A Language for Modeling and Optimizing Experimental Biological Protocols

Automation is becoming ubiquitous in all laboratory activities, moving towards precisely defined and codified laboratory protocols. However, the integration between laboratory protocols and mathematical models is still lacking. Models describe physical processes, while protocols define the steps carried out during an experiment: neither cover the domain of the other, although they both attempt to characterize the same phenomena. We should ideally start from an integrated description of both the model and the steps carried out to test it, to concurrently analyze uncertainties in model parameters, equipment tolerances, and data collection. To this end, we present a language to model and optimize experimental biochemical protocols that facilitates such an integrated description, and that can be combined with experimental data. We provide probabilistic semantics for our language in terms of Gaussian processes (GPs) based on the linear noise approximation (LNA) that formally characterizes the uncertainties in the data collection, the underlying model, and the protocol operations. In a set of case studies, we illustrate how the resulting framework allows for automated analysis and optimization of experimental protocols, including Gibson assembly protocols.

Probability as a modal operator: the possibilities of its combination with other modalities

In this work we examine some of the possibilities of combining a simple probability operator with other modal operators, in particular with a belief operator. We will examine the semantics of two possible situations for expressing probabilistic belief or the lack of it, a simple subjective probability operator (SPO) versus the composition of a belief operator, plus an objective modal operator (BOP). We will study their interpretations in two probabilistic semantics: a relational Kripkean one and a variation of neighbourhood semantics, showing that the latter is able to represent the lack of probabilistic belief more directly, just with the SPO, whereas relational semantics needs the combination of BOP probability to represent lack of belief.

Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach

When modeling real-world domains, we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics (DLs) with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using thetableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLPare systems offering a Prolog implementation of the tableau algorithm. TRILLPbuilds apinpointing formulathat compactly represents the set of explanations and can be directly translated into a BDD. Both reasoners were shown to outperform state-of-the-art DL reasoners. In this paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is directly built during the construction of the tableau, further speeding up the overall inference process. An experimental comparison shows the effectiveness of TORNADO. All systems can be tried online in the TRILL on SWISH web application athttp://trill.ml.unife.it/.

Indicative conditionals

This chapter defends a probabilistic semantics for indicative conditionals and other logical operators. This semantics is motivated in part by the observation that indicative conditionals are context sensitive, and that there are contexts in which the probability of a conditional does not match the conditional probability of its consequent given its antecedent. For example, there are contexts in which you believe the content of ‘it is probable that if Jill jumps from this building, she will die’ without having high conditional credence that Jill will die if she jumps. This observation is at odds with many existing non-truth-conditional semantic theories of conditionals, whereas it is explained by the semantics for conditionals defended in this chapter. The chapter concludes by diagnosing several apparent counterexamples to classically valid inference rules embedding epistemic vocabulary.

Epistemic modals and probability operators

This chapter defends a semantics for epistemic modals and probability operators. This semantics is probabilistic—that is, sentences containing these expressions have sets of probability spaces as their semantic values relative to a context. Existing non-truth-conditional semantic theories of epistemic modals face serious problems when it comes to interpreting nested modal constructions such as ‘it must be possible that Jones smokes’. The semantics in this chapter solves these problems, accounting for several significant features of nested epistemic vocabulary. The chapter ends by defending a probabilistic semantics for simple sentences that do not contain any epistemic vocabulary, and by using this semantics to illuminate the relationship between credence and full belief.