Noise effects in nonlinear biochemical signaling

Area of Science:

• Systems Biology
• Biophysical Chemistry
• Computational Biology

Background:

• Stochasticity is crucial for information processing in biological reaction networks.
• Current models often use Gaussian noise approximations, which can violate physical constraints like chemical concentration positivity.

Purpose of the Study:

• To demonstrate that Gaussian noise models can produce unphysical results despite rare nonphysical fluctuations.
• To develop accurate analytical and approximate methods for stochastic reaction network modeling.

Main Methods:

• Analysis of a simple incoherent-feedforward model exhibiting perfect adaptation.
• Utilizing time-scale separation for an analytically solvable approximate model.
• Application of a cutoff procedure to regularize Gaussian noise models.

Main Results:

• Exact solutions of Gaussian models can yield unphysical results (e.g., negative concentrations).
• An approximate model based on time-scale separation is analytically solvable and captures dynamical responses.
• Regularization techniques can correct for unphysical predictions of the Gaussian approximation.

Conclusions:

• Gaussian noise is an inadequate approximation for biological reaction networks due to potential unphysical outcomes.
• Time-scale separation and regularization offer viable alternatives for accurate stochastic modeling in systems biology.