Below you will find all our applications of Answer Set Programming for Systems Biology. Our aim is to provide a collection of software packages to be used by non-expert users out-of-the-box. Towards this end, we have developed and made available each application in two modes:
In order to create a BioASP environment, you can easily install all our applications by installing the meta-package bioasp.
We address the problem of repairing large-scale biological networks and corresponding yet often discrepant measurements in order to predict unobserved variations. To this end, we propose a range of different operations for altering experimental data and/or a biological network in order to re-establish their mutual consistency—an indispensable prerequisite for automated prediction. For accomplishing repair and prediction, we take advantage of the distinguished modeling and reasoning capacities of Answer Set Programming.
We propose a qualitative approach to elaborating the biosynthetic capacities of metabolic networks. In fact, large-scale metabolic networks as well as measured datasets suffer from substantial incompleteness. Moreover, traditional formal approaches to biosynthesis require kinetic information, which is rarely available. Our approach builds upon a formal method for analyzing large-scale metabolic networks. Mapping its principles into Answer Set Programming allows us to address various biologically relevant problems.
Logic modeling is a useful tool to study signal transduction across multiple pathways. Automated inference of logical networks from experimental data allow for identifying admissible large-scale logic models saving a lot of efforts and without any a priori bias. Next, once a family a logical networks has been identified, one can suggest or design new experiments in order to reduce the uncertainty provided by this family. Finally, one can look for intervention strategies (i.e. inclusion minimal sets of knock-ins and knock-outs) that force a set of target species or compounds (over the complete family of networks) into a desired steady state. Altogether, this constitutes a pipeline for automated reasoning on logical signaling networks. Hence, the aim of caspo is to implement such pipeline providing a powerful and easy-to-use software tool for systems biologist
Integrating heterogeneous knowledge is necessary to elucidate the regulations in biological systems. In particular, such an integration is widely used to identify functional units, that are sets of genes that can be triggered by the same external stimuli, as biological stresses, and that are linked to similar responses of the system. Although several models and algorithms shown great success for detecting functional units on well-known biological species, they fail in identifying them when applied to more exotic species, such as extremophiles, that are by nature unrefined. Shortest Genome Segments (SGS) is a new model of functional units with a predictive power that is comparable to existing methods but overcomes this crucial limitation. In contrary to existing methods, SGS are stable in (i) computational time and (ii) ability to predict functional units when one deteriorates the biological knowledge, which simulates cases that occur for exotic species.
This work proposes a method that integrates predictions of transcription factors, binding sites and operons with gene associations induced by transcriptomic data in order to produce a realistic regulatory graph. Our approach, benchmarked on E. coli, provided a regulatory graph that recovers essential features of the gold standard regulatory network for this organism, keeping experimentally validated regulations with significantly higher probability than non-validated ones. In addition, it shares the expected topological properties of a regulatory network. As a major functional output, our approach can be used to highlight functional relationships between genes clustered together in transcriptomic experiments but moreover emphasizes within the whole genome the key functional global regulators which are necessary when the bacterial system is stressed by environmental conditions.