..

6 recordsLinked to original sources

bioRxiv2022

RBAtools: a programming interface for Resource Balance Analysis modelling

MotivationEfficient resource allocation contributes to an organisms fitness and improves success in competition. The Resource Balance Analysis (RBA) computational framework enables the analysis of an organisms growth-optimal configurations in various environments, at genome-scale. The existing RBApy software enables the construction of RBA models on genome-scale and the calculation of medium-specific, growth-optimal cell states, including metabolic fluxes and the abundance of macromolecular machines. However, to address the needs of non-expert users, there is a need for a simple programming API, easy to use and interoperable with other software through convenient formats for models and data. ResultsThe RBAtools python package enables the convenient use of RBA models and addresses non-expert users. As a flexible programming interface, it enables the implementation of custom workflows and simplifies the modification of existing genome-scale RBA models and data export to various formats. The features comprise simulation, model fitting, parameter screens, sensitivity analysis, variability analysis, and the construction of Pareto fronts. Models and data are represented as structured tables, in HTML, and common formats for fluxomics and proteomics visualization. AvailabilityDetails about RBA can be found at rba.inrae.fr. RBAtools documentation, installation instructions, and tutorials are available at sysbioinra.github.io/rbatools. Contactwolfram.liebermeister@inrae.fr, anne.goelzer@inrae.fr

systems biology↗

bioRxiv2021

runBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats

Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed runBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. runBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable runBioSimulations to execute nine frameworks and 44 algorithms, and they make runBioSimulations extensible to additional frameworks and algorithms. runBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that runBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models.

systems biology↗

bioRxiv2020

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually-built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

systems biology↗

bioRxiv2020

Datanator: an integrated database of molecular data for quantitatively modeling cellular behavior

Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering, and medicine. However, it is difficult to obtain the diverse data needed for integrative research. To facilitate biochemical research, we developed Datanator (https://datanator.info), an integrated database and set of tools for finding clouds of multiple types of molecular data about specific molecules and reactions in specific organisms and environments, as well as data about chemically-similar molecules and reactions in phylogenetically-similar organisms in similar environments. Currently, Datanator includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction rate constants about a broad range of organisms. Going forward, we aim to launch a community initiative to curate additional data. Datanator also provides tools for filtering, visualizing, and exporting these data clouds. We believe that Datanator can facilitate a wide range of research from integrative mechanistic models, such as whole-cell models, to comparative data-driven analyses of multiple organisms.

systems biology↗

bioRxiv2020

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterise their metabolism at genome-scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modelling formalism of metabolic network modelling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded FBA models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae, and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level. SIGNIFICANCEDue to fundamental challenges limiting the experimental characterisation of metabolism within individual cells, computational methods are needed to help infer the metabolic behaviour of single cells from information about their transcriptomes and proteomes. In this paper, we present SSA-FBA, the first systematic framework for modelling the stochastic dynamics of single cells at the level of genome-scale metabolic reaction networks. We provide a robust and efficient algorithm for simulating SSA-FBA models, and apply it to a case study involving the metabolism, RNA and protein synthesis and turnover of a single Mycoplasma pneumoniae cell.

systems biology↗

bioRxiv2017

A blueprint for human whole-cell modeling

Whole-cell models of human cells are a central goal of systems biology. Such models could help researchers understand cell biology and help physicians treat disease. Despite significant challenges, we believe that human whole-cell models are rapidly becoming feasible. To develop a plan for achieving human whole-cell models, we analyzed the existing models of individual cellular pathways, surveyed the biomodeling community, and reflected on our experience developing whole-cell models of bacteria. Based on these analyses, we propose a plan for a project, termed the Human Whole-Cell Modeling Project, to achieve human whole-cell models. The foundations of the plan include technology development, standards development, and interdisciplinary collaboration.

systems biology↗