Biological and (Bio)Medical Complexity

English

Emergence of highly connected, non-redundant micro-RNAs in breast cancer transcriptional networks and their role in the control of biological functions

Breast cancer is one of the leading causes of death for women worldwide. Alterations to the gene regulatory programs lead to perturbed gene expression patterns that in turn generate major deregulations in cellular processes in the cancer phenotype. Among the many mechanisms of gene regulation described, micro-RNA regulation is widely understood to have an important role in the onset and development of breast cancer. Identifying the functional implications of micro-RNA regulation may lead to new strategies for the treatment of this disease.

Warnings and caveats in brain controllability

A recent article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their “controllability”, drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain networks provides an explanation for the types of control roles that different regions play in the brain. In this work, we first briefly review the framework of control theory applied to complex networks.

Complex network theory and methods to control the burden of cardiovascular disease: An exploratory study based on the CARDIA dataset

We are currently conducting a preliminary study to examine the feasibility of employing complex network methods in the analysis of the CARDIA dataset and will present our preliminary findings. Our main hypothesis is that network analysis can reveal information on how different combinations of risk factors can be assessed at a larger scale.

Simultaneous Emergence of Cooperative Response and Mutational Robustness in Gene Regulatory Networks

Gene regulatory networks (GRN) are complex systems in which many genes mutually regulate the expression by the transcription factors for changing the cell state adaptively to the change of environmental conditions. Living systems have gained such adaptive responses through the Darwinian evolution and thus the interactions between genes are not at all random interactions. In this respect, GRNs utilized by life are very rare among “all the possible GRNs”.

Optimal information networks: Application for data-driven integrated health in populations

Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables, instead considering only individual correlations. In addition, a unified method for assessing integrated health statuses of populations is lacking, making systematic comparison among populations impossible.

Unified Mechanism of Atrial Fibrillation in a Simple Model

Atrial fibrillation (AF) is the world’s most common cardiac arrhythmia effecting millions of people worldwide, yet the disease has resisted a full mechanistic explanation for over one hundred years. When the heart is beating normally, each contraction is carefully coordinated by a planar electrical wavefront passing through the muscle from top to bottom. However, these wavefronts can be spontaneously disrupted resulting in the chaotic motion of wavefronts and a loss of heart coordination.

Viruses are Driving Agents in the Human Molecular System

Network controllability integrates control theory concepts with complex networks in an attempt to identify optimal control strategies in disparate systems from natural to engineering fields. However, it is unclear what control means in most biological systems. To address this limitation, we use viral infection as a paradigm to model control in an infected cell using available HIV-1 host-interaction data in the context of the human molecular interactome comparing two dynamic states: normal and infected (Fig. 1A).

Complex Healthcare Process Predictive Modelling for Decision Making and Process Control

Healthcare process usually involves multiple aspects (disease development, treatment application, economics, logistics and many others) and actors (patients, physicians, hospital staff, visitors, etc.). While processing of patient flow, the process evolves in many scales (from city to single patient), undergoes various external processes and environmental conditions (weather, ecology, traffic). The analysis and predictive modeling of such complex process are important for decision making both within the treatment of a single patient and in a context of process control and policymaking.

A minimally invasive neurostimulation method for controlling abnormal synchronisation in the neuronal activity

Synchronization plays an important role in most of the neuronal activities and in particular in the control of the motor system. However, due to biochemical dysfunction in the brain activity, an abnormal and excessive synchronization may occur being responsible for severe symptoms of several neurological diseases. For the case of Parkinson's disease, for instance, an insufficient dopamine production in the basal ganglia causes rigidity or continuous tremors. In the case of epilepsy instead, for still unknown reasons strong unpredictable seizures often occur.

Towards understanding the multi-level complexity of human health: from drug interactions to human sexual behavior

The factors that affect human health occur at several scales, from gene and protein interactions, to the cellular, organism, and social levels. Understanding and controlling human health is especially complex due to the inter-level interactions that cannot be integrated away and thus form true control hierarchies [1]. Yet, the fact that multiple levels participate in the co-regulation of human health, enables us to measure and study it from other levels beyond the molecular.

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