The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli.

Background: Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models.

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis.

Summary: pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements.

Motivation: High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology.

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations.

Large-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization.

redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.

Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups.

Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes.

The overarching ambition of kinetic metabolic modeling is to capture the dynamic behavior of metabolism to such an extent that systems and synthetic biology strategies can reliably be tested in silico. The lack of kinetic data hampers the development of kinetic models, and most of the current models use ad hoc reduced stoichiometry or oversimplified kinetic rate expressions, which may limit their predictive strength. There is a need to introduce the community-level standards that will organize and accelerate the future developments in this area.

Spatial proteomic and phospho-proteomic organization in three prototypical cell migration modes.

Background: Tight spatio-temporal signaling of cytoskeletal and adhesion dynamics is required for localized membrane protrusion that drives directed cell migration. Different ensembles of proteins are therefore likely to get recruited and phosphorylated in membrane protrusions in response to specific cues.

Results: HERE, WE USE AN ASSAY THAT ALLOWS TO BIOCHEMICALLY PURIFY EXTENDING PROTRUSIONS OF CELLS MIGRATING IN RESPONSE TO THREE PROTOTYPICAL RECEPTORS: integrins, recepor tyrosine kinases and G-coupled protein receptors.

Prediction stability in a data-based, mechanistic model of σF regulation during sporulation in Bacillus subtilis.

Mathematical modeling of biological networks can help to integrate a large body of information into a consistent framework, which can then be used to arrive at novel mechanistic insight and predictions. We have previously developed a detailed, mechanistic model for the regulation of σ(F) during sporulation in Bacillus subtilis. The model was based on a wide range of quantitative data, and once fitted to the data, the model made predictions that could be confirmed in experiments.

Analyzing and constraining signaling networks: parameter estimation for the user.

The behavior of most dynamical models not only depends on the wiring but also on the kind and strength of interactions which are reflected in the parameter values of the model. The predictive value of mathematical models therefore critically hinges on the quality of the parameter estimates. Constraining a dynamical model by an appropriate parameterization follows a 3-step process.

Predictive models for cellular signaling networks.

This chapter provides an introduction to the formulation and analysis of differential-equation-based models for biological regulatory networks. In the first part, we discuss basic reaction types and the use of mass action kinetics and of simplifying approximations in the development of models for biological signaling. In the second part we introduce phase plane and linear stability analysis to evaluate the time evolution and identify the long-term attractors of dynamic systems.

A computational analysis of the dynamic roles of talin, Dok1, and PIPKI for integrin activation.

Integrin signaling regulates cell migration and plays a pivotal role in developmental processes and cancer metastasis. Integrin signaling has been studied extensively and much data is available on pathway components and interactions. Yet the data is fragmented and an integrated model is missing.

Plasticity of TGF-β signaling.

Background: The family of TGF-β ligands is large and its members are involved in many different signaling processes. These signaling processes strongly differ in type with TGF-β ligands eliciting both sustained or transient responses. Members of the TGF-β family can also act as morphogen and cellular responses would then be expected to provide a direct read-out of the extracellular ligand concentration.