Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

Modeling stochasticity and robustness in gene regulatory networks.

MOTIVATION: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. RESULTS: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. AVAILABILITY: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

Overall and cause-specific mortality in GH-deficient adults on GH replacement.

OBJECTIVE: Hypopituitarism is associated with an increased mortality rate but the reasons underlying this have not been fully elucidated. The purpose of this study was to evaluate mortality and associated factors within a large GH-replaced population of hypopituitary patients. DESIGN: In KIMS (Pfizer International Metabolic Database) 13,983 GH-deficient patients with 69,056 patient-years of follow-up were available. METHODS: This study analysed standardised mortality ratios (SMRs) by Poisson regression. IGF1 SDS was used as an indicator of adequacy of GH replacement. Statistical significance was set to P<0.05. RESULTS: All-cause mortality was 13% higher compared with normal population rates (SMR, 1.13; 95% confidence interval, 1.04-1.24). Significant associations were female gender, younger age at follow-up, underlying diagnosis of Cushing's disease, craniopharyngioma and aggressive tumour and presence of diabetes insipidus. After controlling for confounding factors, there were statistically significant negative associations between IGF1 SDS after 1, 2 and 3 years of GH replacement and SMR. For cause-specific mortality there was a negative association between 1-year IGF1 SDS and SMR for deaths from cardiovascular diseases (P=0.017) and malignancies (P=0.044). CONCLUSIONS: GH-replaced patients with hypopituitarism demonstrated a modest increase in mortality rate; this appears lower than that previously published in GH-deficient patients. Factors associated with increased mortality included female gender, younger attained age, aetiology and lower IGF1 SDS during therapy. These data indicate that GH replacement in hypopituitary adults with GH deficiency may be considered a safe treatment.

One-year acyclovir prophylaxis for preventing varicella-zoster virus disease after hematopoietic cell transplantation: no evidence of rebound varicella-zoster virus disease after drug discontinuation.

No consensus exists on whether acyclovir prophylaxis should be given for varicella-zoster virus (VZV) prophylaxis after hematopoietic cell transplantation because of the concern of "rebound" VZV disease after discontinuation of prophylaxis. To determine whether rebound VZV disease is an important clinical problem and whether prolonging prophylaxis beyond 1 year is beneficial, we examined 3 sequential cohorts receiving acyclovir from day of transplantation until engraftment for prevention of herpes simplex virus reactivation (n = 932); acyclovir or valacyclovir 1 year (n = 1117); or acyclovir/valacyclovir for at least 1 year or longer if patients remained on immunosuppressive drugs (n = 586). In multivariable statistical models, prophylaxis given for 1 year significantly reduced VZV disease (P < .001) without evidence of rebound VZV disease. Continuation of prophylaxis beyond 1 year in allogeneic recipients who remained on immunosuppressive drugs led to a further reduction in VZV disease (P = .01) but VZV disease developed in 6.1% during the second year while receiving this strategy. In conclusion, acyclovir/valacyclovir prophylaxis given for 1 year led to a persistent benefit after drug discontinuation and no evidence of a rebound effect. To effectively prevent VZV disease in long-term hematopoietic cell transplantation survivors, additional approaches such as vaccination will probably be required.