CplexA

CplexA computes probabilities and average properties of macromolecular assemblies to analyze gene regulation and signal transduction by enumerating configurational states derived from interaction energetics.

Key Features:

• Functional Programming Approach: Employs functional programming paradigms to efficiently handle computations across an exponentially large number of configurational states.
• Stochastic Modeling of Networks: Incorporates stochastic methods to model network dynamics and integrates estimated reaction rates into stochastic kinetics of cellular networks.
• Thermodynamic Concepts: Uses thermodynamic principles to represent interaction energetics and to estimate reaction rates for macromolecular assemblies.
• Application to Gene Expression Studies: Analyzes gene expression regulated by complex promoters with multiple transcription factors binding at local and distal DNA sites.
• Cross-Scale Approach: Bridges network-level representations (protein-protein and DNA-protein interaction networks) and cellular dynamics, with applicability to systems such as the lac operon and phage lambda induction switches.
• Mathematica Package: Implemented as a Mathematica package.

Scientific Applications:

• Gene regulation at complex promoters: Analysis of regulatory mechanisms involving multiple transcription factors and promoter architectures.
• Macromolecular assembly dynamics: Investigation of assembly and disassembly dynamics of protein-DNA and protein-protein complexes within cellular processes.
• Signal transduction pathway analysis: Exploration of how molecular interactions influence signaling network kinetics and behavior.
• Systemic properties of interaction networks: Study of emergent cellular properties arising from integrated protein-DNA and protein-protein interactions.

Methodology:

CplexA uses functional programming to compute probabilities and average properties over exponentially many configurational states, applies thermodynamic principles to estimate reaction rates, and integrates these into stochastic kinetics of macromolecular networks.