Biological networks 101: Computational modeling for molecular biologists

Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that prevent their broad adoption and effective use by molecular biologists. This study clarifies the basic aspects of molecular modeling, how to convert data into useful input, as well as the number of time points and molecular parameters that should be considered for molecular regulatory models with both explanatory and predictive potential. We illustrate the necessary experimental preconditions for converting data into a computational model of network dynamics. This model requires neither a thorough background in mathematics nor precise data on intracellular concentrations, binding affinities or reaction kinetics. Finally, we show how an interactive model of crosstalk between signal transduction pathways in primary human articular chondrocytes allows insight into processes that regulate gene expression.     

Cell signaling networks ontology

Although databases for cell signaling pathways include numbers of reaction data of the pathways, the reaction data cannot be used yet to deduce biological functions from them. For the deduction, we need systematic and consistent interpretation of biological functions of reactions in cell signaling pathways in the context of "information transmission". To address this issue, we have developed a functional ontology for cell signaling pathways, Cell Signaling Network Ontology (CSN-Ontology), which provides framework for the functional interpretation presenting some important concepts as information, selectivity, movability, and signaling rules including passage of time.     

Cell biologists sort things out: Analysis and purification of intracellular organelles by flow cytometry

Flow cytometry was established originally for measuring DNA content and for the analysis of cell-surface markers in combination with cell sorting. During the past two decades, it has added new dimensions to various areas of immunology and medicine. Increased sensitivity and precision of flow cytometers, accompanied by the development of new fluorescent dyes and probes, has led to new applications in molecular cell biology and genetics. This article focuses on applications of flow cytometry in analysis and sorting of intracellular organelles.     

State-transition diagrams for biologists

It is clearly in the tradition of biologists to conceptualize the dynamical evolution of biological systems in terms of state-transitions of biological objects. This paper is mainly concerned with (but obviously not limited too) the immunological branch of biology and shows how the adoption of UML (Unified Modeling Language) state-transition diagrams can ease the modeling, the understanding, the coding, the manipulation or the documentation of population-based immune software model generally defined as a set of ordinary differential equations (ODE), describing the evolution in time of populations of various biological objects. Moreover, that same UML adoption naturally entails a far from negligible representational economy since one graphical item of the diagram might have to be repeated in various places of the mathematical model. First, the main graphical elements of the UML state-transition diagram and how they can be mapped onto a corresponding ODE mathematical model are presented. Then, two already published immune models of thymocyte behavior and time evolution in the thymus, the first one originally conceived as an ODE population-based model whereas the second one as an agent-based one, are refactored and expressed in a state-transition form so as to make them much easier to understand and their respective code easier to access, to modify and run. As an illustrative proof, for any immunologist, it should be possible to understand faithfully enough what the two software models are supposed to reproduce and how they execute with no need to plunge into the Java or Fortran lines.     

Natural insights for chemical biologists

Chemical biologists studying natural-product pathways encoded in genomes have unearthed new chemistry and insights into the evolution of biologically active metabolites.     

Geologists and biologists in palaeontology

Whereas zoopalaeontology is one-sidedly a geological science, palaeobotany is predominantly a science cultivated by biologists. Under the influence of increased application as a stratigraphical and palaeoenvironmentological tool in exploration geology as well as in pure geology, palaeopalynology is also becoming more and more a one-sidedly orientated field of science.     

OBO-Edit--an ontology editor for biologists

OBO-Edit is an open source, platform-independent ontology editor developed and maintained by the Gene Ontology Consortium. Implemented in Java, OBO-Edit uses a graph-oriented approach to display and edit ontologies. OBO-Edit is particularly valuable for viewing and editing biomedical ontologies. Availability: https://sourceforge.net/project/showfiles.php?group_id=36855.