Mahdi Kooshkbaghi

Cold Spring Harbor Laboratory

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Ioannis G. Kevrekidis

Johns Hopkins University

Christos E. Frouzakis

ETH Zurich

Justin B. Kinney

Cold Spring Harbor Laboratory

Konstantinos Boulouchos

ETH Zurich

David M. McCandlish

Cold Spring Harbor Laboratory

Felix Dietrich

Technical University of Munich

Mikko Karttunen

Western University

Noah J. Wichrowski

Johns Hopkins University

Yuma Ishigami

Cold Spring Harbor Laboratory

Thomas N. Thiem

Princeton University

Cooperative Institutions

Princeton University

Johns Hopkins University

Cold Spring Harbor Laboratory

Rutgers, The State University of New Jersey

Stony Brook University

Ackerman Institute for the Family

Rutgers Sexual and Reproductive Health and Rights

Memorial Sloan Kettering Cancer Center

Lomonosov Moscow State University

Technical University of Munich

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Generalized entropy production analysis for mechanism reduction

Figshare (2018)

Luigi Acampora Mahdi Kooshkbaghi Christos E. Frouzakis Francesco Saverio Marra

The paper introduces a generalized formulation for the computation of the relative contribution of each elementary reaction to the total entropy production, which has been proposed as a measure of the importance of elementary reactions and used for the reduction of detailed chemical reaction mechanisms. The reduction method is extended for the cases where the principle of detailed balance does not hold or apply, namely in the case of irreversible reactions or when the reverse rate constants are not computed via the thermodynamic equilibrium constants. Using a mechanism for n-butane consisting exclusively of reversible reactions, the new formulation is compared to the original one, and then applied for the construction of a skeletal mechanism for n-dodecane starting from a detailed mechanism which includes predominantly irreversible reactions. The skeletal scheme is found to accurately capture the ignition delay times over an extended range of pressure, initial temperature and equivalence ratio, the steady-state temperature as function of the residence time in a non-isothermal adiabatic perfectly stirred reactor, and the laminar flame speed of atmospheric flames at different unburned mixture temperatures and equivalence ratios.

Entropy production

10.6084/m9.figshare.7429577

Cite

Citations (0)

Emergent spaces for coupled oscillators

arXiv (Cornell University) (2020)

Thomas N. Thiem Mahdi Kooshkbaghi Tom Bertalan Carlo R. Laing Ioannis G. Kevrekidis

In this paper we present a systematic, data-driven approach to discovering "bespoke" coarse variables based on manifold learning algorithms. We illustrate this methodology with the classic Kuramoto phase oscillator model, and demonstrate how our manifold learning technique can successfully identify a coarse variable that is one-to-one with the established Kuramoto order parameter. We then introduce an extension of our coarse-graining methodology which enables us to learn evolution equations for the discovered coarse variables via an artificial neural network architecture templated on numerical time integrators (initial value solvers). This approach allows us to learn accurate approximations of time derivatives of state variables from sparse flow data, and hence discover useful approximate differential equation descriptions of their dynamic behavior. We demonstrate this capability by learning ODEs that agree with the known analytical expression for the Kuramoto order parameter dynamics at the continuum limit. We then show how this approach can also be used to learn the dynamics of coarse variables discovered through our manifold learning methodology. In both of these examples, we compare the results of our neural network based method to typical finite differences complemented with geometric harmonics. Finally, we present a series of computational examples illustrating how a variation of our manifold learning methodology can be used to discover sets of "effective" parameters, reduced parameter combinations, for multi-parameter models with complex coupling. We conclude with a discussion of possible extensions of this approach, including the possibility of obtaining data-driven effective partial differential equations for coarse-grained neuronal network behavior.

Ode

Kuramoto Model

Manifold (fluid mechanics)

10.48550/arxiv.2004.06053

Cite

Citations (0)

Manifold learning for parameter reduction

Journal of Computational Physics (2019)

Alexander Holiday Mahdi Kooshkbaghi Juan M. Bello‐Rivas C. W. Gear Antonios Zagaris

Interpretability

State variable

Identification

10.1016/j.jcp.2019.04.015

Cite

Citations (45)

n-Heptane/air combustion in perfectly stirred reactors: Dynamics, bifurcations and dominant reactions at critical conditions

Combustion and Flame (2015)

Mahdi Kooshkbaghi Christos E. Frouzakis Konstantinos Boulouchos Iliya V. Karlin

Entropy production

Heptane

10.1016/j.combustflame.2015.05.002

Cite

Citations (29)

Specificity, synergy, and mechanisms of splice-modifying drugs

Nature Communications (2024)

Yuma Ishigami Mandy S. Wong Carlos Martí‐Gómez Andalus Ayaz Mahdi Kooshkbaghi

Abstract Drugs that target pre-mRNA splicing hold great therapeutic potential, but the quantitative understanding of how these drugs work is limited. Here we introduce mechanistically interpretable quantitative models for the sequence-specific and concentration-dependent behavior of splice-modifying drugs. Using massively parallel splicing assays, RNA-seq experiments, and precision dose-response curves, we obtain quantitative models for two small-molecule drugs, risdiplam and branaplam, developed for treating spinal muscular atrophy. The results quantitatively characterize the specificities of risdiplam and branaplam for 5’ splice site sequences, suggest that branaplam recognizes 5’ splice sites via two distinct interaction modes, and contradict the prevailing two-site hypothesis for risdiplam activity at SMN2 exon 7. The results also show that anomalous single-drug cooperativity, as well as multi-drug synergy, are widespread among small-molecule drugs and antisense-oligonucleotide drugs that promote exon inclusion. Our quantitative models thus clarify the mechanisms of existing treatments and provide a basis for the rational development of new therapies.

splice

Cooperativity

Exon skipping

10.1038/s41467-024-46090-5

Cite

Citations (14)

The global relaxation redistribution method for reduction of combustion kinetics

The Journal of Chemical Physics (2014)

Mahdi Kooshkbaghi Christos E. Frouzakis Eliodoro Chiavazzo Konstantinos Boulouchos Iliya V. Karlin

An algorithm based on the Relaxation Redistribution Method (RRM) is proposed for constructing the Slow Invariant Manifold (SIM) of a chosen dimension to cover a large fraction of the admissible composition space that includes the equilibrium and initial states. The manifold boundaries are determined with the help of the Rate Controlled Constrained Equilibrium method, which also provides the initial guess for the SIM. The latter is iteratively refined until convergence and the converged manifold is tabulated. A criterion based on the departure from invariance is proposed to find the region over which the reduced description is valid. The global realization of the RRM algorithm is applied to constant pressure auto-ignition and adiabatic premixed laminar flames of hydrogen-air mixtures.

Manifold (fluid mechanics)

Redistribution

10.1063/1.4890368

Cite

Citations (13)

Emergent spaces for coupled oscillators

Thomas N. Thiem Mahdi Kooshkbaghi Tom Bertalan Carlo R. Laing Ioannis G. Kevrekidis

In this paper we present a systematic, data-driven approach to discovering bespoke coarse variables based on manifold learning algorithms. We illustrate this methodology with the classic Kuramoto phase oscillator model, and demonstrate how our manifold learning technique can successfully identify a coarse variable that is one-to-one with the established Kuramoto order parameter. We then introduce an extension of our coarse-graining methodology which enables us to learn evolution equations for the discovered coarse variables via an artificial neural network architecture templated on numerical time integrators (initial value solvers). This approach allows us to learn accurate approximations of time derivatives of state variables from sparse flow data, and hence discover useful approximate differential equation descriptions of their dynamic behavior. We demonstrate this capability by learning ODEs that agree with the known analytical expression for the Kuramoto order parameter dynamics at the continuum limit. We then show how this approach can also be used to learn the dynamics of coarse variables discovered through our manifold learning methodology. In both of these examples, we compare the results of our neural network based method to typical finite differences complemented with geometric harmonics. Finally, we present a series of computational examples illustrating how a variation of our manifold learning methodology can be used to discover sets of parameters, reduced parameter combinations, for multi-parameter models with complex coupling. We conclude with a discussion of possible extensions of this approach, including the possibility of obtaining data-driven effective partial differential equations for coarse-grained neuronal network behavior.

Ode

Kuramoto Model

Manifold (fluid mechanics)

Cite

Citations (0)

Relaxation Redistribution Method for model reduction

Mahdi Kooshkbaghi Christos E. Frouzakis Eliodoro Chiavazzo I. V. Karlin Konstantinos Boulouchos

The Relaxation Redistribution Method (RRM) is based on the notion of slow invariant manifold (SIM) and is applied for constructing a simplified model of detailed multiscale combustion phenomena. The RRM procedure can be regarded as an efficient and stable scheme for solving the film equation of dynamics, where a discrete set of points is gradually relaxed towards the slow invariant manifold (SIM). Here, the global realization of the RRM algorithm is briefly reviewed and used for auto-ignition and adiabatic premixed laminar flame of a homogeneous hydrogen-air ideal gas mixture.

Redistribution

Manifold (fluid mechanics)

Source

Cite

Citations (0)

A collocated grid, projection method for time-accurate calculation of low-Mach number variable density flows in general curvilinear coordinates

International Journal for Numerical Methods in Fluids (2012)

Mahdi Kooshkbaghi Bamdad Lessani

A time-accurate algorithm is proposed for low-Mach number, variable density flows on curvilinear grids. Spatial discretization is performed on collocated grid that offers computational simplicity in curvilinear coordinates. The flux interpolation technique is used to avoid the pressure odd–even decoupling of the collocated grid arrangement. To increase the stability of the method, a two-step predictor–corrector time integration scheme is employed. At each step, the projection method is used to calculate the hydrodynamic pressure and to satisfy the continuity equation. The robustness and accuracy of the method is illustrated with a series of numerical experiments including thermally driven cavity, polar cavity, three-dimensional cavity, and direct numerical simulation of non-isothermal turbulent channel flow. Copyright © 2012 John Wiley & Sons, Ltd.

Projection method

Decoupling (probability)

Robustness

Interpolation

10.1002/fld.3734

Cite

Citations (9)

On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability

PNAS Nexus (2022)

Nikolaos Evangelou Noah J. Wichrowski George A. Kevrekidis Felix Dietrich Mahdi Kooshkbaghi

We present a data-driven approach to characterizing nonidentifiability of a model's parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established analytically.

Identifiability

Interpretability

Kernel (algebra)

10.1093/pnasnexus/pgac154

Cite

Citations (9)