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Michael Frenklach

Publications and source records attributed to Michael Frenklach.

3 recordsLinked to original sources

Model discrimination using data collaboration.

This paper introduces a practical data-driven method to discriminate among large-scale kinetic reaction models. The approach centers around a computable measure of model/data mismatch. We introduce two provably convergent algorithms that were developed to accommodate large ranges of uncertainty in the model parameters. The algorithms are demonstrated on a simple toy example and a methane combustion model with more than 100 uncertain parameters. They are subsequently used to discriminate between two models for a contemporarily studied biological signaling network.

Algorithms↗

An allosteric model for transmembrane signaling in bacterial chemotaxis.

Bacteria are able to sense chemical gradients over a wide range of concentrations. However, calculations based on the known number of receptors do not predict such a range unless receptors interact with one another in a cooperative manner. A number of recent experiments support the notion that this remarkable sensitivity in chemotaxis is mediated by localized interactions or crosstalk between neighboring receptors. A number of simple, elegant models have proposed mechanisms for signal integration within receptor clusters. What is a lacking is a model, based on known molecular mechanisms and our accumulated knowledge of chemotaxis, that integrates data from multiple, heterogeneous sources. To address this question, we propose an allosteric mechanism for transmembrane signaling in bacterial chemotaxis based on the "trimer of dimers" model, where three receptor dimers form a stable complex with CheW and CheA. The mechanism is used to integrate a diverse set of experimental data in a consistent framework. The main predictions are: (1) trimers of receptor dimers form the building blocks for the signaling complexes; (2) receptor methylation increases the stability of the active state and retards the inhibition arising from ligand-bound receptors within the signaling complex; (3) trimer of dimer receptor complexes aggregate into clusters through their mutual interactions with CheA and CheW; (4) cooperativity arises from neighboring interaction within these clusters; and (5) cluster size is determined by the concentration of receptors, CheA, and CheW. The model is able to explain a number of seemingly contradictory experiments in a consistent manner and, in the process, explain how bacteria are able to sense chemical gradients over a wide range of concentrations by demonstrating how signals are integrated within the signaling complex.

Allosteric Regulation↗

Particle aggregation with simultaneous surface growth.

Particle aggregation with simultaneous surface growth was modeled using a dynamic Monte Carlo method. The Monte Carlo algorithm begins in the particle inception zone and constructs aggregates via ensemble-averaged collisions between spheres and deposition of gaseous species on the sphere surfaces. Simulations were conducted using four scenarios. The first, referred to as scenario 0, is used as a benchmark and simulates aggregation in the absence of surface growth. Scenario 1 forces all balls to grow at a uniform rate while scenario 2 only permits them to grow once they have collided and stuck to each other. The last one is a test scenario constructed to confirm conclusions drawn from scenarios 0-2. The transition between the coalescent and the fully developed fractal aggregation regimes is investigated using shape descriptors to quantify particle geometry. They are used to define the transition between the coalescent and fractal growth regimes. The simulations demonstrate that the morphology of aggregating particles is intimately related to both the surface deposition and particle nucleation rates.

Journal Article↗