695 research outputs found
Mathematics at the eve of a historic transition in biology
A century ago physicists and mathematicians worked in tandem and established
quantum mechanism. Indeed, algebras, partial differential equations, group
theory, and functional analysis underpin the foundation of quantum mechanism.
Currently, biology is undergoing a historic transition from qualitative,
phenomenological and descriptive to quantitative, analytical and predictive.
Mathematics, again, becomes a driving force behind this new transition in
biology.Comment: 5 pages, 2 figure
Smell's puzzling discrepancy: Gifted discrimination, yet pitiful identification
Mind &Language, Volume 35, Issue 1, Page 90-114, February 2020
Combined logical and data-driven models for linking signalling pathways to cellular response
Background
Signalling pathways are the cornerstone on understanding cell function and predicting cell behavior. Recently, logical models of canonical pathways have been optimised with high-throughput phosphoproteomic data to construct cell-type specific pathways. However, less is known on how signalling pathways can be linked to a cellular response such as cell growth, death, cytokine secretion, or transcriptional activity.
Results
In this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible (i.e., construction of canonical pathways), whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines.
Conclusions
We developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion.Marie Curie International Reintegration Grants (MIRG-14-CT-2007-046531)Vertex Pharmaceuticals IncorporatedBundesministerium für Wissenschaft und Forschung (HepatoSys)Massachusetts Institute of Technology (Rockwell International Career Development Professorship)Bundesministerium für Wissenschaft und Forschung (HepatoSys 0313081D
The physics of development 100 years after D'Arcy Thompson's “on growth and form”
By applying methods and principles from the physical sciences to biological problems, D'Arcy Thompson's On Growth and Form demonstrated how mathematical reasoning reveals elegant, simple explanations for seemingly complex processes. This has had a profound influence on subsequent generations of developmental biologists. We discuss how this influence can be traced through twentieth century morphologists, embryologists and theoreticians to current research that explores the molecular and cellular mechanisms of tissue growth and patterning, including our own studies of the vertebrate neural tube
Applications of artificial neural networks (ANNs) in several different materials research fields
PhDIn materials science, the traditional methodological framework is the
identification of the composition-processing-structure-property causal pathways
that link hierarchical structure to properties. However, all the properties of
materials can be derived ultimately from structure and bonding, and so the
properties of a material are interrelated to varying degrees.
The work presented in this thesis, employed artificial neural networks (ANNs) to
explore the correlations of different material properties with several examples in
different fields. Those including 1) to verify and quantify known correlations
between physical parameters and solid solubility of alloy systems, which were
first discovered by Hume-Rothery in the 1930s. 2) To explore unknown crossproperty
correlations without investigating complicated structure-property
relationships, which is exemplified by i) predicting structural stability of
perovskites from bond-valence based tolerance factors tBV, and predicting
formability of perovskites by using A-O and B-O bond distances; ii) correlating
polarizability with other properties, such as first ionization potential, melting
point, heat of vaporization and specific heat capacity. 3) In the process of
discovering unanticipated relationships between combination of properties of
materials, ANNs were also found to be useful for highlighting unusual data
points in handbooks, tables and databases that deserve to have their veracity
inspected. By applying this method, massive errors in handbooks were found,
and a systematic, intelligent and potentially automatic method to detect errors in
handbooks is thus developed.
Through presenting these four distinct examples from three aspects of ANN
capability, different ways that ANNs can contribute to progress in materials
science has been explored. These approaches are novel and deserve to be pursued
as part of the newer methodologies that are beginning to underpin material
research
Review of QSAR Models and Software Tools for Predicting of Genotoxicity and Carcinogenicity
This review of QSARs for genotoxicity and carcinogenicity was performed in a broad sense, considering both models available in software tools and models that are published in the literature. The review considered the potential applicability of diverse models to pesticides as well as to other types of regulated chemicals and pharmaceuticals. The availability of models and information on their applicability is summarised in tables, and a range of illustrative or informative examples are described in more detail in the text. In many cases, promising models were identified but they are still at the research stage. For routine application in a regulatory setting, further efforts will be needed to explore the applicability of such models for specific purposes, and to implement them in a practically useful form (i.e. user-friendly software). It is also noted that a range of software tools are research tools suitable for model development, and these require more specialised expertise than other tools that are aimed primarily at end-users such as risk assessors. It is concluded that the most useful models are those which are implemented in software tools and associated with transparent documentation on the model development and validation process. However, it is emphasised that the assessment of model predictions requires a reasonable amount of QSAR knowledge, even if it is not necessary to be a QSAR practitioner.JRC.DG.I.6-Systems toxicolog
Combinatorial Drug Therapy for Cancer in the Post-Genomic Era.
Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy
Modeling and Simulation of Biological Systems through Electronic Design Automation techniques
Modeling and simulation of biological systems is a key requirement for integrating invitro and in-vivo experimental data. In-silico simulation allows testing different experimental conditions, thus helping in the discovery of the dynamics that regulate the system. These dynamics include errors in the cellular information processing that are responsible for diseases such as cancer, autoimmunity, and diabetes as well as drug effects to the system (Gonalves, 2013). In this context, modeling approaches can be classified into two categories: quantitative and qualitative models. Quantitative modeling allows for a natural representation of molecular and gene networks and provides the most precise prediction. Nevertheless, the lack of kinetic data (and of quantitative data in general) hampers its use for many situations (Le Novere, 2015). In contrast, qualitative models simplify the biological reality and are often able to reproduce the system behavior. They cannot describe actual concentration levels nor realistic time scales. As a consequence, they cannot be used to explain and predict the outcome of biological experiments that yield quantitative data. However, given a biological network consisting of input (e.g., receptors), intermediate, and output (e.g., transcription factors) signals, they allow studying the input-output relationships through discrete simulation (Samaga, 2013). Boolean models are gaining an increasing interest in reproducing dynamic behaviors, understanding processes, and predicting emerging properties of cellular signaling networks through in-silico experiments. They are emerging as a valid alternative to the quantitative approaches (i.e., based on ordinary differential equations) for exploratory modeling when little is known about reaction kinetics or equilibrium constants in the context of gene expression or signaling. Even though several approaches and software have been recently proposed for logic modeling of biological systems, they are limited to specific contexts and they lack of automation in analyzing biological properties such as complex attractors, and molecule vulnerability. This thesis proposes a platform based on Electronic Design Automation (EDA) technologies for qualitative modeling and simulation of Biological Systems. It aims at overtaking limitations that affect the most recent qualitative tools
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