Systems -> Engineering -> Synthetic Biology


Systems Biology Reviews:
IN SILICO
Ideker, T., Galitski, T., Hood, L., 2001. A new approach to decoding life: Systems Biology. Annu. Rev. Genomics Hum. Genet. 2, 343.
Kitano, H., 2002. Systems Biology: a brief overview. Science 295, 1662.
Palsson, B., 2000. The challenges of in silico biology. Nature Biotech. 18, 1147.
Strange, K. 2005. The end of "naive reductionism": rise of systems biology or renaissance of physiology?. Am J Physiol Cell Physiol 288: C968-C974
Mathematical modeling: dynamical&functional properties of cells
Novak, B., Tyson, J. J., 1995. Quantitative analysis of a molecular model of mitotic control in fission yeast. J. Theor. Bio. 173, 283.
Graña, M., Acerenza, L., 2001. A model combining cell physiology and population genetics to explain Escherichia coli laboratory evolution. BMC Evolutionary Biology 1, 12.
Browning, S. T., Shuler, M. L., 2001. Towards the Development of a Minimal Cell Model by Generalization of a Model of Escherichia coli: Use of Dimensionless Rate Parameters. Biotechnol. Bioeng. 76, 187.
Tyson, J. J., Chen, C. K., Novak, B., 2003. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15, 221.
Endy, D., Brent, R., 2001. Modelling cellular behaviour. Nature 409, 391– 395.
Gardner et al. 1998, A theory for controlling cell cycle dynamics using a reversibly binding inhibitor, PNAS 95, 14190
Goldbeter, 2002, Computational approaches to cellular rhythms, Nature 420, 238
Computational Biology Reviews
Slepchenko, B. M., Schaff, J. C., Carson, J. H., Loew, L. M., 2002. Computational cell biology: spatiotemporal simulation of cellular events. Annu. Rev. Biomol. Struct. 31, 423–441.
Lemerle, C., Di Ventura, B., Serrano, L., 2005. Space as the final frontier in stochastic simulations of biological systems. FEBS Lett. 579, 1789–1794.
McAdams, H. H., Arkin, A., 1998. Simulation of prokaryotic genetic circuits. Annu. Rev. Biophys. Biomol. Struct. 27, 199
Turner et al. 2004. Stochastic approaches for modelling in vivo reactions,Comput. Biol. Chem. 28, 165
Deterministic temporal modeling
De Jong, H., 2002. Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. J. Comput. Phys. 9, 67–103.
Morgan, Surovtsev, Lindahl, 2004, A framework for whole-cell mathematical modeling, J. Theor. Biol.
Stochastic temporal modeling
StochSim
Deterministic spatio-temporal modeling VirtualCell
Stochastic spatio-temporal modeling Stundzia, A. B., Lumsden, C. J., 1996. Stochastic simulation of coupled reaction-diffusion processes. J. Comput. Phys. 127, 196–207.
Andrews, S. S., Bray, D., 2004. Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys. Biol. 1, 137–151.

Noise effects
Shinbrot & Muzzio, 2001, Noise to order, Nature 410, 251
Hasty, J., Pradines, J., Dolnik, M., Collins, J., 2000. Noise-based switches and amplifiers for gene expression. Proc. Nat. Acad. Sci. USA 97, 2075– 2080.
McAdams, H. H., Arkin, A., 1999. It’s a noisy business! Trends Genet. 15, 65–69.
Paulsson, J., 2004. Summing up noise in gene networks. Nature 427, 415–418.
Rao, C. V., Wolf, D. M., Arkin, A. P., 2002. Control, exploitation and tolerance of intracellular noise. Nature 420, 231–237.
Chen et al., 2005, Noise-induced cooperative behavior in a multicell system, Bioinformatics 21(11), 2722
Gonze et al., 2002, Robustness of circadian rhythms with respect to molecular noise, PNAS 99(2), 673
Brandman, Ferrell, Li, Meyer, 2005, Interlinked fast and slow positive feedbacks loops drive reliable cell decisions, Science 310, 496
Stochasticity in genes
Kaern, M., Elston, T. C., Blake, W. J., Collins, J. J., 2005. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464.
McAdams, H. H., Arkin, A., 1997. Stochastic mechanisms in gene expression. Proc. Natl. Acad. Sci. USA 94, 814–819.
Pedraza, J. M., van Oudenaarden, A., 2005. Noise Propagation in Gene Networks. Science 307, 1965–1969.
Networs dynamics
Dynetica;
Arkin, A., Ross, J., 1994. Computational Functions in Biochemical Reaction Networks. Biophys. J. 67, 560.
Fran¸cis, P., Hakim, V., 2004. Design of genetic networks with specified functions by evolution in silico. Proc. Nat. Acad. Sci. USA 101, 580–585.

IN VIVO Biology: Bioengineering?
Kaern, M., Blake, W. J., Collins, J. J., 2003. The engeneering of gene regulatory networks. Annu. Rev. Biomed. Eng. 5, 179–206.
Simpson, M. L., 2004. Rewiring the cell: synthetic biology moves towards higher functional complexity. Trends Biotech. 22, 555.
Endy, 2005, Foundations for engineering biology, Nature 438, 449
Nature-inspired design of complex biological system
Blake, W. J., Isaacs, F. J., 2004. Synthetic biology evolves. Trends Biotech. 22, 321.
Design of genetic circuits:
Wall, M. E., Hlavacek, W. S., Savageau, M. A., 2004. Design of gene circuits: lessons from bacteria. Nat. Rev. Genet. 5, 34–42.
Yokobayashi, Y., Collins, C. H., Leadbetter, J. R., Weiss, R., Arnold, F. H.,2003. Evolutionary design of genetic circuits and cell-cell communications. Adv. Complex Syst. 6, 1.
Sprinzak, D., Elowitz, M. B., 2005. Reconstruction of genetic circuits. Nature 438, 443–448.
Cherry & Adler, 2000, How to make a biological switch, J. Theor. Biol. 203, 117
IN VITRO Biology:
Synthetic Biology Reviews
Benner, S. A., Sismour, A. M., 2005. Synthetic Biology. Nature Rev. Genetics 6, 533.
McDaniel, R., Weiss, R., 2005. Advances in synthetic biology: on the path from prototypes to applications. Curr. Opin. Biotech. 16, 476.
Brent, R., 2004, A partnership between biology and engineering, Nature Biotech. 22(10), 1211
McDaniel, R., Weiss, R., 2005. Advances in synthetic biology: on the path from prototypes to applications. Curr. Opin. Biotech. 16, 476.
J. Hasty, D. McMillen, F. Isaacs, and J. J. Collins, Computational studies of gene regulatory networks: in numero molecular biology, Nature Reviews Genetics, vol. 2, no. 4, pp. 268-- 279, 2001.
Design & control of cellular function
Gardner, T. S., Cantor, C. R., Collins, J. J., 2000. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339.
Ferber, D., 2004. Microbes made to order. Science 303, 158–161.
Hasty, J., McMillen, D., Collins, J., 2002. Engineered gene circuits. Nature 420, 224–230.
Fung et al., 2005, A synthetic gene–metabolic oscillator, Nature 435, 118
Jordi Garcia-Ojalvo, Michael B. Elowitz, and Steven H. Strogatz, 2004, Modeling a synthetic multicellular clock:
Repressilators coupled by quorum sensing
, PNAS 101(30), 10955
Minimal self-replicating systems:
reviews
Pohorille, A., Deamer, D., 2002. Artificial cells: prospects for biotechnology. Trends Biotech. 20, 123.
von Kiedrowski, G., 1993. Minimal replicator theory I: Parabolic versus exponential growth. Bioorganic Chemistry Frontiers 3, 113–146. (fotocopias)
Luisi, P. L., 2002. Towards the engineering of minimal living cells. Anat. Rec. 268, 208.

Minimal self-replicating systems:models
Browning, S. T., Shuler, M. L., 2001. Towards the Development of a Minimal Cell Model by Generalization of a Model of Escherichia coli: Use of Dimensionless Rate Parameters. Biotechnol. Bioeng. 76, 187.
Iglesias & Lepchenko, 2002, Modeling the Cell’s Guidance System, Science STKE 148
Minimal self-replicating systems: membrane
Hanczyc, M., Szostak, J., 2004. Replicating vesicles as models of primitive cell growth and division. Curr.Op. Chem. Biol. 8, 660.
Monnard & Deamer, 2002, Membrane self-assembly processes: steps toward the first cellular life, Anat. Rec. 268, 168
Deamer, D., Dworkin, J., Sandford, S., Bernstein, M., Allamandola, L., 2002. The first cell membranes. Astrobiology 2, 371–381.
Chen, Szostak, 2004 , Membrane growth can generate a transmembrane pH gradient in fatty acid vesicles, PANS 101(21), 7965
Pohorille et al., 2005, The Origin and Early Evolution of Membrane Channels, Astrobiology, 5(1), 1
Minimal self-replicating systems: "genome" von Kiedrowski, G., 1986. A self-replicating hexadeoxynucleotide. Angew. Chem. Int. Edn. Engl. 25, 932.
Lee et al. 1997, Emergence of symbiosis in peptide self-replication through hypercyclic network, Nature 390, 591
Nowick et al. 1991, Kinetic studies and modeling of a self-replicating system, J. Am. Chem. Soc. 113, 8831
Minimal self-replicating systems: metabolism Morowitz, H., 1999, A theory of biochemical organization, metabolic pathways and evolution, Complexity 4(6), 39
Selkov, E, 1975, Stabilization of energy charge, generation of scillatiosna and multiple steady statesin energy metabolism as a result of purely stoichiometricregulation, Eur. J. Biocehm. 59, 151
Monnard PA, Deamer DW., Nutrient uptake by protocells: a liposome model system., Orig Life Evol Biosph. 2001 31(1-2), 147-55.
Minimal self-replicating systems: membrane+"genome" Hanczyc, M. M., Fujikawa, S. M., Szostak, J., 2003. Experimental Models of Primitive Cellular Compartments: Encapsulation, Growth, and Division. Science 302, 618–622.
Oberholzer et al. 1999, Protein expression in liposomes, Biochem. Biophys. Res. Comm. 261, 238
Oberholzer et al. 1995, Enzymatic RNA expression in self-reproducing vesicles: an approach to a minimal cell, Biochem. Biophys. Res. Comm. 207, 250
Chen, Roberts, Szostak, 2004, The emergence of competition between model protocells, Science 305, 1474
Bottom-up design
Rasmussen, S., Chen, L., Nilsson, M., Abe, S., 2003. Bridging nonliving and living matter. Artificial Life 9, 269.
Rasmussen, S., Chen, L., Stadler, B. M., Stadler P., 2002,Proto-Organism Kinetics: Evolutionary Dynamics of Lipid Aggregates , Orig. Life Evol. Biosph.
Top-down design
Szostak, J. W., Bartel, D. P., Luisi, P. L., 2001. Synthesizing life. Nature 409, 387.
Luisi, P. L., 2002. Towards the engineering of minimal living cells. Anat. Rec. 268, 208.
Castellanos, L., Hoonlor, A., Yin, J., 2004. A modular minimal cell model: Purine and pyrimidine transport and metabolism . Proc. Nat. Acad. Sci. USA 101, 6681–6686.
Noireaux, V., Libchaber, A., 2004. A vesicle bioreactor as a step toward an artificial cell assembly. Proc. Natl. Acad. Sci. USA 101, 17669–17674.
Oberholzer et al. 1995, Enzymatic RNA expression in self-reproducing vesicles: an approach to a minimal cell, Biochem. Biophys. Res. Comm. 207, 250
Luisi, Ferri, Stano, 2005, Approaches to semi-synthetic minimal cells: a review, Naturwissenschaften
Minimal gene set
Koonin, E. V., 2000. HOW MANY GENES CAN MAKE A CELL: The Minimal-Gene-Set Concept. Annu. Rev. Genomics Hum. Genet 1, 99–116.
Gil, R., Silva, F., Pereto, J., Moya, A., 2004. Determination of the core of a minimal bacterial gene set. Microbiol Mol Biol Rev. 68, 518–537.
Cellular Computing

Amos, M. (Ed.), 2004. Cellular Computing. Oxford Univ. Press.
Hayes, B., 2001. Computing comes to life. American Scientist 89, 204.
Weiss, R., Basu, S., Hooshangi, S., Kalmbach, A., Karig, D., Mehreja, R., Netravali, I., 2003. Genetic circuit building blocks for cellular computation, communications, and signal processing. Nat. Computing 2, 47.
Isaacs et al., 2005, Signal processing in single cells, Science 307, 1886
Weiss et al. Cellular computation and communication using engineered geentic regulatory networks
Simpson, Sayler, Fleming, Applegate, 2001, Whole-cell biocomputing, Trends Biotech. 19(8), 317
Arkin, A., Ross, J., 1994, Computational functions in biochemical reaction networks, Biophys. J., 67, 560
Bray, D., 1995, Protein molecules as computational elements in living cells, nature, 376, 307
Programmable cell Weiss, R., 2003. Challenges and opportunities in programming living cells. The Bridge 33, 39.
You, L., Cox III, R. S., Weiss, R., Arnold, F. H., 2004. Programmed population control by cell-cell communication and regulated killing. Nature 428, 868.
Kobayashi, H., Kaern, M., Araki, M., Chung, K., gardner, T. S., Cantor, C. R., Collins, J. J., 2004. Programmable cells: Interfacing natural and engineered gene networks. Proc. Nat. Acad. Sci. USA 101, 8414–8419.
Basu et al. 2005, A synthetic multicellular system for programmed pattern formation, Nature 434, 1130
Kobayashi et al., 2004, Programmable cells: Interfacing natural and engineered gene networks, PNAS 101(22), 8414
Software
Shapiro, B. E., Levchenko, A., Meyerowitz, E. M., Wold, B. J., Njolsness, E. D., 2002. Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations. Bioinformatics 19, 677–678.
Tomita, M., Hashimoto, K., Takayashi, K., Shimizu, T. S., Matsuzaki, Y., Miyoshi, F., Saito, K., Tanida, S., Yugi, K., Venter, J. C., Hutchinson 3rd, C. A., 1999. E-CELL: software environment for whole-cell simulation. Bioinformatics 15, 72–84.
StochSim: Morton-Firth, C. J., Bray, D., 1998. Predicting remporal fluctuations in a intracellular signalling pathway. J. Theor. Biol. 192, 117–128.
VirtualCell: Schaff, J., Fink, C. C., Slepchenko, B., Carson, J. H., Loew, L. M., 1997. A general computational framework for modeling cellular structure and function. Biophys. J. 73, 1135–1146.
MCell: Stiles, J. R., Bartol, T. M., 2001. Monte carlo methods for simulating realistic synaptic microphysiology using mcell. In: De Schutter, E. (Ed.), Computational Neuroscience: Realistic Modeling for Experimentalists. CRC Press, Boca Raton, pp. 87–127.
Ander, M., Beltrao, P., Di Ventura, B., Ferkinghoff-Borg, J., Foglierini, M., Kaplan, A., Lemerle, C., Toma’s-Oliveira, I., Serrano, L., 2004. Smart-Cell, a framework to simulate cellular processes that combines stochastic approximation with diffusion and localisation: analysis of simple networks. Syst. Biol. 1, 129–138.
You, L., Hoonlor, A., Yin, J., 2003. Modeling biological systems using Dynetica - a simulator of dynamic networks. Bioinformatics 19, 435–436.
Origin of life: energy approach
Deamer, D. 1997, The first living cells: a bioenergetic perspective, Microbio. Mol. Bio. Rev. 61(2), 239
Berry, S. 2002, The chemical basis of membrane bioenergetics, J. Mol. Evol 54 595-613: Trata la bioenergética de membranas en el contexto del origen de la vida: importancia y ubicuidad de la mebrana para generar potenciales, importancia de un proto-"metabolismo"  basado en "cofactores" links entre mundo del RNA-energía-metabolismo y transporte.
Turing patterns
Hasslacher et a., 1993, Molecular Turing structures in the biochemistry of the cell, Chaos 3(1), 7


Red: Educational relevance