Repairing Boolean logical models from time-series data using Answer Set Programming

Abstract Background Boolean models of biological signalling-regulatory networks are increasingly used to formally describe and understand complex biological processes. These models may become inconsistent as new data become available and need to be repaired. In the past, the focus has been shed on the inference of (classes of) models given an interaction network and time-series data sets. However, repair of existing models against new data is still in its infancy, where the process is still manually performed and therefore slow and prone to errors. Results In this work, we propose a method with an associated tool to suggest repairs over inconsistent Boolean models, based on a set of atomic repair operations. Answer Set Programming is used to encode the minimal repair problem as a combinatorial optimization problem. In particular, given an inconsistent model, the tool provides the minimal repairs that render the model capable of generating dynamics coherent with a (set of) time-series data set(s), considering either a synchronous or an asynchronous updating scheme. Conclusions The method was validated using known biological models from different species, as well as synthetic models obtained from randomly generated networks. We discuss the method’s limitations regarding each of the updating schemes and the considered minimization algorithm

Revision of Boolean Logical Models of Biological Regulatory Networks using Answer-Set Programming

Biological regulatory networks are one of the most prominent tools used to represent complex, regulatory cellular processes. Creating computational models of these networks is key to better comprehend the corresponding cellular processes, as they allow for the reproduction of known behaviors, the testing of hypotheses, and the identification of predictions in silico. However, given that the process of constructing and revising such models is mainly a manual one, it is prone to error, and would therefore benefit from automation. An attempt at solving this problem has already been made using a mixture of Answer Set Programming (ASP) and C++. The previous attempt automated the process of revising these models, by using ASP to verify whether a Boolean logical model of a biological regulatory network was consistent with a given set of experimental observations and, in case of inconsistencies, used C++ to implement an algorithm capable of searching for possible sets of repair operations to render the model consistent. In our work we propose an alternative solution for this problem, a solution that fully leverages ASP which, being a declarative language tailored for this type of difficult search problems, has demonstrated to be a great tool to use both for consistency checking as well as model repair. This is in view of the fact that ASP offers a more intuitive and elaboration-tolerant programming style, which facilitates the processes of understanding, and modifying the code behind the model revision process. This, coupled with the powerful and exhaustively optimized solving capabilities provided by the state of the art ASP system clingo, has shown that there is great potential in adopting a fully ASP-based approach to aid in the automation of the revision of Boolean logical models. In this thesis we present the tool that we have developed to automate the process of revising Boolean logical models of Biological Regulatory Network(s) (BRN), which uses ASP to search for inconsistencies and perform repairs on these models.As redes reguladoras biológicas são das ferramentas mais proeminentes usadas para representar processos celulares regulatórios complexos. A criação de modelos computacionais destas redes é fundamental para entender melhor os processos celulares correspondentes, pois permitem reproduzir comportamentos conhecidos, testar hipóteses e identificar previsões in silico. Porém, dado que o processo de construção e revisão destes modelos é principalmente manual, torna-se propenso a erros e, logo, beneficiaria de automação. Já foi feita uma tentativa de resolução deste problema usando uma mistura de Programação por Conjuntos de Resposta (ASP) com C++. A tentativa anterior automatizou o processo de revisão destes modelos, usando ASP para verificar se um modelo lógico booleano de uma rede regulatória é consistente com um determinado conjunto de observações experimentais e, caso inconsistências se verifiquem, é utilizado um algoritmo desenvolvido em C++ capaz de encontrar possíveis conjuntos de operações de reparo para tornar o modelo consistente. No nosso trabalho, propomos uma solução alternativa para este problema, que tira completo partido da utilização ASP que, sendo uma linguagem declarativa adaptada a este tipo de problemas de busca difíceis, demonstrou ser uma excelente ferramenta a utilizar tanto para a verificação da consistência como para a reparação de modelos. Tal deve-se ao facto de ASP oferecer um estilo de programação mais intuitivo e tolerante à elaboração, o que facilita os processos de compreensão, e a modificação do código por detrás do processo de revisão de modelos. Isto, juntamente com as poderosas e otimizadas capacidades de resolução de problemas de busca oferecidas pelo sistema ASP de última geração clingo, demonstrou que existe um grande potencial na adopção de um sistema totalmente baseado em ASP para ajudar na automatização da revisão destes modelos. Nesta tese apresentamos a ferramenta que desenvolvemos para automatizar o processo de revisão de modelos lógicos booleanos de redes reguladoras biológicas (BRN), que utiliza ASP para procurar inconsistências e efectuar reparações nestes modelos