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COMPUTER PROGRAMS
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FCF:
Elucidates the topological and flux connectivity features of genome-scale
metabolic networks and enables the global identification of blocked
reactions, equivalent knockouts, and sets of affected reactions. The FCF
computational procedure allows one to determine whether any two metabolic
fluxes, v1 and v2, are (i) fully coupled, if a non-zero flux for v1 implies a
non-zero, but also a fixed flux for v2 and vice versa; (ii) partially coupled,
if a non-zero flux for v1 implies a non-zero, though variable, flux for v2
and vice versa; or (iii) directionally coupled, if a non-zero flux for v1
implies a non-zero flux for v2 but not necessarily the reverse.
Burgard, A.P.#,
E.V. Nikolaev #, C.H. Schilling, and
C.D. Maranas (2004), "Flux Coupling Analysis
of Genome-scale Metabolic Reconstructions," Genome Research,
14, 301-312. [600 kb]
# These authors contributed equally to this
work.
Click here to see sample input file #1.
Click here to see sample input file #2.
Implemented in Lindo and STL C++.
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OptFolio:
Utilizes Real Options Valuation (ROV) to select optimal pharmaceutical
R&D portfolios under changing conditions (e.g., resource constraints,
market uncertainty, technical uncertainty, etc.). Provides a roadmap for
future decisions by modeling new product development as a series of
continuation/abandonment options and tracking the decision of
abandonment over the course of the planning horizon.
Rogers, M.J., Gupta, A. and C.D. Maranas (2002), "Real Options Based Analysis of
Optimal Pharmaceutical R&D Portfolios," submitted to Industrial & Engineering Chemistry Research.
Click here to see some sample output.
Implemented in GAMS.
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eShuffle:
Predicts statistics and distribution of
crossovers for (family) DNA shuffling under different experimental
conditions (e.g., annealing temperature, fragmentation length,
DNA concentration, salt concentration, etc.).
Moore, G.L., C.D. Maranas, S. Lutz, and S.J. Benkovic (2001), "Predicting Crossover
Generation in DNA Shuffling,"
Proc. Natl. Acad. Sci. USA 98, 3226-3231.
Click here to see some sample output.
Implemented in FORTRAN 90 (ReadMe.txt).
The zip archive of eShuffle contains the software and associated scripts.
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eSCRATCHY:
Predicts statistics and distribution of
crossovers for SCRATCHY under different experimental
conditions (e.g., annealing temperature, fragmentation length,
DNA concentration, salt concentration, etc.).
Lutz, S., M. Ostermeier, G.L. Moore, C.D. Maranas, and S.J. Benkovic (2001), "Creating multiple-crossover DNA libraries
independent of sequence identity,"
Proc. Natl. Acad. Sci. USA, 98, 11248-11253.
Click here to see some sample output.
Implemented in FORTRAN 90 (ReadMe.txt).
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eCodonOpt:
Identifies optimal codon usage for parental genes to be
recombined. Three separate objectives are considered:
(i) maximizing the average number of crossovers per recombined sequence;
(ii) minimizing bias in family DNA shuffling so that each of the
parental sequence pair contributes a similar number of crossovers
to the library; and (iii) maximizing the relative frequency of
crossovers in specific structural regions.
Moore, G.L. and C.D. Maranas (2002), "eCodonOpt: A Systematic
Computational Framework for Optimizing Codon Usage in Directed
Evolution Experiments," Nucleic Acids Research, accepted.
Click here to see some sample output.
Implemented in GAMS & FORTRAN 90
(ReadMe.txt).
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ALGORITHMS
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Ab Initio Library Design:
Here we explore the development of methods for designing ab initio libraries
(to be built using degenerate oligonucleotides) without restricting amino acid usage to
the ones found among the set of parental sequences. This challenging design task will be
approached in two ways. First, we will use stochastic optimization methods
(e.g., Monte Carlo algorithm) to maximize the stability of the library. The key idea
here is that stability is a prerequisite though not necessarily a monotonic descriptor
of protein function. Second, although functionality is challenging to assess computationally,
motivated by the success of FamClash (Saraf et al., 2004) to a priori rank hybrids with respect to
their activities, we will use protein family sequence data to identify what residue or residues "fit" well in every position along the protein sequence. The framework will essentially select for residues
with physical properties that are most in tune with the ones predominantly encountered in the protein
family. We plan to compare and contrast the results from these two complementary approaches to
construct a consensus stability-functionality library.
Protein design using atomistic structural calculations
Library design using sequence data-driven approach
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Optimal Tiling of Parental Segments:
Identifies optimal junction points for parental segments and the set
of parental sequences that can contribute fragments in different locations along the sequence
for designing combinatorial library using oligonucleotide ligation-based protocols such as GeneReassembly,
SISDC, and many others.
Library design via optimal tiling of parental segments
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For licensing/evaluation copy information:
Contact Costas D. Maranas (costas@psu.edu).
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