An ensemble method for identifying regulatory circuits with special reference to the qa gene cluster of Neurospora crassa

Abstract

A chemical reaction network for the regulation of the quinic acid (qa) gene cluster of Neurospora crassa is proposed. An efficient Monte Carlo method for walking through the parameter space of possible chemical reaction networks is developed to identify an ensemble of deterministic kinetics models with rate constants consistent with RNA and protein profiling data. This method was successful in identifying a model ensemble fitting available RNA profiling data on the qa gene cluster. With genome sequencing projects supplying an almost complete inventory of the building blocks of life, functional genomics is now facing the challenge of ''re-assembling the pieces'' (1, 2). Time-dependent mRNA (3) and protein profiling (4), protein-protein (5-8) and protein-DNA (9) interaction mapping, and the in vitro reconstruction of reaction networks (10, 11) are providing insight into the topology and kinetics of a living cell's full biochemical and gene regulatory circuitry. For the first time, it is now possible to place a particular biological circuit like those describing carbon metabolism, transcription, cell cycle, or the biological clock in simple eukaryotes in a larger context, and to examine the coupling of these circuits (12).New tools in computational biology are needed to identify these reaction networks by using well studied subcircuits like those for carbon metabolism, cell cycle, or the biological clock as a launch point into the entire circuit of a living cell. The qa gene cluster of Neurospora crassa and the GAL gene cluster of Saccharomyces cerevisiae in carbon metabolism have served as early paradigms for eukaryotic gene regulation (13,14) and are prime candidates for taking a genomic perspective on biological circuits. Mechanisms of regulation in the qa and GAL clusters with their transcriptional activator and repressors are also shared with many other regulatory networks. Because of their relative simplicity, they also provide an opportunity to test new genomic approaches to identifying chemical reaction networks or biological circuits that underlie many fundamental biological processes (15). Three opportunities exist now for identifying and refining biological circuits: the accumulation of transcriptional profiling data (3), a growing number of approaches to modeling gene regulation (11,(15)(16)(17)(18)(19)(20)(21), and the ability to carry out the in vitro reconstruction of biological circuits with a diversity of emergent properties including bistable (10) and oscillatory activity (11).However, initially, the profiling data will be scarce and the unknown parameters plentiful. Identification of the parameters in a reaction network is further complicated by the facts that the data are noisy and that our knowledge of the underlying reaction network's topology and of its participating molecular species is incomplete, even in well studied networks like those for the -switch, lac operon, trp operon, or GAL cluster. To circumvent these difficulties, we present a statistical modeling approach called the ensemble m...

A genetic network for the clock of Neurospora crassa

“…A central open question of systems biology is whether these building blocks are necessary and sufficient to define a circuit or genetic network that oscillates, and how, in quantitative detail, such oscillations emerge from the interactions among these building blocks. Here we use a recently developed method of genetic network identification (5) to find an ensemble of oscillating network models, constituted from wc-1, wc-2, and frq and their products, which is quantitatively consistent with available RNA and protein profiling data on the N. crassa biological clock. The use of genetic networks to integrate diverse experimental information and to predict the behavior of a complex trait, such as the biological clock, provides a new paradigm for quantitative genetics at the molecular level (6).…”

Workflow for generating competing hypothesis from models with parameter uncertainty

“…To study the FPS by a sole statistical exploration of all points in fsFPS for a given parameter p will return a distribution that spreads over most of the original allowed range, thus limiting its practical use. Correlation and two-dimensional projections of pairs of parameters can be informative [1] but limited to the non-linearities inherent to ODE models. However, it is relevant to observe by simple measures how informative a parameter can be.…”

Bifurcation analysis of a model of the budding yeast cell cycle

“…Although the parameter set presented in Table I leads to a satisfactory fit of the model to many experimental observations, the choice of parameter values should be further constrained by new biochemical data about the kinetics of protein-protein interactions and further improved by automatic parameter estimation techniques. 33,34 On the other hand, different sets of parameters, leading to different bifurcation scenarios, are interesting from a theoretical standpoint. We have proposed a set of parameters for a reduced ͑three-variable͒ model leading to a SNIC bifurcation.…”