Cytological analysis of hybrids among triticales and trigopiros

We studied three different tricepiros: (Don Santiago x Don Noé), (Cumé x Horovitz) and (Cumé x Don Noé). The tricepiro (Don Santiago x Don Noé) was obtained by crossing the triticale Don Santiago INTA (AABBRR, 2n = 6x = 42) with the trigopiro Don Noé INTA (AABBDDJJ, 2n = 8x = 56). The number of chromosomes for the F1 was 2n = 49, the most frequent meiotic configuration being 14 bivalents and 21 univalents. The univalents were situated in the periphery of the equatorial plane, whereas the bivalents were located in the central zone. The chromatids in some of the univalents split when bivalents underwent reductional division in anaphase I. There were few laggard chromosomes or chromatids at this phase. The number of chromosomes (2n = 48-58) was high and variable, and the number of bivalents per cell (18-23) also high in F 3 individuals. In all F 8 tricepiros (Don Santiago x Don Noé), F 12 tricepiros (Cumé x Horovitz) and F 12 tricepiros (Cumé x Don Noé), the number of chromosomes (2n = 42) was the same, these retaining the rye genome, as demonstrated by GISH and FISH. These new synthesized allopolyploids constitute interesting models for investigating the evolutionary changes responsible for diploidization, and the chromosomal and genomic re-ordering that cannot be revealed in natural allopolyploids

A method of ‘speed coefficients’ for biochemical model reduction applied to the NF-κB system

© 2014, The Author(s). The relationship between components of biochemical network and the resulting dynamics of the overall system is a key focus of computational biology. However, as these networks and resulting mathematical models are inherently complex and non-linear, the understanding of this relationship becomes challenging. Among many approaches, model reduction methods provide an avenue to extract components responsible for the key dynamical features of the system. Unfortunately, these approaches often require intuition to apply. In this manuscript we propose a practical algorithm for the reduction of biochemical reaction systems using fast-slow asymptotics. This method allows the ranking of system variables according to how quickly they approach their momentary steady state, thus selecting the fastest for a steady state approximation. We applied this method to derive models of the Nuclear Factor kappa B network, a key regulator of the immune response that exhibits oscillatory dynamics. Analyses with respect to two specific solutions, which corresponded to different experimental conditions identified different components of the system that were responsible for the respective dynamics. This is an important demonstration of how reduction methods that provide approximations around a specific steady state, could be utilised in order to gain a better understanding of network topology in a broader context