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WG1 includes expert researchers in physics, mathematics, chemistry, biology, statistics, computer science and engineering.

Theoretical Modelling of Information Flow in Genetic Coding and Other Coding Systems


  • Develop a theoretical unifying interpretation of the regularities observed in the genetic code and in coding regions of DNA by comparing and integrating existing models and theories.

  • Apply the different theoretical approaches developed for the genetic code and for coding regions of DNA to its noncoding part.

  • Extend these theoretical approaches to the case of other biological codes.


1.1 Develop existing theoretical models for coding, information management, and the associated dynamical behaviour, in the encoding, transcription and translation of proteins. This task mainly involves models based on number theory, quantum and classical information, and dynamical systems theory that exploit to some extent the mathematical regularities observed in the genetic code and in protein coding sequences (e.g. symmetries, topological features).

1.2 Apply the theoretical approaches developed within task 1.1 for the study of the informational organization of non-coding regions of DNA. In human DNA, only about 2% of base pairs are related to protein coding and about 98% of them are non-coding. According to the international Encyclopaedia of DNA Elements (ENCODE) project, at least 80% of human DNA has some biochemical functions. Hence, understanding the roles that biological information and coding play in non-coding DNA represents a major scientific challenge.

1.3 Explore the possibility of extending the approaches of task 1.1 to other biological codes. Many biological codes, other than the genetic code, have been identified in recent years; for example: the splicing code and the histone code in the genetic apparatus, and the signal transduction codes in the cellular metabolism. The theoretical models developed for explaining the genetic code and protein synthesis represent an important scientific prototypical corpus for studying other biological codes.

1.4 Integrate in a unifying view the different theoretical approaches and build a strong interaction among different research groups within WG1 contributing also to a strong collaborative research with WG 2,3, and 4. To this aim, a specific task force will be created inside the group.


Nataša Mišić


Clara Grácio


WG2 includes expert researchers in modern statistics, probabilistic modelling, bioinformatics, machine learning and data science at large.

Statistical and Probabilistic Modelling and Analysis, Bioinformatics and Machine Learning


  • Provide expertise in statistics, probability, bioinformatics, machine learning to address modern biological problems and for the analysis of all kind of (high-dimensional) genomic and proteomic data.

  • Provide know-how for data mining, data management and testing theories and models.

  • Provide the statistical and probabilistic support and expertise to mathematical modelling and experimental design.


2.1 Analysis of the big corpus of data coming from molecular biology in specific problems. Modern biological challenges are multifaceted and require a multi-disciplinary approach. Expert researchers in bioinformatics will deal with the more technical and technological aspects such as, for instance, the management of big databases or devising high performance computing solutions to complex combinatorial problems. The experts in statistics and machine learning will interact with them as to provide the expertise and rigour in statistical and probabilistic modelling by using state-of-the-art methods in hot areas such as high-dimensional classification and estimation problems, repeated testing, networks, just to name a few.

2.2 Provide the statistical and probabilistic support and expertise to mathematical modelling. Complement the theoretical modelling step with a statistical/probabilistic approach. For instance, the theory of dynamical systems is closely linked to time series analysis and the theory of stochastic processes. Second, in order to apply a theoretical model to real data and obtain estimates, a necessary step that involves mathematical statistics is needed, usually in the form of deriving the mathematical properties of the estimators. Third, the theory of design of the experiments will be possibly used to devise new laboratory experiments that will enable to test and validate theories and models.

​2.3 Provide bioinformatic and machine learning expertise to WG1 to solve computational problems and for the analysis of all kind of (high-dimensional) genomic and proteomic data. The theoretical problems that are addressed by WG1 will pose new challenges that require modern technical expertise in the fields of bioinformatics, machine learning and data science. These include both supervised and unsupervised learning, protein structure prediction, solving combinatorial problems and many more.


Jeanine Houwing-Duistermaat


Cross-field Interaction, Validation, and Potential Applications


  • Create a multidisciplinary panel board to foster cross-field interaction, validation, and the exploration of potential applications.

  • Stimulate and coordinate the validation and tests of models and theories.

  • Encourage the design of new experiments and explore potential applications suggested by the different modelling frameworks.


3.1 Creation of a multi-disciplinary panel board with theoretical experts from WG1, statistical and computer science experts from WG2, and members of the dissemination panel. This panel will coordinate the collaborative research efforts between WG1 and WG2, and will promote a paradigm shift in theoretical biology, which can be identified as the informational interpretation of life.

3.2 Validation and testing of the different approaches in WG1 with data and tools from WG2 and the interdisciplinary coordination of the Cross-Field Interaction panel of 3.1.

​3.3 Exploring the potential applicationssuggested by the Interdisciplinary results on the research, validation, and testing performed within task  3.2, stimulated and coordinated by the Cross-Field Interaction panel; proposal of new experimental tests on the basis of the results of task 3.2.


Frans Mulder


Dissemination and Communication


  • Create a panel dedicated to dissemination to facilitate the interdisciplinary communication between different groups of the Action.

  • Promote communication, dissemination, and science-art crossover activities related to informational biology at large remarking the importance of basic research for the evolution and the well-being of the human society.


4.1 Creation of a multidisciplinary panel dedicated to dissemination and communication; the panel will be preferentially coordinated by a member of an ITC country and will deal with both the dissemination activities to promote multidisciplinary interaction and the communication activities at large. It will also devise tools and procedures to break the communication barriers between different disciplines. The involvement of experts in scientific communication and dissemination is foreseen, also through an expansion of the initial network.

4.2 Development of a “National Platform” for Dissemination and Scientific Communication in Informational and Theoretical Biology in an ITC Country.

4.3 Implementation of 5-day theoretical schools for training young researchers, four 3-day Workshops and four 1-day hackathons for training young PhD or Post-Doc researchers.

4.4 Realization of two short videos, one directed to illustrate the scientific and historical aspects of informational biology, and the second for illustrating the results of the Action and the foreseen social impacts. These videos will be mainly used to promote communication with stakeholders, policy makers, and potential industrial partners.

4.5 Implementation of a Forum for communication with potential industrial  beneficiaries and stakeholders at different levels; invitation of key leaders in related applications and policy makers to seminars and Conferences organized by the Action.

4.6 Implementation of the digital content, including interactive websites, blogs and wiki pages.


Jitka Čejková
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