Abstract: In order to correctly divide, cells have to move all their chromosomes at the center, a process known as congression. This task is performed by the combined action of molecular motors and randomly growing and shrinking microtubules. Chromosomes are captured by growing microtubules and transported by motors using the same microtubules as tracks (1). Coherent motion occurs as a result of a large collection of random and deterministic dynamical events. Understanding this process is important since a failure in chromosome segregation can lead to chromosomal instability one of the hallmarks of cancer. We describe this complex process in a three dimensional computational model involving thousands of microtubules. The results show that coherent and robust chromosome congression can only happen if the total number of microtubules is neither too small, nor too large. Our results allow for a coherent interpretation a variety of biological factors already associated in the past with chromosomal instability and related pathological conditions (2). (1) Z. Bertalan et al. Navigation Strategies of Motor Proteins on Decorated Tracks PLoS One 10 e0136945 (2015) (2) Z. Bertalan et al. Role of the Number of Microtubules in Chromosome Segregation during Cell Divison, PLoS One, 10 e0141305 (2015).

Abstract: The phenomenon of “human aging” is a matter which has implications for society, the economy and policymaking. The global proportion of elderly people (defined as those above the age of 60), standing at 11.7% in 2013, is projected to reach 21.1% by 2050. Multiple nations worldwide facing aging populations also face concomitant economic and social pressures. Facing the challenges associated with an aging population requires a holistic approach to human aging, as it spans multiple domains (such as physical, metabolic, immunological, cognitive, psychological and social aspects). Here we report a network-theoretic procedure to characterize the inter-associations among the biomarkers obtained from Singapore Longitudinal Aging Study (SLAS)-2 elderly cohort (N = 3270), as well as evaluate strategies to obtain a small subset of representative biomarkers from the larger biomarker set of 1581 variables. From a network built from calculations of statistically-significant pairwise effect sizes between biomarker variables, we obtain a minimum spanning tree consisting of 1373 variables from the network’s giant cluster (the rest being singletons), and apply the Louvain maximum-modularity community-detection algorithm on the tree which are composed of biomarkers which are highly-associated with each other. We examine the thematic similarities to group the clusters into higher-order thematic groups. We also compare the performance of various machine learning models in predicting SAGE, a multi-modal index of successful or unsuccessful aging, as opposed to using the entire complement of biomarkers. The procedures proposed in this work simultaneously considers both numerical and categorical biomarkers (something not done previously to our knowledge). Furthermore, the results we obtained here are important in both the characterization of a group of elderly people, establishing a hierarchy of importance among their biomarkers, as well as obtaining candidate subsets of biomarkers for measurement and evaluation, something which requires less time and resources compared to obtaining the full set.

Abstract: We consider the model of Boolean genetic regulatory networks named GPBN established in [1]. A GPBN is a directed graph with two different classes of nodes; G and P, representing genes and proteins respectively. For every node we consider only states 0 or 1 (0 means inactive, 1 active). Each gene is strictly linked with a unique specific protein P, but a set of different proteins may influence the activation (or inactivation) of a given gene. The novelty of this model consists that each active protein will remain active throughout a fixed delay of time steps. In the classic Boolean network the delays are one for every node. Given a GPBN with N nodes and a set of delays (dti≥1; i=1,..., N) we prove that its dynamics is equivalent to a usual Boolean network (without delays) with N + SUM (dti) nodes. Furthermore, for the class of disjunctive Boolean networks (i.e., at each node the local activation is an OR function) we prove, by using the previous equivalence, that any GPBN admits only fixed points in spite of the fact that when this class of networks is updated like the usual ones (delays equal to one) they may have limit cycles with super-polynomial periods. Finally, we illustrate the behavior of GPBN by studying the dynamics of one-dimensional elementary cellular automata. Roughly, we observe, from exhaustive simulations, that the majority of the 256 elementary automata converge to fixed point or to confined limit cycles, from that we may conclude that the information transmission in automata with delays is unusual. [1] A. Graudenzi, R. Serra, M. Villani, C. Damiani, A. Colacci, and S. Kauffman. Dynamical properties of a boolean model of gene regulatory network with memory. Journal of Computational Biology, 18:1291–1303, 2011.

Abstract: The human brain is composed of anatomically defined regions characterized by diverse histological, structural and functional connectivity profiles [1]. Previous work showed that genes that are consistently highly expressed across subjects show correlations to both brain structure and function, strongly suggesting a crucial role of differential transcription in modulating the genetic expression patterns across different regions, thus producing canonical gene-specific signatures for brain modules[2]. In this contribution, we study the whole genetic expression signatures of all regions across six subjects from the Allen Human Brain Atlas[1]. We produce an individual topological network of genes co-expression, akin to a coarse-grained backbone, via an extension of the topological simplification algorithm Mapper[3]. This new topological backbone is obtained by slicing the whole sample space, obtaining local clusters and then glueing them together according to a set-overlapping rule. This transformation solves the analysis problems caused by the combined properties of high-dimensionality, due to the large number of genes studied (~30k), and the relative sparsity of the samples (a few hundreds per subject). The resulting backbone preserves the shape of the original dataset while strongly reducing its dimensionality and yields a notion of network connectivity across the gene expression samples. We find that samples from known anatomical modules localize coherently on the backbone occupying almost non overlapping subnetworks formed by compact connected components. This reveals both the spatial architecture of gene (co)expression,as well as the interactions between the different modules. These subnetworks can provide maps to understand the interactions between the genetic pathways of neurotransmitters, an all important step in understanding the complex chemical interactions in the brain. For example, how a pharmaceutical interventions,that target a specific subsystem, such as anti-psychotic targeting the dopamine system, will impact the other sub-systems. 1.Hawrylycz,M.J. et al. Nature 489, 391–399(2013). 2.Hawrylycz,M.J. et al. 18, 1832–1844(2015). 3.Singh,G.,Mémoli,F. & Carlsson,G.E. SPBG(2007).