Browsing by Author "Pekergin, Nihal"

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Citation - WoS: 1
Citation - Scopus: 2
Aggregation for Computing Multi-Modal Stationary Distributions in 1-D Gene Regulatory Networks
(IEEE COMPUTER SOC, 2018) Neslihan Avcu; Nihal Pekergin; Ferhan Pekergin; Cuneyt Guzelis; Pekergin, Nihal; Pekergin, Ferhan; Avcu, Neslihan; Guzelis, Cuneyt
This paper proposes aggregation-based three-stage algorithms to overcome the numerical problems encountered in computing stationary distributions and mean first passage times for multi-modal birth-death processes of large state space sizes. The considered birth-death processes which are defined by Chemical Master Equations are used in modeling stochastic behavior of gene regulatory networks. Computing stationary probabilities for a multi-modal distribution from Chemical Master Equations is subject to have numerical problems due to the probability values running out of the representation range of the standard programming languages with the increasing size of the state space. The aggregation is shown to provide a solution to this problem by analyzing first reduced size subsystems in isolation and then considering the transitions between these subsystems. The proposed algorithms are applied to study the bimodal behavior of the lac operon of E. coli described with a one-dimensional birth-death model. Thus the determination of the entire parameter range of bimodality for the stochastic model of lac operon is achieved.
Citation - Scopus: 2
Bayesian Networks as Approximations of Biochemical Networks
(Springer Science and Business Media Deutschland GmbH, 2023) Adrien L. Le Coënt; Benoît Barbot; Nihal Pekergin; Cüneyt Güzeliş; Barbot, Benoît; Pekergin, Nihal; Le Coënt, Adrien; Güzeliş, Cüneyt; M. Iacono , M. Scarpa , S. Serrano , F. Longo , E. Barbierato , D. Cerotti
Biochemical networks are usually modeled by Ordinary Differential Equations (ODEs) that describe time evolution of the concentrations of the interacting (biochemical) species for specific initial concentrations and certain values of the interaction rates. The uncertainty in the measurements of the model parameters (i.e. interaction rates) and the concentrations (i.e. state variables) is not an uncommon occurrence due to biological variability and noise. So there is a great need to predict the evolution of the species for some intervals or probability distributions instead of specific initial conditions and parameter values. To this end one can employ either phase portrait method together with bifurcation analysis as a dynamical system approach or Dynamical Bayesian Networks (DBNs) in a probabilistic domain. The first approach is restricted to the case of a few number of parameters while DBNs have recently been used for large biochemical networks. In this paper we show that time-homogeneous ODE parameters can be efficiently estimated with Bayesian Networks. The accuracy and computation time of our approach is compared to two-slice time-invariant DBNs that have already been used for this purpose. The efficiency of our approach is demonstrated on two toy examples and the EGF-NGF signaling pathway. © 2023 Elsevier B.V. All rights reserved.