Recent interest in modeling biochemical networks raises questions about the relationship
between often complex mathematical models and familiar arithmetic concepts from classical …

Improved mechanistic understanding of biochemical networks is one of the driving ambitions
of Systems Biology. Computational modeling allows the integration of various sources of …

A major goal of biophysics is to understand the physical mechanisms of biological
molecules and systems. Mechanistic models are evaluated based on their ability to explain …

Dynamical systems modeling, particularly via systems of ordinary differential equations, has
been used to effectively capture the temporal behavior of different biochemical components …

Motivation: Systems biology employs mathematical modelling to further our understanding of
biochemical pathways. Since the amount of experimental data on which the models are …

Biological models contain many parameters whose values are difficult to measure directly
via experimentation and therefore require calibration against experimental data. Markov …

Background Most of the modeling performed in the area of systems biology aims at
achieving a quantitative description of the intracellular pathways within a" typical cell" …

Using models to simulate and analyze biological networks requires principled approaches
to parameter estimation and model discrimination. We use Bayesian and Monte Carlo …

Bayesian inference is a powerful statistical paradigm that has gained popularity in many
fields of science, but adoption has been somewhat slower in biophysics. Here, I provide an …

The epidermal growth factor receptor (EGFR) kinase is generally considered to be activated
by either ligand-induced dimerisation or a ligand-induced conformational change within pre …