Timing Robustness in the Budding and Fission Yeast Cell Cycles

Robustness of biological models has emerged as an important principle in systems biology. Many past analyses of Boolean models update all pending changes in signals simultaneously (i.e., synchronously), making it impossible to consider robustness to variations in timing that result from noise and different environmental conditions. We checked previously published mathematical models of the cell cycles of budding and fission yeast for robustness to timing variations by constructing Boolean models and analyzing them using model-checking software for the property of speed independence. Surprisingly, the models are nearly, but not totally, speed-independent. In some cases, examination of timing problems discovered in the analysis exposes apparent inaccuracies in the model. Biologically justified revisions to the model eliminate the timing problems. Furthermore, in silico random mutations in the regulatory interactions of a speed-independent Boolean model are shown to be unlikely to preserve speed independence, even in models that are otherwise functional, providing evidence for selection pressure to maintain timing robustness. Multiple cell cycle models exhibit strong robustness to timing variation, apparently due to evolutionary pressure. Thus, timing robustness can be a basis for generating testable hypotheses and can focus attention on aspects of a model that may need refinement

Behavior of Poly electrolyte Gels in Concentrated Solutions of Highly Soluble Salts

Ionic hydrogels are an abundant class of materials with applications ranging from drug delivery devices to high performance concrete to baby diapers. A more thorough understanding of interactions between poly electrolyte networks and ionic solutes is critical as these materials are further tailored for performance applications in highly targeted ionic environments. In this work, we seek to develop structure-property relationships between polyelectrolyte gels and environments containing high concentrations of multivalent ions. Specifically, this work seeks to elucidate the causes behind differences in hydrogel response to divalent ions of main group metals versus transition metals. PANa-co-PAM hydrogels containing low and high fractions of ionic groups are investigated in solutions of DI water, NaCl, CaCl2, and CuSO4 at concentrations ranging from 5 to 100 mM in order to understand 1) the transient or permanent nature of crosslinks produced in these networks by divalent counter-ions, 2) the role of polymer ionic content in these interactions, and 3) how these interactions scale with salt concentration. Gravimetric swelling and mechanical compression testing are employed to characterize water and salt-swollen hydrogels in order to develop guiding principles to control and manipulate material properties through polymer-counter-ion interactions. The work presented here confirms the formation of permanent crosslinks by transition metal ions, offers explanation for the behavioral discrepancy observed between ionic hydrogels and main group versus transition metal ions, and illustrates how such hydrogel properties scale with counter-ion concentration

Long-Term Stability of Visual Pattern Selective Responses of Monkey Temporal Lobe Neurons

Many neurons in primate inferotemporal (IT) cortex respond selectively to complex, often meaningful, stimuli such as faces and objects. An important unanswered question is whether such response selectivity, which is thought to arise from experience-dependent plasticity, is maintained from day to day, or whether the roles of individual cells are continually reassigned based on the diet of natural vision. We addressed this question using microwire electrodes that were chronically implanted in the temporal lobe of two monkeys, often allowing us to monitor activity of individual neurons across days. We found that neurons maintained their selectivity in both response magnitude and patterns of spike timing across a large set of visual images throughout periods of stable signal isolation from the same cell that sometimes exceeded two weeks. These results indicate that stimulus-selectivity of responses in IT is stable across days and weeks of visual experience

Consistency and diversity of spike dynamics in the neurons of bed nucleus of Stria Terminalis of the rat: a dynamic clamp study

Neurons display a high degree of variability and diversity in the expression and regulation of their voltage-dependent ionic channels. Under low level of synaptic background a number of physiologically distinct cell types can be identified in most brain areas that display different responses to standard forms of intracellular current stimulation. Nevertheless, it is not well understood how biophysically different neurons process synaptic inputs in natural conditions, i.e., when experiencing intense synaptic bombardment in vivo. While distinct cell types might process synaptic inputs into different patterns of action potentials representing specific "motifs'' of network activity, standard methods of electrophysiology are not well suited to resolve such questions. In the current paper we performed dynamic clamp experiments with simulated synaptic inputs that were presented to three types of neurons in the juxtacapsular bed nucleus of stria terminalis (jcBNST) of the rat. Our analysis on the temporal structure of firing showed that the three types of jcBNST neurons did not produce qualitatively different spike responses under identical patterns of input. However, we observed consistent, cell type dependent variations in the fine structure of firing, at the level of single spikes. At the millisecond resolution structure of firing we found high degree of diversity across the entire spectrum of neurons irrespective of their type. Additionally, we identified a new cell type with intrinsic oscillatory properties that produced a rhythmic and regular firing under synaptic stimulation that distinguishes it from the previously described jcBNST cell types. Our findings suggest a sophisticated, cell type dependent regulation of spike dynamics of neurons when experiencing a complex synaptic background. The high degree of their dynamical diversity has implications to their cooperative dynamics and synchronization

Competition-based model of pheromone component ratio detection in the moth

For some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy

Large rivers and orogens: the evolution of the Yarlung Tsangpo–Irrawaddy system and the eastern Himalayan syntaxis

The eastern Himalayan syntaxis has experienced some of the highest rates of deformation and erosion in the orogen during the Late Cenozoic, and the Yarlung Tsangpo, Brahmaputra, Irrawaddy, Salween, and Mekong rivers are the key erosional systems in that region. The Yarlung Tsangpo drains southern Tibet and the deep Siang River gorge through the eastern Himalayan syntaxis before joining the Brahmaputra in northeastern India. It has been proposed that the Yarlung Tsangpo drained into other large rivers of southern Asia, such as the Irrawaddy, Salween and Red River. We have used uranium/lead dating and hafnium measurements of detrital zircons from Cenozoic sedimentary deposits in Central Myanmar to demonstrate that the Yarlung Tsangpo formerly drained into the Irrawaddy River in Myanmar through the eastern syntaxis, and that this ancient river system was established by (at least) the Middle–Late Eocene. The Yarlung Tsangpo–Irrawaddy river disconnected in the Early Miocene driven by increased deformation in the eastern syntaxis and headward erosion by tributaries of the Brahmaputra. Our results highlight the significance of the sedimentary record of large orogen-parallel rivers and provide key chronological constraints on landscape evolution during the Early Miocene phase of the Himalayan orogeny

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

Modality and uncertainty in data visualizations : A corpus approach to the use of connecting lines

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