73 research outputs found
Mutual Mobile Membranes Systems with Surface Objects
In this paper we introduce mutual mobile membranes with surface objects,
systems which have biological motivation. In P systems with mobile membranes with
surface objects, a membrane may enter or exit another membrane. The second membrane
just undergoes the action, meaning that it has no control on when the movement takes
place. This kind of movement illustrates the lack of an agreement (synchronization)
similar to an asynchronous evolution. In mutual mobile membranes with surface objects
this aspect is adjusted: any movement takes place only if both participants agree by
synchronizing their evolution. In membranes two kinds of competition can occur: resource
competition and location competition. Resource competition refers to rules which request
the same resources, and the available resources can only be allocated to some of the rules.
Location competition refers to the movement of a membrane in the hierarchical structure
of the membrane systems under the request of some conflict rules.We use the two variants
of membrane systems in order to describe and explain these kinds of competition, and
introduce synchronizing objects in mutual mobile membranes which will help to solve
the resource and location competitions
Timed Concurrent Constraint Programming for Analysing Biological Systems
AbstractIn this paper we present our first approach to model and verify biological systems using ntcc, a concurrent constraint process calculus. We argue that the partial information constructs in ntcc can provide a suitable language for such systems. We also illustrate how ntcc may provide a unified framework for the analysis of biological systems, as they can be described, simulated and verified using the elements provided by the calculus
Infobiotics : computer-aided synthetic systems biology
Until very recently Systems Biology has, despite its stated goals, been too reductive in terms of the models being constructed and the methods used have been, on the one hand, unsuited for large scale adoption or integration of knowledge across scales, and on the other hand, too fragmented. The thesis of this dissertation is that better computational languages and seamlessly integrated tools are required by systems and synthetic biologists to enable them to meet the significant challenges involved in understanding life as it is, and by designing, modelling and manufacturing novel organisms, to understand life as it could be. We call this goal, where everything necessary to conduct model-driven investigations of cellular circuitry and emergent effects in populations of cells is available without significant context-switching, “one-pot” in silico synthetic systems biology in analogy to “one-pot” chemistry and “one-pot” biology. Our strategy is to increase the understandability and reusability of models and experiments, thereby avoiding unnecessary duplication of effort, with practical gains in the efficiency of delivering usable prototype models and systems. Key to this endeavour are graphical interfaces that assists novice users by hiding complexity of the underlying tools and limiting choices to only what is appropriate and useful, thus ensuring that the results of in silico experiments are consistent, comparable and reproducible.
This dissertation describes the conception, software engineering and use of two novel software platforms for systems and synthetic biology: the Infobiotics Workbench for modelling, in silico experimentation and analysis of multi-cellular biological systems; and DNA Library Designer with the DNALD language for the compact programmatic specification of combinatorial DNA libraries, as the first stage of a DNA synthesis pipeline, enabling methodical exploration biological problem spaces. Infobiotics models are formalised as Lattice Population P systems, a novel framework for the specification of spatially-discrete and multi-compartmental rule-based models, imbued with a stochastic execution semantics. This framework was developed to meet the needs of real systems biology problems: hormone transport and signalling in the root of Arabidopsis thaliana, and quorum sensing in the pathogenic bacterium Pseudomonas aeruginosa. Our tools have also been used to prototype a novel synthetic biological system for pattern formation, that has been successfully implemented in vitro. Taken together these novel software platforms provide a complete toolchain, from design to wet-lab implementation, of synthetic biological circuits, enabling a step change in the scale of biological investigations that is orders of magnitude greater than could previously be performed in one in silico “pot”
Infobiotics : computer-aided synthetic systems biology
Until very recently Systems Biology has, despite its stated goals, been too reductive in terms of the models being constructed and the methods used have been, on the one hand, unsuited for large scale adoption or integration of knowledge across scales, and on the other hand, too fragmented. The thesis of this dissertation is that better computational languages and seamlessly integrated tools are required by systems and synthetic biologists to enable them to meet the significant challenges involved in understanding life as it is, and by designing, modelling and manufacturing novel organisms, to understand life as it could be. We call this goal, where everything necessary to conduct model-driven investigations of cellular circuitry and emergent effects in populations of cells is available without significant context-switching, “one-pot” in silico synthetic systems biology in analogy to “one-pot” chemistry and “one-pot” biology. Our strategy is to increase the understandability and reusability of models and experiments, thereby avoiding unnecessary duplication of effort, with practical gains in the efficiency of delivering usable prototype models and systems. Key to this endeavour are graphical interfaces that assists novice users by hiding complexity of the underlying tools and limiting choices to only what is appropriate and useful, thus ensuring that the results of in silico experiments are consistent, comparable and reproducible.
This dissertation describes the conception, software engineering and use of two novel software platforms for systems and synthetic biology: the Infobiotics Workbench for modelling, in silico experimentation and analysis of multi-cellular biological systems; and DNA Library Designer with the DNALD language for the compact programmatic specification of combinatorial DNA libraries, as the first stage of a DNA synthesis pipeline, enabling methodical exploration biological problem spaces. Infobiotics models are formalised as Lattice Population P systems, a novel framework for the specification of spatially-discrete and multi-compartmental rule-based models, imbued with a stochastic execution semantics. This framework was developed to meet the needs of real systems biology problems: hormone transport and signalling in the root of Arabidopsis thaliana, and quorum sensing in the pathogenic bacterium Pseudomonas aeruginosa. Our tools have also been used to prototype a novel synthetic biological system for pattern formation, that has been successfully implemented in vitro. Taken together these novel software platforms provide a complete toolchain, from design to wet-lab implementation, of synthetic biological circuits, enabling a step change in the scale of biological investigations that is orders of magnitude greater than could previously be performed in one in silico “pot”
DynamiTE:A 21st-Century Framework for Concurrent Component-Based Design
The free ride for software developers is over. In the past, computer programs have increased in performance simply by running on new hardware with ever increasing clock speeds. Now, however, this line of development has reached its end and chip designers are producing new processors, not with faster clocks, but with more cores.
To take advantage of the speed increases offered by these new products, applications need to be redesigned with parallel processing firmly in mind.
The problem is that mainstream designs are still inherently sequential. Concurrency tends to be an afterthought that may be useful to gain a performance boost, not an essential part of the design process. The current vogue for object-oriented designs tends to also have the side-effect of making them heavily data-oriented which doesn't scale well; each shared element of data has to be protected from simultaneous access, resulting in operations becoming sequential again. In addition, the usual methods
for protecting data tend to be very low-level and error-prone.
In this thesis, we introduce a new design method whereby applications are constructed from small sequential tasks connected by intercommunication primitives. Our approach is based on a two-stage process; first, the individual tasks are created as independent entities and tested with appropriate inputs, then secondly, the communication infrastructure between them is developed. We provide
support for the latter via the DynamiTE framework, which allows the interactions to be defined using the terms of a process calculus.
Depending on the developer's background, they can treat this as just another API, as a design pattern or as an algebraic expression which can be property checked for issues such as deadlocks. Either way, the communication layer can be developed, tested and evaluated separately from the tasks once it is known how the tasks will interface with one
another.
To supplement DynamiTE, we define our own process calculus, Nomadic Time, using a carefully chosen novel selection of constructs. Among the features of the calculus are the ability to perform communication both locally (one-to-one) and globally (one-to-many), and the flexibility to change the location of tasks during execution.
Security is paramount to the design of Nomadic Time and migratory operations can be limited in two ways; by simple enumeration of possibilities or by the optional typing of constructs to allow restriction on a task-by-task basis.
While it can't eradicate all the problems inherent in designing concurrent applications, DynamiTE can make things easier by reducing the dependency on shared resources and enhancing the reusability of concurrent components
Continuous-time temporal logic specification and verification for nonlinear biological systems in uncertain contexts
In this thesis we introduce a complete framework for modelling and verification of biological systems in uncertain contexts based on the bond-calculus process algebra and
the LBUC spatio-temporal logic. The bond-calculus is a biological process algebra which
captures complex patterns of interaction based on affinity patterns, a novel communication
mechanism using pattern matching to express multiway interaction affinities and general
kinetic laws, whilst retaining an agent-centric modelling style for biomolecular species.
The bond-calculus is equipped with a novel continuous semantics which maps models to
systems of Ordinary Differential Equations (ODEs) in a compositional way.
We then extend the bond-calculus to handle uncertain models, featuring interval uncertainties in their species concentrations and reaction rate parameters. Our semantics is also
extended to handle uncertainty in every aspect of a model, producing non-deterministic
continuous systems whose behaviour depends either on time-independent uncertain parameters and initial conditions, corresponding to our partial knowledge of the system at
hand, or time-varying uncertain inputs, corresponding to genuine variability in a system’s
behaviour based on environmental factors.
This language is then coupled with the LBUC spatio-temporal logic which combines
Signal Temporal Logic (STL) temporal operators with an uncertain context operator
which quantifies over an uncertain context model describing the range of environments
over which a property must hold. We develop model-checking procedures for STL and
LBUC properties based on verified signal monitoring over flowpipes produced by the
Flow* verified integrator, including the technique of masking which directs monitoring for
atomic propositions to time regions relevant to the overall verification problem at hand.
This allows us to monitor many interesting nested contextual properties and frequently
reduces monitoring costs by an order of magnitude. Finally, we explore the technique
of contextual signal monitoring which can use a single Flow* flowpipe representing a
functional dependency to complete a whole tree of signals corresponding to different
uncertain contexts. This allows us to produce refined monitoring results over the whole
space and to explore the variation in system behaviour in different contexts
Studying the effects of adding spatiality to a process algebra model
We use NetLogo to create simulations of two models of disease transmission originally expressed in WSCCS. This allows us to introduce spatiality into the models and explore the consequences of having different contact structures among the agents. In previous work, mean field equations were derived from the WSCCS models, giving a description of the aggregate behaviour of the overall population of agents. These results turned out to differ from results obtained by another team using cellular automata models, which differ from process algebra by being inherently spatial. By using NetLogo we are able to explore whether spatiality, and resulting differences in the contact structures in the two kinds of models, are the reason for this different results. Our tentative conclusions, based at this point on informal observations of simulation results, are that space does indeed make a big difference. If space is ignored and individuals are allowed to mix randomly, then the simulations yield results that closely match the mean field equations, and consequently also match the associated global transmission terms (explained below). At the opposite extreme, if individuals can only contact their immediate neighbours, the simulation results are very different from the mean field equations (and also do not match the global transmission terms). These results are not surprising, and are consistent with other cellular automata-based approaches. We found that it was easy and convenient to implement and simulate the WSCCS models within NetLogo, and we recommend this approach to anyone wishing to explore the effects of introducing spatiality into a process algebra model
Computational Logic for Biomedicine and Neurosciences
We advocate here the use of computational logic for systems biology, as a
\emph{unified and safe} framework well suited for both modeling the dynamic
behaviour of biological systems, expressing properties of them, and verifying
these properties. The potential candidate logics should have a traditional
proof theoretic pedigree (including either induction, or a sequent calculus
presentation enjoying cut-elimination and focusing), and should come with
certified proof tools. Beyond providing a reliable framework, this allows the
correct encodings of our biological systems. % For systems biology in general
and biomedicine in particular, we have so far, for the modeling part, three
candidate logics: all based on linear logic. The studied properties and their
proofs are formalized in a very expressive (non linear) inductive logic: the
Calculus of Inductive Constructions (CIC). The examples we have considered so
far are relatively simple ones; however, all coming with formal semi-automatic
proofs in the Coq system, which implements CIC. In neuroscience, we are
directly using CIC and Coq, to model neurons and some simple neuronal circuits
and prove some of their dynamic properties. % In biomedicine, the study of
multi omic pathway interactions, together with clinical and electronic health
record data should help in drug discovery and disease diagnosis. Future work
includes using more automatic provers. This should enable us to specify and
study more realistic examples, and in the long term to provide a system for
disease diagnosis and therapy prognosis
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