Beyond numerical simulation, the possibility of performing symbolic computation on biomolecular interaction networks opens the way to the design of new automated reasoning tools for biologists/modelers. The Biochemical Abstract machine BIOCHAM provides a precise semantics to biomolecular interaction maps as concurrent transition systems. Based on this formal semantics, BIOCHAM offers a compositional rule-based language for modeling biochemical systems, and an original query language based on temporal logic for expressing biological queries about reachability, checkpoints, oscillations or stability. Turning the temporal logic query language into a specification language for expressing the observed behavior of the system (in wild-life and mutated organisms) makes it possible to use machine learning techniques for completing or correcting biological models semi-automatically. Machine learning from temporal logic formulae is quite new however, both from the machine learning perspective and from the Systems Biology perspective. In this paper, we report on the machine learning system of BIOCHAM which allows to discover, on the one hand, interaction rules from a partial model with constraints on the system behavior expressed in temporal logic, and on the other hand, kinetic parameter values from a temporal logic specification with constraints on numerical concentrations.

Mathematical biology has for a long time investigated the dynamics of biomolecular systems by developing numerical models involving (highly non-linear) differential equations and using tools such as Bifurcation Theory for estimating parameters [1]. Mathematical biology provides a firm ground for the numerical analysis of biological systems. However, state-of-the-art quantitative models can hardly be re-used and composed with other models in a systematic fashion, and are limited to a few tenths of variables [2]. Qualitative models of bio-molecular interactions constitute the core of nowadays cell systems biology. Interaction diagrams are the first tool used by biologists to reason about complex systems. The accumulation of knowledge on gene interaction and pathways is currently entered in databases such as KEGG[3], EcoCyc [4], etc. in the form of annotated diagrams. Tools such as BioSpice, Gepasi, GON, E-cell, etc. have been developed for making simulations based on these databases when numerical data is present. Furthermore the interoperability between databases and simulation tools is now possible with standard exchange formats such as the Systems

Abstract. Recent progress in Biology and data-production technologies push research toward a new interdisciplinary field, named Systems Biology, where the challenge is to break the complexity walls for reasoning about large biomolecular interaction systems. Pioneered by Regev, Silverman and Shapiro, the application of process calculi to the description of biological processes has been a source of inspiration for many researchers coming from the programming language community. Machine (BIOCHAM), in which biochemical systems are modeled using a simple language of reaction rules, and the biological properties of the system, known from experiments, are formalized in temporal logic. In this setting, the biological validation of a model can be done by modelchecking, both qualitatively and quantitatively. Moreover, the temporal properties can be turned into specifications for learning modifications or refinements of the model, when incorporating new biological knowledge. 1

This paper presents a study of possible extensions of Pathway Logic to represent and reason about semiquantitative and probabilistic aspects of biological processes. The underlying theme is the annotation of reaction rules with affinity information that can be used in different simulation strategies. Several such strategies were implemented, and experiments carried out to test feasibility, and to compare results of different approaches. Dimerization in the ErbB signalling network, important in cancer biology, was used as a test case. 1

This deliverable specifies use cases based on bioinformatics research carried out by members of A2. The use cases involve the use of rules to reason over ontologies and pathways (Dresden, Edinburgh, Paris, Linköping) and rules to specify workflows to integrate bioinformatics data (Lisbon, Skövde, Jena, Bucharest). The use cases are designed as a reference point to foster the take up of A2 use cases by I-workpackages. Most notably, many of the use cases specify the need for querying and reactivity with languages like Xcerpt (I4), Erus (I5) and Prova (I5). The use cases range from basic research applications to fully deployed software with an international user base.