71,125 research outputs found
Reactive models for biological regulatory networks
A reactive model, as studied by D. Gabbay and his collaborators,
can be regarded as a graph whose set of edges may be altered
whenever one of them is crossed. In this paper we show how reactive
models can describe biological regulatory networks and compare them
to Boolean networks and piecewise-linear models, which are some of the
most common kinds of models used nowadays. In particular, we show
that, with respect to the identification of steady states, reactive Boolean
networks lie between piecewise linear models and the usual, plain Boolean
networks. We also show this ability is preserved by a suitable notion of
bisimulation, and, therefore, by network minimisation.ERDF - The European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project POCI-01-0145-FEDER-030947. and project with reference UID/MAT/04106/2019 at CIDMA. D. Figueiredo also acknowledges the support given by FCT via the PhD scholarship PD/BD/114186/201
Systems biology in animal sciences
Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits
Modelling and simulation framework for reactive transport of organic contaminants in bed-sediments using a pure java object - oriented paradigm
Numerical modelling and simulation of organic contaminant reactive transport in the environment is being increasingly
relied upon for a wide range of tasks associated with risk-based decision-making, such as prediction of contaminant
profiles, optimisation of remediation methods, and monitoring of changes resulting from an implemented remediation
scheme. The lack of integration of multiple mechanistic models to a single modelling framework, however, has
prevented the field of reactive transport modelling in bed-sediments from developing a cohesive understanding of
contaminant fate and behaviour in the aquatic sediment environment. This paper will investigate the problems involved
in the model integration process, discuss modelling and software development approaches, and present preliminary
results from use of CORETRANS, a predictive modelling framework that simulates 1-dimensional organic contaminant
reaction and transport in bed-sediments
Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics
Linearized catalytic reaction equations modeling e.g. the dynamics of genetic
regulatory networks under the constraint that expression levels, i.e. molecular
concentrations of nucleic material are positive, exhibit nontrivial dynamical
properties, which depend on the average connectivity of the reaction network.
In these systems the inflation of the edge of chaos and multi-stability have
been demonstrated to exist. The positivity constraint introduces a nonlinearity
which makes chaotic dynamics possible. Despite the simplicity of such minimally
nonlinear systems, their basic properties allow to understand fundamental
dynamical properties of complex biological reaction networks. We analyze the
Lyapunov spectrum, determine the probability to find stationary oscillating
solutions, demonstrate the effect of the nonlinearity on the effective in- and
out-degree of the active interaction network and study how the frequency
distributions of oscillatory modes of such system depend on the average
connectivity.Comment: 11 pages, 5 figure
Tiling solutions for optimal biological sensing
Biological systems, from cells to organisms, must respond to the ever
changing environment in order to survive and function. This is not a simple
task given the often random nature of the signals they receive, as well as the
intrinsically stochastic, many body and often self-organized nature of the
processes that control their sensing and response and limited resources.
Despite a wide range of scales and functions that can be observed in the living
world, some common principles that govern the behavior of biological systems
emerge. Here I review two examples of very different biological problems:
information transmission in gene regulatory networks and diversity of adaptive
immune receptor repertoires that protect us from pathogens. I discuss the
trade-offs that physical laws impose on these systems and show that the optimal
designs of both immune repertoires and gene regulatory networks display similar
discrete tiling structures. These solutions rely on locally non-overlapping
placements of the responding elements (genes and receptors) that, overall,
cover space nearly uniformly.Comment: 11 page
A Model of the Cellular Iron Homeostasis Network Using Semi-Formal Methods for Parameter Space Exploration
This paper presents a novel framework for the modeling of biological
networks. It makes use of recent tools analyzing the robust satisfaction of
properties of (hybrid) dynamical systems. The main challenge of this approach
as applied to biological systems is to get access to the relevant parameter
sets despite gaps in the available knowledge. An initial estimate of useful
parameters was sought by formalizing the known behavior of the biological
network in the STL logic using the tool Breach. Then, once a set of parameter
values consistent with known biological properties was found, we tried to
locally expand it into the largest possible valid region. We applied this
methodology in an effort to model and better understand the complex network
regulating iron homeostasis in mammalian cells. This system plays an important
role in many biological functions, including erythropoiesis, resistance against
infections, and proliferation of cancer cells.Comment: In Proceedings HSB 2012, arXiv:1208.315
Modeling reactivity to biological macromolecules with a deep multitask network
Most
small-molecule drug candidates fail before entering the market,
frequently because of unexpected toxicity. Often, toxicity is detected
only late in drug development, because many types of toxicities, especially
idiosyncratic adverse drug reactions (IADRs), are particularly hard
to predict and detect. Moreover, drug-induced liver injury (DILI)
is the most frequent reason drugs are withdrawn from the market and
causes 50% of acute liver failure cases in the United States. A common
mechanism often underlies many types of drug toxicities, including
both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes
into reactive metabolites, which then conjugate to sites in proteins
or DNA to form adducts. DNA adducts are often mutagenic and may alter
the reading and copying of genes and their regulatory elements, causing
gene dysregulation and even triggering cancer. Similarly, protein
adducts can disrupt their normal biological functions and induce harmful
immune responses. Unfortunately, reactive metabolites are not reliably
detected by experiments, and it is also expensive to test drug candidates
for potential to form DNA or protein adducts during the early stages
of drug development. In contrast, computational methods have the potential
to quickly screen for covalent binding potential, thereby flagging
problematic molecules and reducing the total number of necessary experiments.
Here, we train a deep convolution neural networkî—¸the XenoSite
reactivity modelî—¸using literature data to accurately predict
both sites and probability of reactivity for molecules with glutathione,
cyanide, protein, and DNA. On the site level, cross-validated predictions
had area under the curve (AUC) performances of 89.8% for DNA and 94.4%
for protein. Furthermore, the model separated molecules electrophilically
reactive with DNA and protein from nonreactive molecules with cross-validated
AUC performances of 78.7% and 79.8%, respectively. On both the site-
and molecule-level, the model’s performances significantly
outperformed reactivity indices derived from quantum simulations that
are reported in the literature. Moreover, we developed and applied
a selectivity score to assess preferential reactions with the macromolecules
as opposed to the common screening traps. For the entire data set
of 2803 molecules, this approach yielded totals of 257 (9.2%) and
227 (8.1%) molecules predicted to be reactive only with DNA and protein,
respectively, and hence those that would be missed by standard reactivity
screening experiments. Site of reactivity data is an underutilized
resource that can be used to not only predict if molecules are reactive,
but also show where they might be modified to reduce toxicity while
retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity
Recommended from our members
Transcriptomic Profiles of Monocyte-Derived Macrophages in Response to Escherichia coli is Associated with the Host Genetics.
Reactive Nitrogen Species (RNS) are a group of bactericidal molecules produced by macrophages in response to pathogens in a process called oxidative burst. Nitric oxide (NO-) is a member of RNS produced from arginine by inducible Nitric Oxide Synthase (iNOS) enzyme. The activity of iNOS and production of NO- by macrophages following stimulation is one of the indicators of macrophage polarization towards M1/proinflammatory. Production of NO- by bovine monocyte-derived macrophage (MDM) and mouse peritoneal macrophages has been shown to be strongly associated with host genetic with the heritability of 0.776 in bovine MDM and 0.8 in mouse peritoneal macrophages. However, the mechanism of genetic regulation of macrophage response has remained less explored. In the current study, the transcriptome of bovine MDMs was compared between two extreme phenotypes that had been classified as high and low responder based on NO- production. The results showed that 179 and 392 genes were differentially expressed (DE) between high and low responder groups at 3 and 18 hours after exposure to Escherichia coli, respectively. A set of 11 Transcription Factors (TFs) (STAT1, IRF7, SPI1, STAT4, IRF1, HIF1A, FOXO3, REL, NFAT5, HIC1, and IRF4) at 3 hours and a set of 13 TFs (STAT1, IRF1, HIF1A, STAT4, ATF4, TP63, EGR1, CDKN2A, RBL1, E2F1, PRDM1, GATA3, and IRF4) at 18 hours after exposure to E. coli were identified to be differentially regulated between the high and low responder phenotypes. These TFs were found to be divided into two clusters of inflammatory- and hypoxia-related TFs. Functional analysis revealed that some key canonical pathways such as phagocytosis, chemotaxis, antigen presentation, and cell-to-cell signalling are enriched among the over-expressed genes by high responder phenotype. Based on the results of this study, it was inferred that the functional characteristics of bovine MDMs are associated with NO-based classification. Since NO- production is strongly associated with host genetics, this study for the first time shows the distinct proinflammatory profiles of macrophages are controlled by the natural genetic polymorphism in an outbred population. In addition, the results suggest that genetics can be considered as a new dimension in the current model of macrophage polarization which is currently described by the combination of stimulants, only
- …