Abstract
BackgroundRecent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling.ResultsWe demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes.ConclusionBiological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems.
Highlights
Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome
Sequence homology may be viewed as a means of generating an initial "draft" hypothesis for the gene network of a newly sequenced organism that can be built upon using high throughput experimental techniques such as DNA chips and microarrays for mRNA transcript profiling [3], protein abundance profiling with mass spectroscopy and 2-D gel electrophoresis [4], and protein-protein and proteinDNA binding assayed using SELDI mass spectrometry [5] and protein chips [6]
Yeast cell cycle data set As a proof of concept, we have used exhaustive search to generate fuzzy gene networks based on yeast (Saccharomyces cerevisiae) cell cycle microarray time series data sets presented in [18]
Summary
Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Given the potential scale and complexity of experiments and resulting data sets, biologists need a modeling and simulation framework to optimally design experiments and interpret results. The problem is not one of "reverse engineering" to find the optimal "best fit" gene, protein, and/or metabolite interaction model to explain a set of experimental results; rather, modeling should suggest the range of hypotheses that can potentially explain the results of one experiment and select the optimal experiment to reduce the number of possible alternative hypotheses, with the goal of converging to a biological system model that can be used to predict the effect of molecular perturbations. A major challenge of modeling biological systems is that conventional methods based on physical and chemical principles require data that is difficult to accurately and consistently obtain using either conventional biochemical or high throughput technologies, which typically yield noisy, semi-quantitative data (often in terms of a ratio rather than a physical quantity) [3]. Continuous modeling approaches (e.g., [10,11]) are limited to finding the single model that best fits experimental data given some set of constraints, such as a maximally sparse gene interaction network [11]
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