A dataset for illuminant- and device- invariant colour barcode decoding with cameras.

Barcodes are visual representations of data widely used in commerce and administration to compactly codify information about objects, services, and people. Specifically, a barcode is an image composed of parallel lines, with different widths, spacing and sizes. Generally, the lines are dark (usually black) on a bright background (usually white) or vice-versa.

How the latent geometry of a biological network provides information on its dynamics: the case of the gene network of chronic myeloid leukaemia.

The concept of the latent geometry of a network that can be represented as a graph has emerged from the classrooms of mathematicians and theoretical physicists to become an indispensable tool for determining the structural and dynamic properties of the network in many application areas, including contact networks, social networks, and especially biological networks. It is precisely latent geometry that we discuss in this article to show how the geometry of the metric space of the graph representing the network can influence its dynamics. We considered the transcriptome network of the Chronic Myeloid Laeukemia K562 cells.

Graph embedding and geometric deep learning relevance to network biology and structural chemistry.

Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g.

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.

Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network.

Dynamic Modelling of DNA Repair Pathway at the Molecular Level: A New Perspective.

DNA is the genetic repository for all living organisms, and it is subject to constant changes caused by chemical and physical factors. Any change, if not repaired, erodes the genetic information and causes mutations and diseases. To ensure overall survival, robust DNA repair mechanisms and damage-bypass mechanisms have evolved to ensure that the DNA is constantly protected against potentially deleterious damage while maintaining its integrity.

Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG.

This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML.

Identification of Potential Leukocyte Biomarkers Related to Drug Recovery of CFTR: Clinical Applications in Cystic Fibrosis.

The aim of this study was the identification of specific proteomic profiles, related to a restored cystic fibrosis transmembrane conductance regulator (CFTR) activity in cystic fibrosis (CF) leukocytes before and after ex vivo treatment with the potentiator VX770. We used leukocytes, isolated from CF patients carrying residual function mutations and eligible for Ivacaftor therapy, and performed CFTR activity together with proteomic analyses through micro-LC-MS. Bioinformatic analyses of the results obtained revealed the downregulation of proteins belonging to the leukocyte transendothelial migration and regulation of actin cytoskeleton pathways when CFTR activity was rescued by VX770 treatment.

Control Theory and Cancer Chemotherapy: How They Interact.

Control theory arises in most modern real-life applications, not least in biological and medical applications. In particular, in biological and medical contexts, the role of control theory began to take shape in the early 1980s when the first works appeared on the application of control theory in models of pharmacokinetics and pharmacodynamics for antitumor therapies. Forty years after those first works, the theory of control continues to be considered a mathematical analysis tool of extreme importance and usefulness, but the challenges it must overcome in order to manage the complexity of biological processes are in fact not yet overcome.

Composite nanocellulose-based hydrogels with spatially oriented degradation and retarded release of macromolecules.

The oral delivery of macromolecular therapeutics to the intestinal tract requires novel, robust, and controlled formulations. Here, we report on fabrication by molding of composite hydrogel cylinders made of cellulose nanocrystals (CNCs) and chitosan (Cht) and their performance as delivery vehicles. CNCs provide excellent mechanical and chemical stress resistance, whereas Cht allows scaffold degradation by enzyme digestion.

On TD-WGcluster: Theoretical Foundations and Guidelines for the User.

We review the TD-WGcluster (time delayed weighted edge clustering) software integrating static interaction networks with time series data in order to detect modules of nodes between which the information flows at similar time delays and intensities. The software has represented an advancement of the state of the art in the software for the identification of connected components due to its peculiarity of dealing with direct and weighted graphs, where the attributes of the physical entities represented by nodes vary over time. This chapter aims to deepen those theoretical aspects of the clustering model implemented by TD-WGcluster that may be of greater interest to the user.

Identifying necessary and sufficient conditions for the observability of models of biochemical processes.

The notions of observability and controllability of non-linear systems are a cornerstone of mathematical control theory and cover a wide scope of applications including process design, characterization, monitoring and control. Synthetic biology - which aims to (re)-program living functionalities - and bio-based process engineering - which aims to develop biotechnological manufacturing processes based on industrial and natural living agents - remarkably benefit of methodological improvements inspired to control theory for countless reasons including the huge variety of control mechanisms in living organisms, experimental limitations in terms of measurement feasibility, design of controllers - at single cell or population level - of synthetic production processes and process optimization purposes. Many fundamental problems of control theory such as stabilisability of unstable systems and optimal control may be solved under the assumption that the system is observable/controllable.

A Reaction-Based Model of the State Space of Chemical Reaction Systems Enables Efficient Simulations.

The choice of the state space representation of a system can turn into a prominent advantage or burden in any endeavour to mathematically model dynamical systems since it entails the analytical tractability of the related modelling formalism and the efficiency of the numerical computation. The Reaction-Based Model (RBM) of the state space, which is presented in this article, is a novel formalization of the kinetics of a system of interacting molecules. According to our representation, the state S of a system of M reactions and N molecular species, is identified with the occurrence of the reaction R ( μ = 1, .

How Monte Carlo heuristics aid to identify the physical processes of drug release kinetics.

We implement a Monte Carlo heuristic algorithm to model drug release from a solid dosage form. We show that with Monte Carlo simulations it is possible to identify and explain the causes of the unsatisfactory predictive power of current drug release models. It is well known that the power-law, the exponential models, as well as those derived from or inspired by them accurately reproduce only the first 60% of the release curve of a drug from a dosage form.

Hybrid deterministic/stochastic simulation of complex biochemical systems.

In a biological cell, cellular functions and the genetic regulatory apparatus are implemented and controlled by complex networks of chemical reactions involving genes, proteins, and enzymes. Accurate computational models are indispensable means for understanding the mechanisms behind the evolution of a complex system, not always explored with wet lab experiments. To serve their purpose, computational models, however, should be able to describe and simulate the complexity of a biological system in many of its aspects.

Time Series Analysis of the Sporulation Network Reveals Low Dimensional Chaotic Dynamics.

Chaotic behavior refers to a behavior which, albeit irregular, is generated by an underlying deterministic process. Therefore, a chaotic behavior is potentially controllable. This possibility becomes practically amenable especially when chaos is shown to be low-dimensional, i.

Network-Oriented Approaches to Anticancer Drug Response.

A long-standing paradigm in drug discovery has been the concept of designing maximally selective drugs to act on individual targets considered to underlie a disease of interest. Nonetheless, although some drugs have proven to be successful, many more potential drugs identified by the "one gene, one drug, one disease" approach have been found to be less effective than expected or to cause notable side effects. Advances in systems biology and high-throughput in-depth genomic profiling technologies along with an analysis of the successful and failed drugs uncovered that the prominent factor to determine drug sensitivity is the intrinsic robustness of the response of biological systems in the face of perturbations.

Accurate prediction of the age incidence of chronic myeloid leukemia with an improved two-mutation mathematical model.

Chronic myeloid leukemia (CML) is a malignant clonal disorder whose hallmark is a reciprocal translocation between chromosomes 9 and 22 occurring in 95% of affected patients. This translocation causes the expression of a deregulated BCR/ABL fusion oncogene and, interestingly, is detectable in healthy individuals. Based on this information we assumed that the sole presence of the BCR/ABL transcript represents a necessary but not sufficient event for the clonal expansion of CML precursors.

Defining order and timing of mutations during cancer progression: the TO-DAG probabilistic graphical model.

Somatic mutations arise and accumulate both during tumor genesis and progression. However, the order in which mutations occur is an open question and the inference of the temporal ordering at the gene level could potentially impact on patient treatment. Thus, exploiting recent observations suggesting that the occurrence of mutations is a non-memoryless process, we developed a computational approach to infer timed oncogenetic directed acyclic graphs (TO-DAGs) from human tumor mutation data.

Detecting modules in biological networks by edge weight clustering and entropy significance.

Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes.

SPOP mutation leads to genomic instability in prostate cancer.

Genomic instability is a fundamental feature of human cancer often resulting from impaired genome maintenance. In prostate cancer, structural genomic rearrangements are a common mechanism driving tumorigenesis. However, somatic alterations predisposing to chromosomal rearrangements in prostate cancer remain largely undefined.

Stochastic chemical kinetics : A review of the modelling and simulation approaches.

A review of the physical principles that are the ground of the stochastic formulation of chemical kinetics is presented along with a survey of the algorithms currently used to simulate it. This review covers the main literature of the last decade and focuses on the mathematical models describing the characteristics and the behavior of systems of chemical reactions at the nano- and micro-scale. Advantages and limitations of the models are also discussed in the light of the more and more frequent use of these models and algorithms in modeling and simulating biochemical and even biological processes.

Methods of biological network inference for reverse engineering cancer chemoresistance mechanisms.

We review recent Bayesian network inference methodologies we developed to infer genetic and metabolic pathways associated to oncological drug chemoresistance. Bayesian inference is supported by a rigorous and widely accepted mathematical formalization of predictive analytics. It is an inherently integrative approach allowing the incorporation of prior knowledge and constraints.