Genetic networks

Meir E, Munro EM, Odell GM, Von Dassow G. Ingeneue a versatile tool for reconstituting genetic networks, with examples from the segment polarity network. J Exp Zool 2002 294 216-51. 

Laub MT et al. Global analysis of the genetic network controlling a bacterial cell cycle. Science 2000 291 2144-2148. 

Genetic Network Cascade in Drosophila Segment Development... 

John Ross, Igor Schreiber, Marcel O. Vlad, and Adam Arkin. Determination of Complex Reaction Mechanisms Analysis of Chemical, Biological, and Genetic Networks. Oxford University Press 2005... 

All these genetic factors may interact in still-unknown genetic networks, leading to a cascade of pathogenic events characterized by abnormal protein processing and misfolding with snbseqnent accnmnlation of abnormal proteins (conformational changes), nbiqnitin-proteasome system dysfunction, excitotoxic reactions, oxidative... 

Other approaches to genetic networks include study of small circuits with either differential equations or stochastic differential equations. The use of stochastic equations emphasizes the point that noise is a central factor in the dynamics. This is of conceptual importance as well as practical importance. In all the families of models studied, the non-linear dynamical systems typically exhibit a number of dynamical attractors. These are subregions of the system s state space to which the system flows and in which it thereafter remains. A plausible interpretation is that these attractors correspond to the cell types of the organism. However, in the presence of noise, attractors can be destabilized. 

A genetic network in which the protein product of the first stage (T7RNA polymerase) is required to drive the protein synthesis of the second stage (GFP). 

Based on the initial report on the expression of functional protein into liposomes by Yomo and coworkers (Yu et al, 2001), the work by Ishikawa et al. (2004) represents another stage of the work on GFP expression. In fact, a two-stage genetic network is described, where the first stage is the production of T7 RNA polymerase, required to drive the GFP synthesis as the second stage. 

Ishikawa, K., Sato, K., Shima, Y., Urabe, L, and Yomo, T. (2004). Expression of cascading genetic network within liposomes. FEBSLett., 576, 387-90. 

Post-analytically, schemes are beginning to emerge specifically to compare practice and performance between laboratories pertaining to the interpretation of test results. For instance, in the UK, NEQAS in conjunction with the National Biochemical Genetic network, MetBio.Net, are offering a scheme that provides the opportunity, when given relevant clinical details, to interpret quantitative amino acid results. This proficiency scheme can compare interpretive skills without the need to circulate scarce clinical samples. 

Pineda, D., Gonzalez, J., Callaerts, P., Ikeo, K., Gehring, W.J. and Salo, E. (2000) Searching for the prototypic eye genetic network sine oculis is essential for eye regeneration in planarians. Proceedings of the National Academy of Sciences USA 97, 4525-4529. 

Biochemical networks, genetic networks, complex behavior... 

Gardner TS, di Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301 102-105... 

Many methods have been developed for model analysis for instance, bifurcation and stability analysis [88, 89], parameter sensitivity analysis [90], metabolic control analysis [16, 17, 91] and biochemical systems analysis [18]. One highly important method for model analysis and especially for large models, such as many silicon cell models, is model reduction. Model reduction has a long history in the analysis of biochemical reaction networks and in the analysis of nonlinear dynamics (slow and fast manifolds) [92-104]. In all cases, the aim of model reduction is to derive a simplified model from a larger ancestral model that satisfies a number of criteria. In the following sections we describe a relatively new form of model reduction for biochemical reaction networks, such as metabolic, signaling, or genetic networks. 

Scott, M. Ingalls, B. Kaem, M. Estimations of intrinsic and extrinsic noise in models of nonlinear genetic networks. Chaos 2006,16 026107. 

The next obvious step is to incorporate both a gene transcription system and protein synthesis in lipid vesicles. This was reported in 2004 by Ishikawa et al. [87], who managed to assemble a two-stage genetic network in liposomes in which the gene for an RNA polymerase was expressed first and the polymerase then used to produce mRNA required for GFP synthesis. 

Guet CC, Elowitz MB, Hsing WH, Leibler S (2002) Combinatorial synthesis of genetic networks. Science 296 1466-1470... 

Doyle and co-workers have used sensitivity and identifiability analyses in a complex genetic regulatory network to determine practically identifiable parameters (Zak et al., 2003), i.e., parameters that can be extracted from experiments with a certain confidence interval, e.g., 95%. The data used for analyses were based on simulation of their genetic network. Different perturbations (e.g., step, pulse) were exploited, and an identifiability analysis was performed. An important outcome of their analysis is that the best type of perturbations for maximizing the information content from hybrid multiscale simulations differs from that of the deterministic, continuum counterpart model. The implication of this interesting finding is that noise may play a role in systems-level tasks. 

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