7,464 research outputs found
Modeling and evolving biochemical networks: insights into communication and computation from the biological domain
This paper is concerned with the modeling and evolving
of Cell Signaling Networks (CSNs) in silico. CSNs are
complex biochemical networks responsible for the coordination of cellular activities. We examine the possibility to computationally evolve and simulate Artificial Cell Signaling Networks (ACSNs) by means of Evolutionary Computation techniques. From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. For example, understanding some inherent properties of CSNs such as crosstalk may be of interest: A potential benefit of engineering crosstalking systems is that it allows the modification of a specific process according to the state of other processes in the system. This is clearly necessary in order to achieve complex control tasks. This work may also contribute to the biological understanding of the origins and evolution of real CSNs. An introduction to CSNs is first
provided, in which we describe the potential applications
of modeling and evolving these biochemical networks in
silico. We then review the different classes of techniques to model CSNs, this is followed by a presentation of two alternative approaches employed to evolve CSNs within the
ESIGNET project. Results obtained with these methods
are summarized and discussed
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
Incorporating molecular data in fungal systematics: a guide for aspiring researchers
The last twenty years have witnessed molecular data emerge as a primary
research instrument in most branches of mycology. Fungal systematics, taxonomy,
and ecology have all seen tremendous progress and have undergone rapid,
far-reaching changes as disciplines in the wake of continual improvement in DNA
sequencing technology. A taxonomic study that draws from molecular data
involves a long series of steps, ranging from taxon sampling through the
various laboratory procedures and data analysis to the publication process. All
steps are important and influence the results and the way they are perceived by
the scientific community. The present paper provides a reflective overview of
all major steps in such a project with the purpose to assist research students
about to begin their first study using DNA-based methods. We also take the
opportunity to discuss the role of taxonomy in biology and the life sciences in
general in the light of molecular data. While the best way to learn molecular
methods is to work side by side with someone experienced, we hope that the
present paper will serve to lower the learning threshold for the reader.Comment: Submitted to Current Research in Environmental and Applied Mycology -
comments most welcom
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