481 research outputs found
Reversibility in Massive Concurrent Systems
Reversing a (forward) computation history means undoing the history. In
concurrent systems, undoing the history is not performed in a deterministic way
but in a causally consistent fashion, where states that are reached during a
backward computation are states that could have been reached during the
computation history by just performing independent actions in a different
order.Comment: Presented at MeCBIC 201
From Electric Circuits to Chemical Networks
Electric circuits manipulate electric charge and magnetic flux via a small
set of discrete components to implement useful functionality over continuous
time-varying signals represented by currents and voltages. Much of the same
functionality is useful to biological organisms, where it is implemented by a
completely different set of discrete components (typically proteins) and signal
representations (typically via concentrations). We describe how to take a
linear electric circuit and systematically convert it to a chemical reaction
network of the same functionality, as a dynamical system. Both the structure
and the components of the electric circuit are dissolved in the process, but
the resulting chemical network is intelligible. This approach provides access
to a large library of well-studied devices, from analog electronics, whose
chemical network realization can be compared to natural biochemical networks,
or used to engineer synthetic biochemical networks
Abstract Machines of Systems Biology (Extended Abstract)
Living cells are extremely well-organized autonomous systems, consisting of
discrete interacting components. Key to understanding and modelling their behavior is
modelling their system organization, which can be described as a collection of distinct
but interconnected abstract machines. Biologists have invented a number of notations
attempting to describe, abstractly, these abstract machines and the processes that they
implement. Systems biology aims to understand how these abstract machines work, separately and together
Forward and Backward Bisimulations for Chemical Reaction Networks
We present two quantitative behavioral equivalences over species of a
chemical reaction network (CRN) with semantics based on ordinary differential
equations. Forward CRN bisimulation identifies a partition where each
equivalence class represents the exact sum of the concentrations of the species
belonging to that class. Backward CRN bisimulation relates species that have
the identical solutions at all time points when starting from the same initial
conditions. Both notions can be checked using only CRN syntactical information,
i.e., by inspection of the set of reactions. We provide a unified algorithm
that computes the coarsest refinement up to our bisimulations in polynomial
time. Further, we give algorithms to compute quotient CRNs induced by a
bisimulation. As an application, we find significant reductions in a number of
models of biological processes from the literature. In two cases we allow the
analysis of benchmark models which would be otherwise intractable due to their
memory requirements.Comment: Extended version of the CONCUR 2015 pape
PID Control of Biochemical Reaction Networks
Principles of feedback control have been shown to naturally arise in
biological systems and successfully applied to build synthetic circuits. In
this work we consider Biochemical Reaction Networks (CRNs) as a paradigm for
modelling biochemical systems and provide the first implementation of a
derivative component in CRNs. That is, given an input signal represented by the
concentration level of some species, we build a CRN that produces as output the
concentration of two species whose difference is the derivative of the input
signal. By relying on this component, we present a CRN implementation of a
feedback control loop with Proportional-Integral-Derivative (PID) controller
and apply the resulting control architecture to regulate the protein expression
in a microRNA regulated gene expression model.Comment: 8 Pages, 4 figures, Submitted to CDC 201
Two-Domain DNA Strand Displacement
We investigate the computing power of a restricted class of DNA strand
displacement structures: those that are made of double strands with nicks
(interruptions) in the top strand. To preserve this structural invariant, we
impose restrictions on the single strands they interact with: we consider only
two-domain single strands consisting of one toehold domain and one recognition
domain. We study fork and join signal-processing gates based on these
structures, and we show that these systems are amenable to formalization and to
mechanical verification
An Intuitive Automated Modelling Interface for Systems Biology
We introduce a natural language interface for building stochastic pi calculus
models of biological systems. In this language, complex constructs describing
biochemical events are built from basic primitives of association, dissociation
and transformation. This language thus allows us to model biochemical systems
modularly by describing their dynamics in a narrative-style language, while
making amendments, refinements and extensions on the models easy. We
demonstrate the language on a model of Fc-gamma receptor phosphorylation during
phagocytosis. We provide a tool implementation of the translation into a
stochastic pi calculus language, Microsoft Research's SPiM
- …