Computer Science with/for Biology and (bio)medicine

The vibrant and emerging research area of 'doing research and engineering in the subject domain of biology and the applied biosciences' comprises one or more (sub-) disciplines of computer sciences and information technology that can be mixed with any of the (sub-) disciplines in biology, ecology, and applied biosciences (such as medicine and agriculture). Depending on the emphasis, this combination tends to favour one or more of the following terms to indicate the type of activity: Computational Biology, Systems Biology, Bioinformatics, In Silico Biology, Ecoinformatics, (Bio)Medical Informatics, and bio-ontologies, among others. But what exactly is the breadth and depth of these relatively new fields, and what are its characteristic activities? What is, or can be, used from mathematics to advance biology at a faster pace? What type of problems do bioscientists perceive that need to be solved? Is engineering only a supportive discipline for biology? If not, where and how is biology pushing the frontiers of computer science and IT? How did, and does, the combination of computer science & biology lead to landmark achievements – and which ones are considered to be achievements?

Against this background, the KRDB Research Centre of Faculty of Computer Science at the Free University of Bozen-Bolzano aimed to present and form new expertise and professional profiles who can answer the growing demands of the biosciences and ultimately our societies in the area of using both theoretical and applied aspects of computer science and engineering, thereby contributing to pushing the frontiers in computer science as well as (applied) biology. To this end, it has organized the “CS & IT with/for biology” Seminar Series. The aim of the seminars was to provide a broad spectrum of achievements, opportunities, and challenges on using/combining computer science with/for biology, highlighting diverse foci and approaches traversing biology (sub-) disciplines and applied bioscience and a wide range of computer science approaches. This coverage goes from basic biosciences, such as genetics & cellular processes and larger systems in ecology, and the applied biosciences medicine and agriculture, to CS/IT fields of ontology/ies, logics, natural language processing, database integration, and software development.

A reader [1] was made from the extended abstracts of the invited speakers, offering both a summary of the seminar as well as additional references to give useful pointers to key publications, the most recent research output, and 'hot' topics.

The first chapter in this reader provides a general overview of historical aspects and current characteristics of the rather flexible interpretation that was given to biology & informatics – and the more recent diversification into multiple niche areas. It can aid novices in the field to grasp some of the more, and less, active research activities and 'insiders' to have ample material for discussion. From this introduction, we first take a step back before going into details, by looking at some ethical considerations, as described by Heiner Fangerau. Within a short time span, many new possibilities are (or seem) just around the corner: stem cell research and personalised medicine to name just two; but who benefits, and is a regrouping of the human world population into certain groups with genetic predispositions for particular diseases – technologically not impossible – actually desirable and beneficial for the society at large? Which biases are 'built in' when we do our literature research?

The subsequent chapters go into some detail, both with regard to the technological and computer science aspects as (applied) biology. In chapter 3 Alberto Policriti introduces mathematical modelling for systems biology, with automata and pi-calulus in particular. These topics are relevant for in silico simulations of cellular processes and the mathematical complexities of the outstanding problems, i.e. modelling biological knowledge requires new solutions from mathematicians. The next chapter by Marco Roos, on the other hand, takes a case-based approach: biologists desire to understand better e.g. Huntington's Disease and histones, and to achieve this, they need a computer infrastructure to enable them to do their research. A regrouping of this requirement with technological support has resulted in the initiation of a virtual laboratory for e-science. Marie-Paule Lefranc has taken a yet different path (in chapter 5), where demands from biology, immunogenetics in this case, are combined with the latest developments in computer science, such that her laboratory belongs not only to the ‘early adopters’ of technology over the past 15 years, but also can use it effectively to discover biologically meaningful new information: bio & info in synergy.

The infamous biological data explosion that has occurred over the past 10 years may be well-know, its ‘consequently' disconnected software tools and databases is known in considerably less detail. Apart from the obvious data integration issues between databases and linking database and analysis tools, one first needs to be able to find what is there, and then for the biologist to find what s/he needs. This is a central topic of Sarah Cohen-Boulakia's contribution: what are biologists actually looking for, and how can we, automatically, find the relevant software resources? The issue of finding the right information is addressed from an entirely different angle and context by Werner Ceusters in chapter 7. Advances made in the sub-discipline of natural language understanding can help processing electronic health records, annotated with an ontology, to mine that data and discover new patterns in the patient's treatment and history with as aim to improve biomedicine. Last, with Aldo Gangemi we take a closer look at the usefulness of task and action ontologies for software development in agriculture, with the UN Food and Agriculture Organisation (FAO) among the beneficiaries.

While the topics do not cover all aspects of CS\&IT with/for (applied) biology, it can give you some insight in its multifaceted aspects, ranging from applied mathematics and philosophy to software engineering, from core to applied biology, and from enabling information technology to successful combination of bio-info and biology-driven computer science.

[1] CSBio reader: extended abstracts of the 'CS&IT with/for biology' Seminar Series. Free University of Bozen-Bolzano, 2005.

Philosophy & Informatics, and a future for computer science

I just returned from attending an interesting and lively workshop about philosophy and informatics that had as this year’s theme bio(med), which was held at the DFKI building on the campus of Saarland University in Saarbrücken and co-organised by the Philosophy and Informatics SIG of Germany [1] and IFOMIS [2].

Apart from the occasional comment that informaticians are just teenagers, the atmosphere was generally constructive. Topics varied from developing Aristotelian ontologies with OWL to Gestalt & mereology to (ontology) fusion, and then some. One of the contributions that generated much discussion (and will continue to do so I think) was about the (future) role of computer science in the 21st century by Dieter Fensel and Dieter Wolf [3].

Fensel and Wolf propose that computer science will become the foundation of the sciences because it has information processing and knowledge management (and goal-oriented services) at its core. From that perspective, physics, biology and neuroscience are then branches of computer sciences as they deal with specific types/sections of information and knowledge management. But not everybody sees computer science in that light.
There is the (longstanding?) debate if all knowledge can be captured in axioms or not, and even if it can, this does not mean the computer can do something with it (cf. undecidability, intractability, approximations, …). The argument that the human mind is more and more complex featured prominently; the magic of the mind versus the descartian machine methaphor. Then there were some issues regarding the from-data-to-information-to-knowledge sequence, which features clearly in undergraduate informatics courses, but, as it appeared, has not penetrated study programmes of other disciplines.
Then there is the ‘traditional’ philosophy of science, but this did not make it to a discussion point. Recollecting my philosophy of science course, they taught that, aside from philosophy as the foundation for the sciences, there is the dogmatic/established view that there are somehow ‘layers’, where the core sciences are its basis (e.g. cell physiology, physics), the applied sciences bring knowledge of a few core sciences together in a context (e.g. biomedicine, food science), technology/engineering brings it to the application stage and ‘enables’ scientific research, and practice really implements it. Then the knowledge generated at each layer sort of flows in the direction from core to practice, with few examples the other way around (i.e. that some technology just works, but scientifically the “why” is not known yet). Some (sub-)sub-disciplines of informatics can be considered applied mathematics, others as technology. To put that at the basis is like saying that all biology rests on medicine and health care. Or maybe the analogy does not hold.

Talking about the scientific role of computer sciences, be it now or in the (near) future, assumes it is known to both informaticians (computer scientists) and others what exactly computer science is. Research activities currently categorized under computer science are quite diverse, and contents of BSc & MSc degrees in different countries, even at different universities in the same country, vary widely and do not only comprise “scientific and technological aspects of (automated) information processing” (the definition of informatics/computer science according to my dictionary).
Before discussing the role of computer science, it may be useful to clarify what is computer science. But if one adheres to the information processing and knowledge management essence, then it may well be that the role of computer science is to function as the foundation of the other sciences, which would amount to an interesting paradigm shift – at least it provides some food for thought for philosophers of science.

[1] Philosophy and Informatics SIG

[2] Institute for Formal Ontology in Medical Information Science (IFOMIS)

[3] Fensel, D., Wolf, D. The Scientific Role of Computer Science in the 21st Century. Third International Workshop on Philosophy and Informatics (WSPI06), Saarbrücken, 3-4 May 2006. pp33-46. Online proceedings