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The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL: www.kimosys.org

Trees are a useful framework for classifying entities whose attributes are, at least partially, related through a common ancestry, such as species of organisms, family members or languages. In some common applications, such as phylogenetic trees based on DNA sequences, relatedness can be inferred from the statistical analysis of unweighted attributes. The vast majority of mutations that survive across generations are evolutionarily neutral, which means that most genetic differences between species will have accumulated independently and randomly. In these cases, it is possible to calculate the tree from a precomputed matrix of distances. In other cases, such as with anatomical traits or languages, the assumption of random and independent differences does not hold, making it necessary to consider some traits to be more relevant than others for determining how related two entities are. In this paper, we present a constraint programming approach that can enforce consistency between bounds on the relative weight of each trait and tree topologies, so that the user can best determine which sets of traits to use and how the entities are likely to be related.

Bioinformatics aims at applying computer science methods to the wealth of data collected in a variety of experiments in life sciences (e.g. cell and molecular biology, biochemistry, medicine, etc.) in order to help analysing such data and eliciting new knowledge from it. In addition to string processing bioinformatics is often identified with machine learning used for mining the large banks of bio-data available in electronic format, namely in a number of web servers. Nevertheless, there are opportunities of applying other computational techniques in some bioinformatics applications. In this paper, we report the application of constraint programming to address two structural bioinformatics problems, protein structure prediction and protein interaction (docking). The efficient application of constraint programming requires innovative modelling of these problems, as well as the development of advanced propagation techniques (e.g. global reasoning and propagation), which were adopted in Chemera, a system that is currently used to support biochemists in their research.

Logic programming languages, such asProlog, allow a declarative specification of Constraint Satisfaction Problems (CSPs), freeing the user from specifying more or less complex control directives. However, the price to pay for such flexibility is a loss of efficiency, which makes Logic Programming inadequate to solve CSPs of even moderate size and complexity. To extend the range of applicability of logic programming, several improvements have been proposed. The use of heuristics is one such improvement. Although this usually forces the user to specify some form of control (thus abandoning the pure declarative nature of a logic program), these specifications can be made declarative by making use of some appropriate meta-predicates. Another extension to logic programming that improves its efficiency, is the active use of constraints, as done in the various formulations of constraint logic programming languages. In particular, the use of finite domains is quite adequate to implement look-ahead schemes to efficiently solve several types of CSPs. In this paper, we discuss the complementary nature of heuristics and look-ahead schemes and present a constraint logic programming framework that integrates both these techniques. Results obtained with a time-tabling problem executed on a prototype that implements such a framework are presented, and show that significant efficiency improvements can be achieved when compared with the separate use of the two techniques.

Trees are a useful framework for classifying entities whose attributes are, at least partially, related through a common ancestry, such as species of organisms, family members or languages. In some common applications, such as phylogenetic trees based on DNA sequences, relatedness can be inferred from the statistical analysis of unweighted attributes. In this paper we present a Constraint Programming approach that can enforce consistency between bounds on the relative weight of each trait and tree topologies, so that the user can best determine which sets of traits to use and how the entities are likely to be related.

RNA secondary structure prediction depends on context. When only a few (sometimes putative) RNA homologs are available, one of the most famous approach is based on a set of recursions proposed by Sankoff in 1985. Although this modus operandi insures an algorithmically optimal result, the main drawback lies in its prohibitive time and space complexities. A series of heuristics were developed to face that difficulty and turn the recursions usable. In front of the inescapable intricacy of the question when handling the full thermodynamic model, we come back in the present paper to a biologically simplified model that helps focusing on the algorithmic issues we want to overcome. We expose our ongoing developments by using the constraints framework which we believe is a powerful paradigm for heuristic design. We give evidence that the main heuristics proposed by others (structural and alignment banding, multi-loop restriction) can be refined in order to produce a substantial gain both in time computation and space requirements. A beta implementation of our approach, that we named ARNICA, exemplify that gain on a sample set that remains unaffordable to other methods. The sources and sample tests of ARNICA are available at http://centria.di.fct.unl.pt/~op/arnica.tar.gz

Objective:: Deep biomedical models are often expressed by means of differential equations. Despite their expressive power, they are difficult to reason about and make decisions, given their non-linearity and the important effects that the uncertainty on data may cause. The objective of this work is to propose a constraint reasoning framework to support safe decisions based on deep biomedical models. Method:: The methods used in our approach include the generic constraint propagation techniques for reducing the bounds of uncertainty of the numerical variables complemented with new constraint reasoning techniques that we developed to handle differential equations. Results:: The results of our approach are illustrated in biomedical models for the diagnosis of diabetes, tuning of drug design and epidemiology where it was a valuable decision-supporting tool notwithstanding the uncertainty on data. Conclusion:: The main conclusion that follows from the results is that, in biomedical decision support, constraint reasoning may be a worthwhile alternative to traditional simulation methods, especially when safe decisions are required.

In this paper we present an efficient deductive method for addressing combina- tional circuit diagnosis problems. The method resorts to bottom-up dependen- cies propagation, where truth-values are annotated with sets of faults. We com- pare it with several other logic programming techniques, starting with a naïve generate-and-test algorithm, and proceeding with a simple Prolog backtracking search. An approach using tabling is also studied, based on an abductive approach. For the sake of completeness, we also address the same problem with Answer Set Programming. Our tests recur to the ISCAS85 circuit bench- marks suite, although the technique is generalized to systems modelled by a set of propositional rules. The dependency-directed method outperforms others by orders of magnitude. authorsversion published

Deep biomedical models are often expressed by means of differential equations. Despite their expressive power, they are difficult to reason about and make decisions, given their non-linearity and the important effects that the uncertainty on data may cause. For this reason traditional numerical simulations may only provide a likelihood of the results obtained. In contrast, we propose in this paper the use of a constraint reasoning framework able to make safe decision notwithstanding some degree of uncertainty, and illustrate this approach in the diagnosis of diabetes and the tuning of drug design.

ESTEEM is an AIM project which is primarily concerned with how to develop, integrate and clinically test knowledge-based systems for electromyography (EMG) in order to get them generally acceptable, useful and applicable into disseminated clinical routines. A medical workstation entitled the 'EMG-Platform' on which different kinds of application modules including KBSs can be interfaced to a kernel is being developed. Accordingly, an EMG communication protocol is being developed. The ESTEEM consortium is composed of a technical specialist group and a group of clinical experts in EMG from seven European countries. The last group has, besides extensive data collection for building up a multicentre EMG database, agreed on a common EMG terminology and a subsequent general EMG data set specification which covers the informatic needs for describing an EMG examination of different 'EMG schools'.

This paper presents an EMG diagnostic Knowledge Based System, that is the first application of our methodology for reasoning with causal-functional (meta-)models. Despite past difficulties, diagnosis is still an important application of KBSs, if considered in an appropriate context of medical practice. We argue that this is the case with neurophisiology, which lends to deep modelling of the domain and associated reasoning. The results obtained with our prototype system, and the clinical context where the system may be used make it a quite promising application, not only to experiment advanced artificial intelligence techniques but also to provide an useful decision support system for medical practice.

Simulation of biomedical systems (e.g. drug metabolism) is an important component of decision support in many medical applications. To cope with the uncertainty of the parameters that are used in the models, one may rely on either applying stochastic methods to quantitative simulation or using qualitative simulation instead, where a number of techniques infer as much as possible in the lack of quantitative information. Many cases, however, rather than lack of quantitative information, are cases of incomplete information, in that numerical bounds can be assigned to the unprecisely known parameters of a model. In such cases, constraint solving techniques might be used to cope with such source of uncertainty and a balance must be sought between the expressive power of the models and the constraint solving capabilities that can be effective applied to these models. This paper presents semi-qualitative modeling and simulation, a new approach aimed at reaching such appropriate balance. This new formalism is introduced with a simple example and by means of a more formal presentation, and its application to a multicompartmental model for drug metabolism is discussed.

In this paper we present a causal-functional approach that we are developing for the representation of medical knowledge which integrates features from both the causal and functional approaches, and may thus take advantage of the kinds of deep knowledge that they exploit, namely temporal transitions between pathophysiological states and function and structure decomposition.

The concept of Expert Decision Support Systems is based on the integration of Decision Support Systems and Expert Systems, providing the decision maker with features from both types of systems (e.g. simulation, explanations, control of reasoning). Constraint logic programming languages, such as CLP(R) or CHIP, achieve this integration by merging logic programming, a paradigm of Artificial Intelligence and Expert Systems, with constraint solving techniques similar to those used in Operations Research and Decision Support Systems. Their embedded constraint solver extends the expressive power of logic programming languages, most notably on the domain of numerical expressions. The paper illustrates how constraint logic programming may be used in EDSS development by presenting a small classroom scheduling problem developed with CLP(R), and compares this approach to more traditional Operations Research techniques.

This paper reports on EPISTOL, a project set up to provide a perspective on how knowledge based systems are going to be used in the health sector 5–10 years from now, and how should this expected use influence the planning of research and development work up to that period. The results of the project are aimed to aid the planning of future programmes concerned with research and development in health telematics, namely the fourth Framework Programme of the Commission of the European Communities.

This paper presents a constraint-based method for improving protein docking results. Efficient constraint propagation cuts over 95% of the search time for finding the configurations with the largest contact surface, provided a contact is specified between two amino acid residues. This makes it possible to scan a large number of potentially correct constraints, lowering the requirements for useful contact predictions. While other approaches are very dependent on accurate contact predictions, ours requires only that at least one correct contact be retained in a set of, for example, one hundred constraints to test. It is this feature that makes it feasible to use readily available sequence data to predict specific potential contacts. Although such prediction is too inaccurate for most purposes, we demonstrate with a Naïve Bayes Classifier that it is accurate enough to more than double the average number of acceptable models retained during the crucial filtering stage of protein docking when combined with our constrained docking algorithm. All software developed in this work is freely available as part of the Open Chemera Library. Electronic supplementary material The online version of this article (doi:10.1186/s13015-015-0036-6) contains supplementary material, which is available to authorized users.