Vassily Hatzimanikatis

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Cellular functions are largely regulated by reversible post-translational modifications of proteins which act as switches. Amongst these, S-palmitoylation is unique in that it confers hydrophobicity. Due to technical difficulties, the understanding of this ...

The central objective of this work is to revisit the development of the widely used solubility parameters and linear solvation energy relationships (LSER) and seek interconnections and possibilities for secure exchange of information. Partial solvation par ...

The expanding generation of dynamic biological data requires approaches that integrate and analyze information from different types of cellular processes –metabolism, regulation, and signaling–, and ultimately increase our insights into the cell behavior u ...

Developing efficient bioprocesses requires selecting the best biosynthetic pathways, which can be challenging and time-consuming due to the vast amount of data available in databases and literature. The extension of the shikimate pathway for the biosynthes ...

In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional ...

Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treat ...

Despite the establishment of design principles to optimize codon choice for heterologous expression vector design, the relationship between codon sequence and final protein yield remains poorly understood. In this work, we present a computational framework ...

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how ...

Synthetic biology and metabolic engineering rely on computational search tools for predictions of novel biosynthetic pathways to industrially important compounds, many of which are derived from aromatic amino acids. Pathway search tools vary in their scope ...

Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA pol ...

Systems biology has long been interested in models capturing both metabolism and expression in a cell. We propose here an implementation of the metabolism and expression model formalism (ME-models), which we call ETFL, for Expression and Thermodynamics Flu ...

Motivation: Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and ...

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biol ...

Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual react ...

Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consiste ...

The engineering of cells for the production of fuels and chemicals involves simultaneous optimization of multiple objectives, such as specific productivity, extended substrate range and improved tolerance – all under a great degree of uncertainty. The achi ...

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 re ...

Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the la ...

There is a remarkable wealth of thermodynamic information in freely accessible databases, the LSER database being a classical example. The LSER, or Abraham solvation parameter model, is a very successful predictive tool in a variety of applications in the ...

Metabolic networks have been studied for decades, and sophisticated computational frameworks are needed to augment experimental approaches to harness these complex networks. BNICE (Biochemical Network Integrated Computational Explorer), a computational app ...

Vassily Hatzimanikatis

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Model-Driven Understanding of Palmitoylation Dynamics: Regulated Acylation of the Endoplasmic Reticulum Chaperone Calnexin

Redefining solubility parameters: Bulk and surface properties from unified molecular descriptors

Integration of Metabolic, Regulatory and Signaling Networks Towards Analysis of Perturbation and Dynamic Responses

Computer-aided design and implementation of efficient biosynthetic pathways to produce high added-value products derived from tyrosine in Escherichia coli

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast

A computational framework for the design of optimal protein synthesis

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks

ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds

Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models

Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties

Modeling of uncertainties in biochemical reactions

Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

Production of biofuels and biochemicals: in need of an ORACLE

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

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Linear Solvation-energy Relationships (lser) and Equation-of-state Thermodynamics: on the Extraction of Thermodynamic Information From the Lser Database

DREAMS of metabolism