Perturb-Seq combines CRISPR (clustered regularly interspaced short palindromic repeats)-
based genetic screens with single-cell RNA sequencing readouts for high-content …

In life sciences, deriving insights from dynamic models can be challenging due to the large
number of state variables involved. To address this, model reduction techniques can be …

Biochemical networks are usually modeled by ordinary differential equations that describe
the time evolution of the concentrations of the interacting (biochemical) species for specific …

Detailed dynamical systems models used in life sciences may include dozens or even
hundreds of state variables. Models of large dimension are not only harder from the …

Positive systems naturally arise in situations where the model tracks physical quantities.
Although the linear case is well understood, analysis and controller design for nonlinear …

Dynamical models described by ordinary differential equations (ODEs) are a fundamental
tool in the sciences and engineering. Exact reduction aims at producing a lower …

Efficient methods for the simulation of quantum circuits on classic computers are crucial for
their analysis due to the exponential growth of the problem size with the number of qubits …

Gaining insights from realistic dynamical models of biochemical systems can be challenging
given their large number of state variables. Model reduction techniques can mitigate this by …

Structural identifiability concerns the question of which unknown parameters of a model can
be recovered from (perfect) inputoutput data. If all of the parameters of a model can be …

Chemical reaction networks are a popular formalism for modeling biological processes
which supports both a deterministic and a stochastic interpretation based on ordinary …