Christian Darabos

The multiscale backbone of the human phenotype network based on biological pathways

BackgroundNetworks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes.ResultsThe resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle.ConclusionsWe unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases’ common biology, and in the elaboration of diagnosis and treatments.

Performance And Robustness Of Cellular Automata Computation On Irregular Networks

We investigate the performances of collective task-solving capabilities and the robustness of complex networks of automata using the density and synchronization problems as typical cases. We show by computer simulations that evolved Watts–Strogatz small-world networks have superior performance with respect to several kinds of scale-free graphs. In addition, we show that Watts–Strogatz networks are as robust in the face of random perturbations, both transient and permanent, as configuration scale-free networks, while being widely superior to Barabasi–Albert networks. This result differs from information diffusion on scale-free networks, where random faults are highly tolerated by similar topologies.

Additive Functions in Boolean Models of Gene Regulatory Network Modules

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genomes evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

Semi-synchronous activation in scale-free boolean networks

We study the dynamics of Boolean networks of the scale-free type. The model takes into account the topology and abstracts recent findings about real genetic regulatory networks. We propose a new, more biologically plausible, semi-synchronous update scheme on networks of larger sizes. We simulate statistical ensembles of networks and discuss the attractors of the dynamics, showing that it is compatible with theoretical biological network models. Moreover, then model demonstrates interesting scaling abilities as the size of the networks is increased.

Evolution of Small-World Networks of Automata for Computation

We study an extension of cellular automata to arbitrary interconnection topologies for the majority problem. By using an evolutionary algorithm, we show that small-world network topologies consistently evolve from regular and random structures without being designed beforehand. These topologies have better performance than regular lattice structures and are easier to evolve, which could explain in part their ubiquity.

Scale-Free automata networks are not robust in a collective computational task

We investigate the performances and collective task-solving capabilities of complex networks of automata using the density problem as a typical case We show by computer simulations that evolved Watts–Strogatz small-world networks have superior performance with respect to scale-free graphs of the Albert–Barabasi type Besides, Watts–Strogatz networks are much more robust in the face of transient uniformly random perturbations This result differs from information diffusion on scale-free networks, where random faults are highly tolerated.

Studying the Genetics of Complex Disease With Ancestry-Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations

Genome‐wide association studies (GWAS) have led to the discovery of over 200 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes mellitus (T2DM). Additionally, East Asians develop T2DM at a higher rate, younger age, and lower body mass index than their European ancestry counterparts. The reason behind this occurrence remains elusive. With comprehensive searches through the National Human Genome Research Institute (NHGRI) GWAS catalog literature, we compiled a database of 2,800 ancestry‐specific SNPs associated with T2DM and 70 other related traits. Manual data extraction was necessary because the GWAS catalog reports statistics such as odds ratio and P‐value, but does not consistently include ancestry information. Currently, many statistics are derived by combining initial and replication samples from study populations of mixed ancestry. Analysis of all‐inclusive data can be misleading, as not all SNPs are transferable across diverse populations. We used ancestry data to construct ancestry‐specific human phenotype networks (HPN) centered on T2DM. Quantitative and visual analysis of network models reveal the genetic disparities between ancestry groups. Of the 27 phenotypes in the East Asian HPN, six phenotypes were unique to the network, revealing the underlying ancestry‐specific nature of some SNPs associated with T2DM. We studied the relationship between T2DM and five phenotypes unique to the East Asian HPN to generate new interaction hypotheses in a clinical context. The genetic differences found in our ancestry‐specific HPNs suggest different pathways are involved in the pathogenesis of T2DM among different populations. Our study underlines the importance of ancestry in the development of T2DM and its implications in pharmocogenetics and personalized medicine.

Genome-wide genetic interaction analysis of glaucoma using expert knowledge derived from human phenotype networks.

The large volume of GWAS data poses great computational challenges for analyzing genetic interactions associated with common human diseases. We propose a computational framework for characterizing epistatic interactions among large sets of genetic attributes in GWAS data. We build the human phenotype network (HPN) and focus around a disease of interest. In this study, we use the GLAUGEN glaucoma GWAS dataset and apply the HPN as a biological knowledge-based filter to prioritize genetic variants. Then, we use the statistical epistasis network (SEN) to identify a significant connected network of pairwise epistatic interactions among the prioritized SNPs. These clearly highlight the complex genetic basis of glaucoma. Furthermore, we identify key SNPs by quantifying structural network characteristics. Through functional annotation of these key SNPs using Biofilter, a software accessing multiple publicly available human genetic data sources, we find supporting biomedical evidences linking glaucoma to an array of genetic diseases, proving our concept. We conclude by suggesting hypotheses for a better understanding of the disease.

A bipartite network approach to inferring interactions between environmental exposures and human diseases.

Environmental exposure is a key factor of understanding health and diseases. Beyond genetic propensities, many disorders are, in part, caused by human interaction with harmful substances in the water, the soil, or the air. Limited data is available on a disease or substance basis. However, we compile a global repository from literature surveys matching environmental chemical substances exposure with human disorders. We build a bipartite network linking 60 substances to over 150 disease phenotypes. We quantitatively and qualitatively analyze the network and its projections as simple networks. We identify mercury, lead and cadmium as associated with the largest number of disorders. Symmetrically, we show that breast cancer, harm to the fetus and non-Hodgkins lymphoma are associated with the most environmental chemicals. We conduct statistical analysis of how vertices with similar characteristics form the network interactions. This dyadicity and heterophilicity measures the tendencies of vertices with similar properties to either connect to one-another. We study the dyadic distribution of the substance classes in the networks show that, for instance, tobacco smoke compounds, parabens and heavy metals tend to be connected, which hint at common disease causing factors, whereas fungicides and phytoestrogens do not. We build an exposure network at the systems level. The information gathered in this study is meant to be complementary to the genome and help us understand complex diseases, their commonalities, their causes, and how to prevent and treat them.

Cellular Automata Coevolution of Update Functions and Topologies: A Tradeoff between Accuracy and Speed

Biological organisms have the ability to develop novel phenotypes in response to environmental changes. When several traits are evolved simultaneously or as a result of one another, we talk of coevolution. Cellular Automata (CAs) have been successfully used to artificially evolve problem specific update functions. The resulting CAs are, however, much slower and more sensitive to perturbations than those with an evolved underlying topology and fixed uniform update rule. Unfortunately, these are not nearly as accurate, and suffer from scaling up the total number of cells. We propose a hybrid paradigm that simultaneously coevolves the supporting network and the update functions of CAs. The resulting systems combine the higher fitness and performance of the update evolution and the robustness properties and speed of the topology evolution CAs. Moreover, these systems seem to perform better as the size of the CA scales up, where as single-feature evolution systems are negatively impacted. Coevolution in CAs is an interesting tradeoff between the two single trait evolutions.