Summary. Motivated by the intriguing complexity of biochemical circuitry within individual cells we study Stochastic Chemical Reaction Networks (SCRNs), a formal model that considers a set of chemical reactions acting on a finite number of molecules in a well-stirred solution according to standard chemical kinetics equations. SCRNs have been widely used for describing naturally occurring (bio)chemical systems, and with the advent of synthetic biology they become a promising language for the design of artificial biochemical circuits. Our interest here is the computational power of SCRNs and how they relate to more conventional models of computation. We survey known connections and give new connections between SCRNs and

Developmental processes in complex animals are directed by a hardwired genomic regulatory code, the ultimate function of which is to set up a progression of transcriptional regulatory states in space and time. The code specifies the gene regulatory networks (GRNs) that underlie all major developmental events. Models of GRNs are required for analysis, for experimental manipulation and, most fundamentally, for comprehension of how GRNs work. To model GRNs requires knowledge of both their overall structure, which depends upon linkage amongst regulatory genes, and the modular building blocks of which GRNs are heirarchically constructed. The building blocks consist of basic transcriptional control processes executed by one or a few functionally linked genes. We show how the functions of several such building blocks can be considered in mathematical terms, and discuss resolution of GRNs by both ‘‘top down’ ’ and ‘‘bottom up’ ’ approaches. BioEssays 24:1118–1129,

Background: The comprehension of the gene regulatory code in eukaryotes is one of the major challenges of systems biology, and is a requirement for the development of novel therapeutic strategies for multifactorial diseases. Its bi-fold degeneration precludes brute force and statistical approaches based on the genomic sequence alone. Rather, recursive integration of systematic, whole-genome experimental data with advanced statistical regulatory sequence predictions needs to be developed. Such experimental approaches as well as the prediction tools are only starting to become available and increasing numbers of genome sequences and empirical sequence annotations are under continual discovery-driven change. Furthermore, given the complexity of the question, a decade(s) long multi-laboratory effort needs to be envisioned. These constraints need to be considered in the creation of a framework that can pave a road to successful comprehension of the gene regulatory code. Results: We introduce here a concept for such a framework, based entirely on systematic annotation in terms of probability profiles of genomic sequence using any type of relevant experimental and theoretical information and subsequent cross-correlation analysis in hypothesisdriven

A major goal of pharmaceutical bioinformatics is to develop computational tools for systematic in silico molecular target identification. Here we demonstrate that in the yeast Saccharomyces cerevisiae the phenotypic effect of single gene deletions simultaneously correlates with fluctuations in mRNA expression profiles, the functional categorization of the gene products, and their connectivity in the yeast's protein-protein interaction network. Building on these quantitative correlations, we developed a computational method for predicting the phenotypic effect of a given gene's functional disabling or removal. Our subsequent analyses were in good agreement with the results of systematic gene deletion experiments, allowing us to predict the deletion phenotype of a number of untested yeast genes. The results underscore the utility of large genomic databases for in silico systematic drug target identification in the postgenomic era.

Research article Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Trypanosoma cruzi is a protozoan pathogen of major medical importance in Latin America. It is also an early diverging eukaryote that displays many unusual biochemical features. The completion of the T. cruzi genome project has highlighted the need to extend the range of techniques available to study gene function. To this end we report the development of a stable tetracycline-dependent expression vector applicable to this parasite and describe in detail the parameters of the system. Results: We first produced T. cruzi cell lines that constitutively expressed bacteriophage T7 RNA polymerase and the tetracycline repressor protein from a multicopy episome. An integrative vector with an inducible expression site under the control of a tetracycline-regulatable T7 promoter (pTcINDEX) was targeted to the transcriptionally silent ribosomal RNA spacer region of these parasites and transformants selected using a T7 RNA polymerase-dependent hygromycin resistance gene. To test the system we used two marker proteins, luciferase and red fluorescent protein (RFP), and an endogenous parasite protein (a mitochondrial superoxide dismutase). In each

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: In recent years, stochastic descriptions of biochemical reactions based on the Master Equation (ME) have become widespread. These are especially relevant for models involving gene regulation. Gillespie’s Stochastic Simulation Algorithm (SSA) is the most widely used method for the numerical evaluation of these models. The SSA produces exact samples from the distribution of the ME for finite times. However, if the stationary distribution is of interest, the SSA provides no information about convergence or how long the algorithm needs to be run to sample from the stationary distribution with given accuracy. Results: We present a proof and numerical characterization of a Perfect Sampling algorithm for the ME of networks of biochemical reactions prevalent in gene regulation and enzymatic catalysis. Our algorithm combines the SSA with Dominated Coupling From The Past (DCFTP) techniques to provide guaranteed sampling from the stationary distribution. The resulting DCFTP-SSA is applicable to networks of reactions with uni-molecular stoichiometries and sub-linear, (anti-) monotone propensity functions. We showcase its applicability studying steady-state properties

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Research article Analytical approximations for the amplitude and period of a relaxation oscillator

This is an Open Access article distributed under the terms of the Creative Commons Attribution License