Special Sessions

CIBB 2016 will host the following Special Sessions:

These will regard cutting-edge aspects of the rapidly evolving scientific context which CIBB refers to. Queries regarding each session shall be directed to the reference Contact.

Publication of Special Sessions papers

Accepted papers will be presented at the conference and will be published in proceedings for conference distribution. Authors (at least one) of accepted papers are expected to register and present their papers at the conference.

Extended and revised versions of the papers presented at CIBB 2016 will be invited for a post-conference monograph. This is traditionally published in the Springer series of Lecture Notes in Bioinformatics (LNBI) , (arrangements undergoing). Continuing the tradition of CIBB, we are planning to publish the best papers in one (or more, as appropriate) special issue of an international scientific journal (such as BMC Bioinformatics, in the latest editions).

Special session papers shall be formatted as regular papers.

Engineering Bio-interfaces and Rudimentary Cells as a way to develop Synthetic Biology

Aims and scope
The bioengineering has been fundamental in both regenerate medicine and the understanding of biochemical mechanisms involved in life appearance and maintenance. The aim of this special session is to bring together theoretical researchers interested in cutting-edge methods to address the challenges posed by the huge amount of data produced in omics sciences and in application to systems and synthetic biology and experimental researchers with interests on develop experimentally new approaches of synthetic biology for biomedical and biotechnological applications like implants, artificial organs, advanced medical systems, drug delivery systems and sensors. The track of this SS aims to present latest experimental advancements concerning synthetic biology. Relevant topics within this context include all the field of physical interactions between biological molecules, cell-nanomaterials interactions, molecular aspects of membrane assembly and transport, Communication between cells, biosensors at micro and nanoscales, drug delivery systems, liposomes and encapsulation of molecules, synaptic transmission, artificial organs and contractile systems.


  • Maria Raposo, Universidade Nova de Lisboa
  • Quirina Ferreira, Universidade de Lisboa
  • Andrea Antunes, Universidade Federal de Uberlândia
  • Patricia Targon, Campana Universidade de São Paulo
  • Roberto Marangoni, University of Pisa
  • Fabio Mavelli, University of Bari

PAPER SUBMISSION DEADLINE: 12th June 2016 (Extended!)

CONTACTS: Maria Raposo, mfr@fct.unl.pt

Biocuration and integration of biomedical databases

Aims and scope
A large multidisciplinary community of researchers have dedicated decades to structure biological and biomedical data and made them freely and easily available. There are many specialized databases on a variety of fields from protein structures to PPI, from metabolic pathways to micro RNA functions and so on. It is a cliché that the amount of stored data, in any area, is exponentially growing. This is especially true for the biomedical area where scientific results need to make use of the collaborative efforts of those who generate, format, and integrate the data. The large number of new different information and used terms makes a big challenge to interconnect results from heterogeneous data sources. This makes necessary to provide accurate data, tools, algorithms and managing platforms. Our special section of a “bioinformatics and biostatistics methods conference” welcomes original research and reviews contributions on the curation of novel data, their representation and mining.
List of topics:

  • Biological curation
  • Data Integration 

  • noSQL databases
  • Data Visualization
  • Data Annotation and standardization
  • Big Data analytics 


  • Rosalba Giugno, University of Verona
  • Giosue' Lo Bosco, University of Palermo
  • Alfredo Pulvirenti, University of Catania
  • Riccardo Rizzo, CNR-ICAR, Palermo

PAPER SUBMISSION DEADLINE: 12th June 2016 (Extended!)

CONTACTS: Riccardo Rizzo, ricrizzo@pa.icar.cnr.it

Modeling and simulation methods for Systems Biology and Systems Medicine

Aims and scope
Systems Biology deals with the analysis of natural systems at different scales of complexity, requiring completely different modeling frameworks and computational methods. Given that Systems Biology approaches are becoming well established, the challenge is now to apply the developed techniques towards the definition of personalized models in order to identify individually tailored drugs and treatments; i.e. to realize the Personalized Medicine paradigm. The scope of this special session is to bring together researchers involved in the development of methods applied to the fields of Systems Biology and Systems Medicine. Topics of interest include, but are not limited to:

  • analysis of robustness of cellular networks 

  • biomedical model parameterization
  • cancer progression models
  • clinical image analysis
  • emergent properties in complex biological systems 

  • flux balance analysis 

  • metabolic engineering 

  • metabolic pathway analysis 

  • model verification and refinement methods 

  • models of neural activity
  • multi­scale modelling and simulation of biological systems 

  • parameter estimation methods
  • personalized models
  • reverse engineering of reaction networks 

  • software tools for systems biology 

  • spatio­temporal modelling and simulation of biological systems


  • Paolo Cazzaniga, University of Bergamo
  • Marco S. Nobile, University of Milano-Bicocca
  • Chiara Damiani, University of Milano-Bicocca
  • Riccardo Colombo, University of Milano-Bicocca
  • Giancarlo Mauri, University of Milano-Bicocca

PAPER SUBMISSION DEADLINE: 12th June 2016 (Extended!)

CONTACTS: Paolo Cazzaniga, paolo.cazzaniga@unibg.it

High-Performance Computing and Deep learning methods for Genomic Data Analysis

Aims and scope
The unprecedented wealth of heterogeneous genomic data has generated an enormous demand for tools and methods to analyse and decipher the complexity of such large information. Genomics bursts on the scene with the most growing data, so much that the Genomic research community is now facing many of the scale-out issues that High-Performance Computing has been addressing for years: it requires powerful infrastructures with fast computing and storage capabilities, with substantial challenges regarding data processing, statistical analysis, and data representation. Traditional techniques and tools for data analytic and autonomous learning are no longer suitable — or even unusable — to extract human-interpretable knowledge and information from this significant amount of data. The aim of this special session is to present the latest advancements concerning High-Performance Computing solutions, deep learning and optimization algorithms required to manage the large-scale challenges outlined above — including related BigData aspects — and to foster the integration of researchers interested in HPC and Computational Biology.
Examples of topics of interest include, but are not limited to:

  • HPC applications for Bioinformatics
  • HPC architectures for Computational Biology
  • Parallel Machine Learning and Deep Learning approaches for Bioinformatics
  • Bioinformatics big data applications and MapReduce implementations
  • Next-Generation Sequencing data analysis and interpretation
  • Differential gene expression analysis and clustering techniques
  • Algorithms for genomic and proteomic
  • Genomic data visualisation


  • Zakaria Benmounah, Constantine 2 University, Algeria and University of Cambridge, UK
  • Filippo Spiga, University of Cambridge, UK
  • Fabio Tordini, University of Torino, Italy

PAPER SUBMISSION DEADLINE: 12th June 2016 (Extended!)

CONTACTS: Fabio Tordini, tordini@di.unito.it

Statistical inference in mechanistic models of biological systems

Aims and scope
Parameter inference in complex systems described by coupled differential equations (DEs) is a challenging problem arising in many scientific disciplines. Conventional adaptive inference methods involve repeatedly solving the DEs by numerical integration, which is computationally onerous and does not scale up to high dimensions. Aimed at reducing the computational costs, various new concepts based on gradient matching, Bayesian filtering, Baysian optimization and statistical emulation have been proposed in the computational statistics and machine learning literature. This is a vibrant area of methodological research, with a broad range of applications ranging from bioengineering to molecular systems biology. The objective of the special session is to review the current state of the art, discuss methodological avenues for method innovation, and demonstrate applications in real-world scenarios.

To be considered for a presentation, please submit a paper (formatted as a regular paper) that addresses the following criteria:

  1. Methodological concept, and how it is related to the current state of the art
  2. Evaluation procedure
  3. Computational complexity
  4. Data sets used for evaluation


  • Dirk Husmeier, University of Glasgow, UK
  • Maurizio Filippone, Eurecom, France
  • Simon Rogers, University of Glasgow, UK
  • Mu Niu, University of Glasgow, UK
  • Benn Macdonald, University of Glasgow, UK


CONTACTS: Dirk Husmeier, Dirk.Husmeier@glasgow.ac.uk