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Publications about 'algorithms'
Articles in journal or book chapters
  1. T. Kang, J.T. White, Z. Xie, Y. Benenson, E.D. Sontag, and L. Bleris. Reverse engineering validation using a benchmark synthetic gene circuit in human cells. ACS Synthetic Biology, 2:255-262, 2013. [PDF] Keyword(s): reverse engineering, systems biology, synthetic biology.
    Abstract:
    This work introduces an experimental platform customized for the development and verification of reverse engineering and pathway characterization algorithms in mammalian cells. Specifically, we stably integrate a synthetic gene network in human kidney cells and use it as a benchmark for validating reverse engineering methodologies. The network, which is orthogonal to endogenous cellular signaling, contains a small set of regulatory interactions that can be used to quantify the reconstruction performance. By performing successive perturbations to each modular component of the network and comparing protein and RNA measurements, we study the conditions under which we can reliably reconstruct the causal relationships of the integrated synthetic network.


  2. R. Albert, B. Dasgupta, and E.D. Sontag. Inference of signal transduction networks from double causal evidence. In David Fenyö, editor, Computational Biology, Methods in Molecular Biology vol. 673, pages 239-251. Springer, 2010. [PDF] Keyword(s): systems biology, biochemical networks, algorithms, signal transduction networks, graph algorithms.
    Abstract:
    We present a novel computational method, and related software, to synthesize signal transduction networks from single and double causal evidence.


  3. E.D. Sontag and D. Zeilberger. A symbolic computation approach to a problem involving multivariate Poisson distributions. Advances in Applied Mathematics, 44:359-377, 2010. Note: There are a few typos in the published version. Please see this file for corrections: https://drive.google.com/file/d/0BzWFHczJF2INUlEtVkFJOUJiUFU/view. [PDF] Keyword(s): probability theory, stochastic systems, systems biology, biochemical networks, Chemical Master Equation.
    Abstract:
    Multivariate Poisson random variables subject to linear integer constraints arise in several application areas, such as queuing and biomolecular networks. This note shows how to compute conditional statistics in this context, by employing WZ Theory and associated algorithms. A symbolic computation package has been developed and is made freely available. A discussion of motivating biomolecular problems is also provided.


  4. R. Albert, B. Dasgupta, R. Dondi, and E.D. Sontag. Inferring (biological) signal transduction networks via transitive reductions of directed graphs. Algorithmica, 51:129-159, 2008. [PDF] [doi:10.1007/s00453-007-9055-0] Keyword(s): systems biology, biochemical networks, algorithms, signal transduction networks, graph algorithms.
    Abstract:
    The transitive reduction problem is that of inferring a sparsest possible biological signal transduction network consistent with a set of experimental observations, with a goal to minimize false positive inferences even if risking false negatives. This paper provides computational complexity results as well as approximation algorithms with guaranteed performance.


  5. S. Kachalo, R. Zhang, E.D. Sontag, R. Albert, and B. Dasgupta. NET-SYNTHESIS: A software for synthesis, inference and simplification of signal transduction networks. Bioinformatics, 24:293 - 295, 2008. [PDF] Keyword(s): systems biology, biochemical networks, algorithms, signal transduction networks, graph algorithms.
    Abstract:
    This paper presents a software tool for inference and simplification of signal transduction networks. The method relies on the representation of observed indirect causal relationships as network paths, using techniques from combinatorial optimization to find the sparsest graph consistent with all experimental observations. We illustrate the biological usability of our software by applying it to a previously published signal transduction network and by using it to synthesize and simplify a novel network corresponding to activation-induced cell death in large granular lymphocyte leukemia.


  6. R. Albert, B. DasGupta, R. Dondi, S. Kachalo, E.D. Sontag, A. Zelikovsky, and K. Westbrooks. A novel method for signal transduction network inference from indirect experimental evidence. In R. Giancarlo and S. Hannenhalli, editors, 7th Workshop on Algorithms in Bioinformatics (WABI), volume 14, pages 407-419. Springer-Verlag, Berlin, 2007. Note: Conference version of journal paper with same title. Keyword(s): systems biology, biochemical networks, algorithms, signal transduction networks, graph algorithms.


  7. R. Albert, B. DasGupta, R. Dondi, S. Kachalo, E.D. Sontag, A. Zelikovsky, and K. Westbrooks. A novel method for signal transduction network inference from indirect experimental evidence. Journal of Computational Biology, 14:927-949, 2007. [PDF] Keyword(s): systems biology, biochemical networks, algorithms, signal transduction networks, graph algorithms.
    Abstract:
    This paper introduces a new method of combined synthesis and inference of biological signal transduction networks. The main idea lies in representing observed causal relationships as network paths, and using techniques from combinatorial optimization to find the sparsest graph consistent with all experimental observations. The paper formalizes the approach, studies its computational complexity, proves new results for exact and approximate solutions of the computationally hard transitive reduction substep of the approach, validates the biological applicability by applying it to a previously published signal transduction network by Li et al., and shows that the algorithm for the transitive reduction substep performs well on graphs with a structure similar to those observed in transcriptional regulatory and signal transduction networks.


  8. P. Berman, B. Dasgupta, and E.D. Sontag. Algorithmic issues in reverse engineering of protein and gene networks via the modular response analysis method. Annals of the NY Academy of Sciences, 1115:132-141, 2007. [PDF] Keyword(s): systems biology, biochemical networks, gene and protein networks, reverse engineering, systems identification, graph algorithms.
    Abstract:
    This paper studies a computational problem motivated by the modular response analysis method for reverse engineering of protein and gene networks. This set-cover problem is hard to solve exactly for large networks, but efficient approximation algorithms are given and their complexity is analyzed.


  9. P. Berman, B. Dasgupta, and E.D. Sontag. Randomized approximation algorithms for set multicover problems with applications to reverse engineering of protein and gene networks. Discrete Applied Mathematics Special Series on Computational Molecular Biology, 155:733-749, 2007. [PDF] Keyword(s): systems biology, biochemical networks, gene and protein networks, systems identification, reverse engineering.
    Abstract:
    This paper investigates computational complexity aspects of a combinatorial problem that arises in the reverse engineering of protein and gene networks, showing relations to an appropriate set multicover problem with large "coverage" factor, and providing a non-trivial analysis of a simple randomized polynomial-time approximation algorithm for the problem.


  10. B. DasGupta, G.A. Enciso, E.D. Sontag, and Y. Zhang. Algorithmic and complexity aspects of decompositions of biological networks into monotone subsystems. BioSystems, 90:161-178, 2007. [PDF] [doi:http://dx.doi.org/10.1016/j.biosystems.2006.08.001] Keyword(s): monotone systems, systems biology, biochemical networks.
    Abstract:
    A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio inapproximability results. One of the algorithms, which has a worst-case guarantee of 87.9% from optimality, is based on the semidefinite programming relaxation approach of Goemans-Williamson. The algorithm was implemented and tested on a Drosophila segmentation network and an Epidermal Growth Factor Receptor pathway model.


  11. B. DasGupta, J.P. Hespanha, J. Riehl, and E.D. Sontag. Honey-pot constrained searching with local sensory information. Nonlinear Analysis, 65:1773-1793, 2006. [PDF] Keyword(s): search problems, algorithms, computational complexity.
    Abstract:
    This paper investigates the problem of searching for a hidden target in a bounded region of the plane by an autonomous robot which is only able to use limited local sensory information. It proposes an aggregation-based approach to solve this problem, in which the continuous search space is partitioned into a finite collection of regions on which we define a discrete search problem and a solution to the original problem is obtained through a refinement procedure that lifts the discrete path into a continuous one. The resulting solution is in general not optimal but one can construct bounds to gauge the cost penalty incurred. The discrete version is formalized and an optimization problem is stated as a `reward-collecting' bounded-length path problem. NP-completeness and efficient approximation algorithms for various cases of this problem are discussed.


  12. E.D. Sontag. Realization theory of discrete-time nonlinear systems. I. The bounded case. IEEE Trans. Circuits and Systems, 26(5):342-356, 1979. [PDF]
    Abstract:
    A state-space realization theory is presented for a wide class of discrete time input/output behaviors. Although In many ways restricted, this class does include as particular cases those treated in the literature (linear, multilinear, internally bilinear, homogeneous), as well ss certain nonanalytic nonlinearities. The theory is conceptually simple, and matrix-theoretic algorithms are straightforward. Finite-realizability of these behaviors by state-affine systems is shown to be equivalent both to the existence of high-order input/output equadons and to realizability by more general types of systems.


Conference articles
  1. B. DasGupta, J.P. Hespanha, and E.D. Sontag. Computational complexities of honey-pot searching with local sensory information. In Proceedings American Control Conf., Boston, June 2004, CD-ROM, ThA06.1, IEEE Publications, Piscataway, 2004. [PDF]
    Abstract:
    In this paper we investigate the problem of searching for a hidden target in a bounded region of the plane, by an autonomous robot which is only able to use limited local sensory information. We formalize a discrete version of the problem as a "reward-collecting" path problem and provide efficient approximation algorithms for various cases.


Internal reports
  1. E.D. Sontag and H.J. Sussmann. Optimization algorithms for image restoration and segmentation. Technical report 34, Rutgers Center for Computer Aids for Industrial Productivity, 1987.



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