Algorithms for Biological Network Modelling

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 2669

Special Issue Editor

Department of Mathematics, University of Exeter, Exeter EX4 4QE, UK
Interests: mathematical and computational biology; machine learning and optimization; nonlinear dynamics; scientific computing

Special Issue Information

Dear Colleagues,

Computational models of biological networks have come to occupy a central role in quantitative biology and medicine, enabling precise predictions to be made and tested regarding the effects of chemical and genetic perturbations. However, a key bottleneck in constructing predictive models is the process of calibrating parameters to experimental data, particularly in view of the exponential increase in model parameters with system size.

This Special Issue will bring together cutting-edge methods from diverse computational disciplines that are currently being employed to address this important problem. Of particular interest are (i) methods from evolutionary computing to efficiently explore and characterize the design space (e.g., evolutionary algorithms, multi-objective optimisation, surrogate-assisted optimisation, landscape-aware heuristic search) and (ii) methods from applied mathematics to construct model formulations that reduce complexity whilst preserving predictive capacity (e.g., logic-based models).

We encourage submissions across a broad range of application domains, including—but not restricted to—gene regulatory network modelling, metabolic modelling, synthetic biology and computational neuroscience.

Dr. Ozgur Akman
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational biology
  • evolutionary algorithms
  • surrogate models
  • multi-objective optimization
  • landscape analysis
  • local optima networks

Published Papers (1 paper)

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Review

14 pages, 2585 KiB  
Review
Approaches to Parameter Estimation from Model Neurons and Biological Neurons
by Alain Nogaret
Algorithms 2022, 15(5), 168; https://0-doi-org.brum.beds.ac.uk/10.3390/a15050168 - 20 May 2022
Cited by 1 | Viewed by 1763
Abstract
Model optimization in neuroscience has focused on inferring intracellular parameters from time series observations of the membrane voltage and calcium concentrations. These parameters constitute the fingerprints of ion channel subtypes and may identify ion channel mutations from observed changes in electrical activity. A [...] Read more.
Model optimization in neuroscience has focused on inferring intracellular parameters from time series observations of the membrane voltage and calcium concentrations. These parameters constitute the fingerprints of ion channel subtypes and may identify ion channel mutations from observed changes in electrical activity. A central question in neuroscience is whether computational methods may obtain ion channel parameters with sufficient consistency and accuracy to provide new information on the underlying biology. Finding single-valued solutions in particular, remains an outstanding theoretical challenge. This note reviews recent progress in the field. It first covers well-posed problems and describes the conditions that the model and data need to meet to warrant the recovery of all the original parameters—even in the presence of noise. The main challenge is model error, which reflects our lack of knowledge of exact equations. We report on strategies that have been partially successful at inferring the parameters of rodent and songbird neurons, when model error is sufficiently small for accurate predictions to be made irrespective of stimulation. Full article
(This article belongs to the Special Issue Algorithms for Biological Network Modelling)
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