32 research outputs found
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
A Process Algebraical Approach to Modelling Compartmentalized Biological Systems
This paper introduces Protein Calculus, a special modeling language designed for encoding and calculating the behaviors of compartmentilized biological systems. The formalism combines, in a unified framework, two successful computational paradigms - process algebras and membrane systems. The goal of Protein Calculus is to provide a formal tool for transforming collected information from in vivo experiments into coded definition of the different types of proteins, complexes of proteins, and membrane-organized systems of such entities. Using this encoded information as input, our calculus computes, in silico, the possible behaviors of a living system. This is the preliminary version of a paper that was published in Proceedings of International Conference of Computational Methods in Sciences and Engineering (ICCMSE), American Institute of Physics, AIP Proceedings, N 2: 642-646, 2007 (http://scitation.aip.org/dbt/dbt.jsp?KEY=APCPCS&Volume=963&Issue=2)
Bigraphical models for protein and membrane interactions
We present a bigraphical framework suited for modeling biological systems
both at protein level and at membrane level. We characterize formally bigraphs
corresponding to biologically meaningful systems, and bigraphic rewriting rules
representing biologically admissible interactions. At the protein level, these
bigraphic reactive systems correspond exactly to systems of kappa-calculus.
Membrane-level interactions are represented by just two general rules, whose
application can be triggered by protein-level interactions in a well-de\"ined
and precise way. This framework can be used to compare and merge models at
different abstraction levels; in particular, higher-level (e.g. mobility)
activities can be given a formal biological justification in terms of low-level
(i.e., protein) interactions. As examples, we formalize in our framework the
vesiculation and the phagocytosis processes
A framework for protein and membrane interactions
We introduce the BioBeta Framework, a meta-model for both protein-level and
membrane-level interactions of living cells. This formalism aims to provide a
formal setting where to encode, compare and merge models at different
abstraction levels; in particular, higher-level (e.g. membrane) activities can
be given a formal biological justification in terms of low-level (i.e.,
protein) interactions. A BioBeta specification provides a protein signature
together a set of protein reactions, in the spirit of the kappa-calculus.
Moreover, the specification describes when a protein configuration triggers one
of the only two membrane interaction allowed, that is "pinch" and "fuse". In
this paper we define the syntax and semantics of BioBeta, analyse its
properties, give it an interpretation as biobigraphical reactive systems, and
discuss its expressivity by comparing with kappa-calculus and modelling
significant examples. Notably, BioBeta has been designed after a bigraphical
metamodel for the same purposes. Hence, each instance of the calculus
corresponds to a bigraphical reactive system, and vice versa (almost).
Therefore, we can inherith the rich theory of bigraphs, such as the automatic
construction of labelled transition systems and behavioural congruences
Service-based analysis of biological pathways
Background: Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results: Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion: Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper
Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics.
BACKGROUND: The epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language. RESULTS: Our analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization. CONCLUSIONS: The insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are
An inferential framework for biological network hypothesis tests
Background
Networks are ubiquitous in modern cell biology and physiology. A large literature exists for inferring/proposing biological pathways/networks using statistical or machine learning algorithms. Despite these advances a formal testing procedure for analyzing network-level observations is in need of further development. Comparing the behaviour of a pharmacologically altered pathway to its canonical form is an example of a salient one-sample comparison. Locating which pathways differentiate disease from no-disease phenotype may be recast as a two-sample network inference problem. Results
We outline an inferential method for performing one- and two-sample hypothesis tests where the sampling unit is a network and the hypotheses are stated via network model(s). We propose a dissimilarity measure that incorporates nearby neighbour information to contrast one or more networks in a statistical test. We demonstrate and explore the utility of our approach with both simulated and microarray data; random graphs and weighted (partial) correlation networks are used to form network models. Using both a well-known diabetes dataset and an ovarian cancer dataset, the methods outlined here could better elucidate co-regulation changes for one or more pathways between two clinically relevant phenotypes. Conclusions
Formal hypothesis tests for gene- or protein-based networks are a logical progression from existing gene-based and gene-set tests for differential expression. Commensurate with the growing appreciation and development of systems biology, the dissimilarity-based testing methods presented here may allow us to improve our understanding of pathways and other complex regulatory systems. The benefit of our method was illustrated under select scenarios