Automated refinement and inference of analytical models for metabolic networks

Automated refinement and inference of analytical models for metabolic networks.

The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model--suggesting nonlinear terms and structural modifications--or even constructing a new model that agrees with t...

Inference of analytical thermodynamic models for biological networks

Automated Inference of Symbolic Models for Gene Regulatory Networks

Motivation: Multiple models exist to describe the topology and the dynamical behavior of gene regulatory networks (GRNs). Among them, symbolic models (such as systems of ordinary differential equations) have the advantage of providing intuitive insights into the inner workings of a network so that general laws can be derived. Obtaining such models from time series data has proven to be a diffic...

Automated refinement of executable biological models

Motivation: Executable models of biological phenomena offer a powerful way to understand and analyze the properties of complex systems. By exploiting model checking techniques developed in the field of formal verification, we avoid the need for computationally intensive, exhaustive simulation approaches. Proofs of stability specifically demonstrate that a model accurately represents a robust eq...

Automated Variational Inference for Gaussian Process Models

We develop an automated variational method for approximate inference in Gaussian process (GP) models whose posteriors are often intractable. Using a mixture of Gaussians as the variational distribution, we show that (i) the variational objective and its gradients can be approximated efficiently via sampling from univariate Gaussian distributions and (ii) the gradients wrt the GP hyperparameters...