In the last few years an entirely new discipline has emerged: 'Computational Biology'. It tries to solve, baaed on a strong mathematical and computational background, problems raised from Biosciences. The Protein Folding Problem (PF for short) is one of the most important open problems in Biology. It can be stated as follows: given an unfolded aminoacid sequence, find the 'right' folding of that sequence. In nature, the proteins fold to their 'native' state, which determines its functionality. Some lattice-based computational models of the PF were shown NP-Complete, others remain NP-hard [2, 9], but some ap proximation algorithms errist [3]. However its theoretical and practical relevance [8, 9] makes worthwhile spending resources and time in modeling the folding process. Usually, strong enfaais is put in the results obtained, rather that in the way they are generated, enlarging the gap between researchers from Computer Science and Biology. We claim that, using the right tools, both communities can collaborate much closer, enhancing the results at the same time.Historically, 'Functional Programming' (FP for short) [1] has been associated with a small scope of applications, mainly academic. Computer Science community did not pay enough attention to its potential, perhaps due to the lack of efficiency of functional languages. Now, new theoretical developments in the field of FP [4] are emerging, and better languages (e.g. Haakell [7], Concurrent Haekell [5]) have been defined and implemented. Also, the gap between theory and practice is smaller in this paradigm than that of other paradigms, making FP a good choice for developing simulation and optimization programs [10]. Traditionally, all programs for optimization problems were written in C, C++ or Ada; this builds a firewall between developers and end-users. PF is suitable to be modeled with a lazy concurrent functional language for many reasons: non-computer-science people can think in a very high abstraction level and map their ideas, aimost directly, to functional code; the learning curve of a FP language is smoother than that of an imperative one, bridging the gap between developera and usera; functional code is concise; the folding process is intrinsically parallel and FP is specially adequate for managing parallelism; concurrent processes on the string to be folded can be simulated using easy-to-use features of concurrent functional Permission to make digital/hard copy of part or all this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advanlanguages; the use of lazy languages avoids the construction of protein configurations until they are needed (if ever); using FP, it is straightforward to associate folding algorithms to folding patterns [6]. Our proposal is to use FP as a bridge between researchers of Computer Science and Biosciences. Computer science researchers have their benefit because of rapid protot yping, while bioscience-researchers haveit because of the high ab...
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