Scale free topology as an effective feedback system

Mathematical models of learning and what can be learned from them

Learning is a multi-faceted phenomenon of critical importance and hence attracted a great deal of research, both experimental and theoretical. In this review, we will consider some of the paradigmatic examples of learning and discuss the common themes in theoretical learning research, such as levels of modeling and their corresponding relation to experimental observations and mathematical ideas common to different types of learning.

Systematic identification and characterization of long noncoding RNAs (lncRNAs) during Aedes albopictus development

Background

Aedes albopictus originated in the tropical forests of Southeast Asia and can currently be found on all continents. As one of the main arboviral vectors, the control of Ae. albopictus requires novel strategies, informed by a deep knowledge of its biology. Little is known regarding mosquito long noncoding RNAs (lncRNAs), which are transcripts longer than 200 nucleotides that lack protein-coding potential and have roles in developmental regulation.

Results

Based on RNA-seq data from five developmental time points, eggs, early larvae, late larvae, pupae, and adults (female and male) of Ae. albopictus, 21,414 lncRNAs were characterized in this study. Differential expression analysis revealed that lncRNAs exhibited developmental stage specificity. The expression of most lncRNAs was upregulated at the onset of metamorphosis developmental stages. More differentially expressed lncRNAs were observed between eggs and early larvae. Weighted gene co-expression network analysis (WGCNA) further confirmed that the expression patterns of lncRNAs were obviously correlated with specific developmental time points. Functional annotation using co-expression analysis revealed that lncRNAs may be involved in the regulation of metamorphic developmental transitions of Ae. albopictus. The hub lncRNAs and hub gene clusters were identified for each module that were highly associated with specific developmental time points.

Conclusions

The results of this study will facilitate future researches to elucidate the regulatory mechanisms of lncRNAs in the development of Ae. albopictus and utilize lncRNAs to assist with mosquito control.

Cancer progression as a learning process

Drug resistance and metastasis-the major complications in cancer-both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.

Information Thermodynamics and Reducibility of Large Gene Networks

Gene regulatory networks (GRNs) control biological processes like pluripotency, differentiation, and apoptosis. Omics methods can identify a large number of putative network components (on the order of hundreds or thousands) but it is possible that in many cases a small subset of genes control the state of GRNs. Here, we explore how the topology of the interactions between network components may indicate whether the effective state of a GRN can be represented by a small subset of genes. We use methods from information theory to model the regulatory interactions in GRNs as cascading and superposing information channels. We propose an information loss function that enables identification of the conditions by which a small set of genes can represent the state of all the other genes in the network. This information-theoretic analysis extends to a measure of free energy change due to communication within the network, which provides a new perspective on the reducibility of GRNs. Both the information loss and relative free energy depend on the density of interactions and edge communication error in a network. Therefore, this work indicates that a loss in mutual information between genes in a GRN is directly coupled to a thermodynamic cost, i.e., a reduction of relative free energy, of the system.