LABORATORY OF JOE ZBILUT

Department of Molecular Biophysics & Physiology

Our main interest has been the understanding of biological signals. Most recently this has been with respect to 1) proteins; and 2) metabolic networks. Analysis of these signals has been guided by a recurrence-based approach: proteins are relatively short, nonlinear and nonstationary, thus precluding the uninformed use of more traditional signal analysis methods. This strategy seeks to uncover unforeseen “singularities” of hydrophobicity in proteins and related function. We believe these singularities also inform their interactions with other proteins and the related “networks.

Discussion of these singularities needs to be framed in the context of randomness vs determinsim. Traditionally, randomness and determinism have been viewed as being diametrically opposed, based on the idea that causality and determinism is complicated by “noise.” Although recent research has suggested that noise can have a productive role, it still views noise as a separate entity. Our work suggests that this not need to be so. instead, we trace the problem to traditional assumptions regarding dynamical equations and their need for unique solutions. If this requirement is relaxed, the equations admit for instability and stochasticity evolving from the dynamics itself. This allows for a decoupling from the “burden” of the past and provides insights into concepts such as predictability, irreversibility, adaptability, creativity and multi-choice behavior. This reformulation is especially relevant for biological sciences whose need for flexibility a propos of environmental demands is important to understand: this suggests that many system models are based on randomness and nondeterminism complicated with a little bit of determinism to ultimately achieve concurrent flexibility and stability. As a result, the statistical perception of reality is seen as being a more productive tool than classical determinism.

It is posited that singularities in the code identify important loci for protein folding. Additionally, singular behavior may determine the interactions of the proteins themselves. The singularities are dependent upon local factors, or boundary conditions, which force a stochastic understanding of the process in terms of combinatorial probabilities.

In a recent paper, using these approaches we suggest a rational explanation to protein scaling of contacts vs. length on the basis of both protein connectivity and hydrophobic constraints of residues compactness relative to surface volume. [Zbilut JP, Chua GH, Krishnan A, Bossa C, Rother K, Webber CL, Giuliani A (2007). A topologically related singularity suggests a maximum preferred size for protein domains. Proteins Structure Function & Bioinformatics 66: 621-629]. A website has been created where interested readers can submit their structure files online to get the predicted REC3D (ρ) value and plot it against the scaling curve. The data is based on previous CASP competitions. The URL is http://gosper.iab.keio.ac.jp

For a copy of recurrence programs, see CL Webber, Jr of the Physiology Department of Loyola University.

Click here for a bibliography of recurrence analysis maintained by Dr. Norbert Marwan.

For an overview of the approach see:

Zbilut JP, Chua GH, Krishnan A, Bossa C, Colafranceschi M, Giuliani A (2006). Entropic criteria for protein folding derived from recurrences: Six residues patch as the basic protein word. FEBS Lett 580: 4861-4864

Benigni R, Giuliani A, Zbilut JP, Ellis SW, Allorge D (2005). A signal analysis approach applied to the study of sequence, structure and function of the proteins. Current Computer-Aided Drug Design 2: 1-19.

Zbilut JP, Mitchell JC, Giuliani A,Marwan N, Webber Jr. CL (2004). Singular hydrophobicity patterns and net charge: A mesoscopic principle for protein aggregation/folding. Physica A 343: 348–358.).

Giuliani A, Benigni B, Zbilut J, Webber CL, Sirabella P, Colosimo P (2002). Nonlinear signal analysis methods in the elucidation of protein sequence/structure relationships. Chemical Reviews 102(5): 1471-1492.

Zbilut JP, Sirabella P, GiulianiA, ManettiC, ColosimoA, Webber, Jr CL (2002). Review of nonlinear analysis of proteins through recurrence quantification. Cell Biochemistry and Biophysics 36: 67-87.

Sirabella P, Giuliani A, Zbilut J, Colosimo A (2001). Recurrence quantification analysis and multivariate statistical methods in the study of protein sequences. Recent Res. Devel. Protein Eng. 1:261-275.

Monographs:

Zak M, Zbilut JP, Meyers RE (1997). From instability to intelligence: complexity and predictability in nonlinear dynamics. (Lecture Notes in Physics: New Series m 49). Springer Verlag, Berlin Heidelberg New York. (available at amazon.com)

Zbilut JP (2004). Unstable singularities and randomness: their importance in the complexity of physical, biological and social sciences. Elsevier, Amsterdam.

Zbilut JP (2004). Singolarità Instabili e Casualità. La loro Importanza nella Complessità delle Scienze Fisiche e Psico-sociali. FrancoAngeli, Milan.

Webber CL Jr, Zbilut JP (2005). Recurrence quantification analysis of nonlinear dynamical systems. In: Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences, Riley MA, Van Orden GC (Eds.) Found at URL, http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp (link)

Zbilut JP, Scheibel T (2006). Protein Folding-Misfolding: Some Current Concepts of Protein Chemistry. Nova Publishers.

Zbilut JP, Giuliani A (in press). Simplicity: The Latent Order of Complexity. Nova Publishers, New York. (link)

Selected Recent Publications

1. Bianciardi M, Sirabella P, Hagberg GE, Giuliani A, Zbilut JP, Colosimo A. (2007) Model-free analysis of brain fMRI data by recurrence quantification. Neuroimage 37: 489-503.

2. Orsucci F, Giuliani A, Webber, Jr C, Zbilut J, Fonagy P, Mazza M (2006). Combinatorics and synchronization in natural semiotics. Physica A 361:665-676

3. Benigni R, Giuliani A, Zbilut JP, Ellis SW, Allorge D (2006). A signal analysis approach to the study of sequence, structure and function of the proteins. Current Computer-Aided Drug Design 2: 1-19.

4. Zbilut JP, Webber, CL, (April 14, 2006). Recurrence quantification analysis. In: Metin Akay, Ed., Wiley Encyclopedia of Biomedical Engineering. Hoboken. John Wiley & Sons. DOI: 10.1002/9780471740360.ebs1355

5. Zbilut JP, Scheibel T, Huemmerich D, Webber Jr CL, Colafranceschi M, Giuliani A (2005). Statistical approaches for investigating silk properties. Applied Physics A 82: 243-251.

6. Zbilut JP, Scheibel T, Huemmerich D, Colafranceschi M, Giuliani A (2005). Spatial stochastic resonance in proteins. Physics Letters A 346: 33-41.

7. Colafranceschi M, Colosimo A, Zbilut JP, Uversky VN, Giuliani A (2005). Structure-related statistical singularities along protein sequences: A correlation study. J Chem Inf Model 45: 183 -189.

8. Valerio M, Colosimo A, Conti F, Giuliani A, Grottesi A, Manetti C, Zbilut JP (2005). Early events in protein aggregation: molecular flexibility and hydrophobicity/charge interaction in amyloid peptides as studied by molecular dynamics simulations. Proteins Structure Function and Bioinformatics 58:110-118.

9. Zbilut JP, Mitchell JC, Giuliani A, Colosimo A, Marwan N, Colafranceschi M, Webber Jr CL (2005). Aggregation propensity of proteins quantified by hydrophobicity patterns and net charge. In: Benigni R, Colosimo A, Giuliani A, Sirabella P, Zbilut J (2005). International Meeting, Complexity in the Living: A Problem-Oriented Approach. Rome September 28-30, 2004. (Rapporti ISTISAN 05/20). Istituto Sueriore di Sanita, Rome, pp 136-151.

10. Bianciardi M, Sirabella P, Hagberg GE, Giuliani A, Zbilut JP, Colosimo A (2005). Analyzing spatial distributions of fMRI “bold” signals by RQA variables. In: Benigni R, Colosimo A, Giuliani A, Sirabella P, Zbilut J (2005). International Meeting, Complexity in the Living: A Problem-Oriented Approach. Rome September 28-30, 2004. (Rapporti ISTISAN 05/20). Istituto Sueriore di Sanita, Rome, pp 238-243.

11. Webber CL, Zbilut JP (2005). Recurrence quantification analysis of nonlinear dynamical systems. In: MA Riley & GC Van Orden (Eds.). Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences . http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp

12. Zbilut JP, Giuliani A. Algorithmic complexity (2004). In: Encyclopedia of Nonlinear Science (Scott, A, Ed). Routledge, New York and London .

13. Zbilut JP, Giuliani A, Colosimo A, Mitchell JC, Colafranceschi M, Marwan N, Uversky,VN, Webber CL Jr (2004). Charge and hydrophobicity patterning along the sequence predicts the folding mechanism and aggregation of proteins: A computational approach. J Proteome Res 3:1243-1253.

14. Zbilut JP, Mitchell JC, Giuliani A, Marwan N, Webber Jr. CL (2004). Singular hydrophobicity patterns and net charge: A mesoscopic principle for protein aggregation/folding. Physica A 343: 348–358.

15. Conte E, Federici A, Zbilut JP (2004). On a simple case of possible non-deterministic chaotic behavior in compartment theory of biological observables. Chaos, Solitons & Fractals 22:277-284.

16. Manetti C, Castro C, Zbilut JP (2004). Application of trilinear SLICING to analyse a single relaxation curve. Journal of Magnetic Resonance 168:273-7.

17. Porrello A, Soddu S, Zbilut JP, Crescenzi M, Giuliani A, (2004). Discrimination of single amino acid mutations of the p53 protein by means of deterministic singularities of recurrence quantification analysis. Proteins Structure Function & Genetics 55: 743-755

18. Giuliani A, Zbilut JP, Conti F, Manetti C, Miccheli A (2004). Invariant features of metabolic networks: a data analysis application on scaling properties of biochemical pathways. Physica A: Statistical and Theoretical Physics 337: 157-170.

19. Conte E, Federici A, Khrennikov A, Zbilut JP, (2004). Is determinism the basic tenet in dynamics of biological matter? Procedings of the International Conference on QuantumTheory, University of Vaxjo,Sweden,June 1-6, 2003.

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