6,719 research outputs found
Experimental Biological Protocols with Formal Semantics
Both experimental and computational biology is becoming increasingly
automated. Laboratory experiments are now performed automatically on
high-throughput machinery, while computational models are synthesized or
inferred automatically from data. However, integration between automated tasks
in the process of biological discovery is still lacking, largely due to
incompatible or missing formal representations. While theories are expressed
formally as computational models, existing languages for encoding and
automating experimental protocols often lack formal semantics. This makes it
challenging to extract novel understanding by identifying when theory and
experimental evidence disagree due to errors in the models or the protocols
used to validate them. To address this, we formalize the syntax of a core
protocol language, which provides a unified description for the models of
biochemical systems being experimented on, together with the discrete events
representing the liquid-handling steps of biological protocols. We present both
a deterministic and a stochastic semantics to this language, both defined in
terms of hybrid processes. In particular, the stochastic semantics captures
uncertainties in equipment tolerances, making it a suitable tool for both
experimental and computational biologists. We illustrate how the proposed
protocol language can be used for automated verification and synthesis of
laboratory experiments on case studies from the fields of chemistry and
molecular programming
Petri nets for systems and synthetic biology
We give a description of a Petri net-based framework for
modelling and analysing biochemical pathways, which uni¯es the qualita-
tive, stochastic and continuous paradigms. Each perspective adds its con-
tribution to the understanding of the system, thus the three approaches
do not compete, but complement each other. We illustrate our approach
by applying it to an extended model of the three stage cascade, which
forms the core of the ERK signal transduction pathway. Consequently
our focus is on transient behaviour analysis. We demonstrate how quali-
tative descriptions are abstractions over stochastic or continuous descrip-
tions, and show that the stochastic and continuous models approximate
each other. Although our framework is based on Petri nets, it can be
applied more widely to other formalisms which are used to model and
analyse biochemical networks
New insights on stochastic reachability
In this paper, we give new characterizations of the stochastic reachability problem for stochastic hybrid systems in the language of different theories that can be employed in studying stochastic processes (Markov processes, potential theory, optimal control). These characterizations are further used to obtain the probabilities involved in the context of stochastic reachability as viscosity solutions of some variational inequalities
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
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