Functional languages provide an excellent framework for formulating biological algorithms in a naive form and then transforming them into an efficient form. This helps biologists understand what matters about programming and brings functional programming into the realm of the practical. In this column, we present an example from our MSc course on bioinformatics and report on our experiences teaching functional programming in this context.

The setting is a class on functional programming. There are four students, Anne, Jack, Mary and Theo.

At the heart of functional programming rests the principle of referential transparency, which in particular means that a function f applied to a value x always yields one and the same value y=f(x). This principle seems to be violated when contemplating the use of functions to describe probabilistic events, such as rolling a die: It is not clear at all what exactly the outcome will be, and neither is it guaranteed that the same value will be produced repeatedly. However, these two seemingly incompatible notions can be reconciled if probabilistic values are encapsulated in a data type.

Pattern matching is an important operation in functional programs. So far, pattern matching has been investigated in the context of structured terms. This article presents an approach to extend pattern matching to terms without (much of a) structure such as binaries which is the kind of data format that network applications typically manipulate. After introducing the binary datatype and a notation for matching binary data against patterns, we present an algorithm that constructs a decision tree automaton from a set of binary patterns. We then show how the pattern matching using this tree automaton can be made adaptive, how redundant tests can be avoided, and how we can further reduce the size of the resulting automaton by taking interferences between patterns into account. Since the size of the tree automaton is exponential in the worst case, we also present an alternative new approach to compiling binary pattern matching which is conservative in space and analyze its complexity properties. The effectiveness of our techniques is evaluated using standard packet filter benchmarks and on implementations of network protocols taken from actual telecom applications.

Position-specific scoring matrices are one way to represent approximate string patterns, which are commonly encountered in the field of bioinformatics. An important problem that arises with their application is calculating the statistical significance of matches. We review the currently most efficient algorithm for this task, and show how it can be implemented in Haskell, taking advantage of the built-in non-strictness of the language. The resulting program turns out to be an instance of dynamic programming, using lists rather the typical dynamic programming matrix.

Confinement properties impose a structure on object graphs which can be used to enforce encapsulation properties. From a practical point of view, encapsulation is essential for building secure object-oriented systems as security requires that the interface between trusted and untrusted components of a system be clearly delineated and restricted to the smallest possible set of operations and data structures. This paper investigates the notion of package-level confinement and proposes a type system that enforces this notion for a call-by-value object calculus as well as a generic extension thereof. We give a proof of soundness of this type system, and establish links between this work and related research in language-based security.