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[ACH95] Alur, Courcoubetis, Halbwachs, Henzinger, Ho, Nicollin, Olivero, Sifakis, Yovine. The Algorithmic Analysis of Hybrid Systems. TCS 138 1995
[ABI01] Alur, Belta, Ivancic, Kumar, Mintz, Pappas, Rubin, Schug. Hybrid modeling and simulation of biomolecular networks. HSCC01, LNCS 2034 2001
[ADI02] Alur, Dang, Ivancic. Reachability Analysis of Hybrid Systems via Predicate Abstraction. HSCC02, LNCS 2289 2002
[ADI03a] Alur, Dang, Ivancic. Counter-example Guided Predicate Abstraction of Hybrid Systems. TACAS, LNCS 2619 2003
[ADI03b] Alur, Dang, Ivancic. Progress on reachability analysis of hybrid systems using predicate abstraction. HSCC, LNCS 2623 2003
[AD94] Alur, Dill. A theory of timed automata. TCS 126 1994
[AGAT02] Amonlirdviman, Ghosh, Axelrod, Tomlin. A hybrid systems approach to modeling and analyzing planar cell polarity. Sys. Bio. 2002
[AMPPS03] Antoniotti, Mishra, Piazza, Policriti, Simeoni. Modelling cellular behavior with hybrid automata: Bisimulation and collapsing. CMSB, LNCS 2602 2003
[APUM02] Antoniotti, Policriti, Ugel, Mishra. XS-systems: eXtended S-Systems and Algebraic Differential Automata for Modeling Cellular Behavior. HiPC 2002
[Ard02] Ardelean. The relevance of biomembranes for P systems, Fund. Inf. 49 2002
[Ard03] Ardelean. Molecular Biology of Bacteria and its Relevance for P Systems. WMC-CdeA, LNCS 2597 2003
[BAM03] Besozzi, Ardelean, Mauri. The potential of P systems for modelling the activity of channels in E. Coli. M.C., LNCS 2003
[BFMZ03] Besozzi, Ferretti, Mauri, Zandron. P Systems with Deadlock. BioSystems 70 2003
[BMPZ03] Besozzi, Mauri, Paun, Zandron. Gemmating P systems: Collapsing Hierarchies. TCS 296 2003
[BCMMT05] Barbuti, Cataudella, Maggiolo, Milazzo, Troina. A Probabilistic Model for Molecular Systems. Fund. Inf. 67 2005
[BMMT06a] Barbuti, Maggiolo, Milazzo, Troina. A Calculus of Looping Sequences for Modelling Microbiological Systems. Fund. Inf. 72 2006
[BCDPPQ] Brodo, Curti, Degano, Prandi, Priami, Quaglia. Formal Executable Descriptions of Bio Systems. IEEE QEST 2005
[BG05] Busi, Gorrieri. On the Computational Power of Brane Calculi. TCSB, to appear
[Bus05] Busi. On the Computational Power of the Mate/Bud/Drip Brane Calculus: Interleaving vs. Maximal Parallelism. M.C., LNCS 3850 2006
[Car05] Cardelli. Brane Calculi. Interactions of Bio Membranes. CMSB, LNCS 3082 2005
[CG98] Cardelli, Gordon. Mobile ambients. FOSSACS98, LNCS 1998
[CP05] Cardelli, Pradalier. Where membranes meet complexes, BioConcur 2005
[CMPM05] Casagrande, Mysore, Piazza, Mishra. Independent dynamics hybrid automata systems biology. AB 2005
[CPM05] Casagrande, Piazza, Mishra. Semi-Algebraic Constant Reset Hybrid Automata. CDC-ECC, IEEE 2005
[CD03] Chiaverini, Danos. A core modeling language for the working molecular biologist. CMSB, LNCS 2602 2003
[CCDM04] Chiarugi, Curti, Degano, Marangoni, R. VICE: a Virtual Cell. CMSB, LNCS 3082 2004.
[CE82] Clarke, Emerson. Design and Synthesis of Synchronization Skeletons using Branching-Time Temporal Logic. Logic of Prog. 1982
[CES86] Clarke, Emerson, Sistla. Automatic verification of finite-state concurrent systems using temporal logic specifications. TOPLAS 8 1986
[CC79] Cousot, Cousot. Systematic Design of Program Analysis Frameworks. POPL 1979
[CDPB03] Curti, Degano, Priami, Baldari. Causal pi-calculus for Biochemical Modelling. CMSB, LNCS 2602 2003
[CDPB04] Curti, Degano, Priami, Baldari. Modelling biochemical pathways through enhanced pi-calculus. TCS 325 2004
[DL03] Danos, Laneve. Core formal molecular biology. ESOP, LNCS 2618 2003
[DL04] Danos, Laneve. Formal Molecular Biology. TCS 325 2004
[DP04] Danos, Pradalier. Projective Brane Calculus. CMSB, LNCS 3082 2005
[FB96] Fontana, Buss. The barrier of objects: from dynamical system to bounded organizations. Addison-Wesley 1996
[FM03] Franco, Manca. A membrane system for the leucocyte selective recruitment. Memb. Comp. 2003
[FH05] Franzle, Herde. Efficient Proof Engines for Bounded Model Checking of Hybrid Systems. ENTCS 133 2005
[FJ02a] Frisco, Ji. Info-energy P systems. DNA 2002
[FJ02b] Frisco, Ji. Towards a hierarchy of info-energy P systems. WMC-CdeA02, LNCS 2597 2003
[Gil77] Gillespie. Exact stochastic simulation of coupled chemical reactions. J.Ch.Phys. 81 1997
[Gil01] Gillespie. Approximate accelerated stochastic simulation of chemically reacting systems. J.Ch.Phys. 115 2001
[GL05] Gori, Levi. A new occurrence counting analysis for BioAmbients. APLAS05, LNCS 3780 2005
[GPB05] Giorgetti, Pappas, Bemporad. Bounded model checking of hybrid dynamical systems. CDC-ECC, IEEE 2005
[GP05a] Girard, Pappas. Approximate bisimulations for nonlinear dynamical systems. CDC-ECC, IEEE 2005
[GP05b] Girard, Pappas. Approx. bisimulations for constrained linear systems. CDC-ECC, IEEE 2005
[GP06] Girard, Pappas. Approx. simulation relations for hybrid systems. ADHS 2006
[GT01] Ghosh, Tomlin. Lateral Inhibition through Delta-Notch Signaling: A Piecewise Affine Hybrid Model. HSCC, LNCS 2034 2001
[GT04] ] Ghosh, Tomlin. Symbolic Reachable Set Computation of Piecewise Affine Hybrid Automata and its Application to Bio Modelling: Delta-Notch Protein. Syst. Bio. 1 2004
[GT05] ] Ghosh, Tomlin. An Algorithm for Reachability Computations on Hybrid Automata Models of ProteSignaling Networks. CDC-ECC, IEEE 2005
[Hoa85] Hoare. Communicating Sequential Processes. Prentice-Hall 1985 <br />[HHGT01] Holcombe, Holcombe, Gheorghe, Talbot. A hybrid machine model of rice blast fungus Magnaporthe grisea. IPCAT 2001
[Kit02] Kitano. Foundations of System Biology. MIT Press 2002
[Kop96] Kopke. The Theory of Rectangular Hybrid Automata. PhD thesis, Cornell U 1996
[LPS00] Lafferriere, Pappas, Sastry. O-minimal hybrid systems. Math. Control Sig. Syst. 13 2000
[Mar03] Marcus. Bridging P Systems and Genomics: A Preliminary Approach. WMC-CdeA02, LNCS 2597 2003
[Mil89] Milner. Communication and Concurrency. Prentice-Hall 1989
[MPW92] Milner, Parrow, Walker. A calculus of mobile processes. I&amp;C 100 1992
[Mis02] Mishra. A Symbolic Approach to Modeling Cellular Behavior. LNCS 2552 2002
[NOMK99] Nagasaki, Onami, Miyano, Kitano. Bio-calculus: Its concept and molecular interaction. Genome Infor. 10 1999
[Nak72] Nakanishi. Stochastic analysis of an oscillating chemical reaction. J.Phys. Soc. Jap. 32 1972
[Nis02] Nishida. Simulations of Photosynthesis by a K-Subset Transforming System with Membranes. Fund. Inf. 49 2002
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[PQ04] Priami, Quaglia. Beta binders for bio interactions. CMSB, LNCS 3082 2005.
[PQ05] Priami, Quaglia. Operational patterns in Beta-binders. TCSB 2005
[PRSS01] Priami, Regev, Silverman, Shapiro. Application of a stochastic passing-name calculus to representation and simulation of molecular processes. IPL 80 2001
[PV94] Puri, Varaiya. Decidability of hybrid systems with rectangular differential inclusions. CAV, LNCS 818 1994
[QS82] Queille, Sifakis. Specification and verification of concurrent systems. Int. Symp. Prog. 1982
[RPSCS04] Regev, Panina, Silverman, Cardelli, Shapiro. BioAmbients: An Abstraction for Bio Compartments. TCS 325 2004
[RSS01] Regev, Silverman, Shapiro. Representation and Simulation of Biochemical Processes Using the pi-Calculus Process Algebra. Pacific Symp. Biocomp., WSP 2001
[SFTT01] Suzuki, Fujiwara, Tanaka, Takabayashi. Artificial Life Applications of a Class of P Systems: Abstract Rewriting Systems on Multisets, Multiset Processing. LNCS 2235 2001
[SOT03] Suzuki, Ogishima, Tanaka. Modeling the p53 signaling network by using P systems. M.C. 2003
[STT00] Suzuki, Takabayashi, Tanaka. Adaptive Behavior a Tritrophic Interactions Consisting of Plants, Herbivors and Carnivores. SAB 2000
[ST00] Suzuki, Tanaka. Chemical Evolution Among Artificial
Proto-cells. Artif. Life 7 2000
[VPCD03] Vasilco, Popescu, Chiurtu, Dascalu. The Architecture for Living Structures-A Possible Basis for Molecular Computing. LNCS 2597 2003
Keywords
SYSTEMS BIOLOGY, BIOCONCURRENCY, BIO-INSPIRED CALCULI AND MODELS

Bio-Inspired Systems and Calculi with Applications -- BISCA

Università di Pisa
Abstract
Abstract

We plan to develop and use methods typical of computer science for describing, analysing and implementing in silico biological systems.
Besides offering such a modelization, we expect that the interaction and organization mechanisms of living matter will suggest us new paradigms of computation and new formalisms beneficial to information technology in general.
In particular, the project will focus on languages and models for specifying biological systems, for simulating their behaviour and for verifying formally their properties.

We will mainly concentrate on the specification and analysis of intra- and inter-cellular interactions.
The description of these phenomena will include both qualitative aspects, e.g. cause-effect relationships, and quantitative aspects, e.g. those depending on time and probabilistic distributions typical of biological systems.
Quantitative and qualitative aspects require new enhancements of existing computer-based analysis techniques and the development of new ones.

We plan to use linguistic mechanisms and models, specifically tailored for describing and manipulating such aspects of living matter as biochemical reactions, metabolic pathways and membranes.
Therefore we may be required to adapt existing linguistic instruments and models and to develop new ones.

Our ideas and proposals will be tuned, tested and validated over case studies coming from >>>

Principal Investigator
Pierpaolo Degano Università degli Studi di PISA
Research Objectives
Our long term goal is twofold. First, we aim at discovering new models and theories within Computer Science inspired by the biological world, and at producing techniques and tools to deal with much more complex Information Technology problems than those addressable with current technology. Second, we plan to provide biologists with an environment for attacking problems at a system level, not addressable without using Information Technology. This environment will provide biologists with modelling, analysis and simulation tools capable of handling complex behaviour and of representing emerging properties.
We advocate thus a convergence between computer and life sciences, placing ourselves in the computational side of Systems Biology. This emerging paradigm of biology moves from the classical reductionist approach to a system level understanding of life, where unpredictable, complex behaviour show up. Essential to that shift is the ongoing change of focus in biology from structure to functionality. We claim that computer science will greatly contribute to a better understanding of the behaviour of bio-systems, as computer science deals with the “process of functioning” whereas, e.g. mathematics or physics deal with the “result of these functioning”. In our terminology, passing from structure to functions amounts to equipping syntax with a behavioural semantics. We thus call for a paradigm shift also on the bio-informatics side: we plan to develop models, languages and >>>

Timescale
24 months
National and international background
Base di Partenza – Modello A


The recent, often astonishing developments in biology have produced a huge amount of data on the structure of living matter; consider e.g. the success of the human genome project.
Less instead is known on the versatile biological functions that cells and their components display. Consequently, in the last years we have seen a shift from structure to functionality, and the growth of a new paradigm, that moves from the classical reductionist approach to a system level understanding of life. It is called systems biology [Kit02].

There is a general understanding in the scientific community that computer science will be as indispensable for biology as mathematics has been for physics. E.g., mapping the human genome would be impossible without computers, algorithms and syntax to model structures: it has been crucial representing DNA as a formal language over a four character alphabet and using search and matching algorithms over strings. Much in the same way, computer science appears to be essential for understanding the behaviour of living organisms: passing from structure to functions amounts to equipping syntax with semantics. Computational sciences use to deal with the “process of functioning” whereas mathematics deals with the “result of these functioning”. Therefore, computational sciences may be of great help to theorise the functioning of biological systems as a tangle of processes. Moreover >>>