Publications de Ljubisa Miskovic

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In order to identify targets for metabolic engineering interventions or understand the physiology of the cell, the first and foremost prerequisite is the knowledge or a hypothesis about the intracellular network state(s). However, even with the advanced metabolomics and fluxomics methods currently at our disposal, it is still impossible or rather difficult to determine the exact intracellular flux states due to the number of reactions in the real system compounded with the reversibility of many of these rea

Precise information about kinetic properties of enzymes within a biochemical network is a necessary ingredient of any successful metabolic engineering design. Such information inevitably involves uncertainty due to changes in cellular and process parameters. In this contribution, we present a computational methodology that explores the kinetic data space, and accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints. Thus, the proposed framework allows us t

Systematic analysis for the redirection of carbon flux in metabolite-producing microorganisms requires the comprehensive understanding of their complex metabolic processes. The use of large-scale dynamic models of metabolism plays key role in the understanding of these processes and the study of possible metabolic engineering interventions. However, the generation of such models is hampered by the intrinsic nonlinearities of enzymatic reactions, and the uncertainties at different levels. In particular there

A precise quantification of the effect of perturbations in a metabolic network depends on explicit knowledge of the kinetic properties of the enzymes of the individual reactions. However, a comprehensive knowledge of enzymes kinetics in a metabolic network is very difficult, if not impossible, to obtain. Furthermore, experimental data obtained under different conditions introduce uncertainty in enzyme kinetic parameters. In this work, we model the uncertainty in the kinetic data and we predict quantitativel

Abundant in nature lignocellulosic plant biomass represent a promising, sustainable, source of energy as it can be converted into fuel ethanol through the fermentation of one of its prevalent sugar constituents – xylose. Xylose-utilizing recombinant Saccharomyces cerevisiae strains are engineered to overcome the potential bottlenecks of xylose uptake and cofactor imbalance, and to minimize the xylitol accumulation. In this work, we apply a novel computational framework to identify metabolic engineering targ

Kinetic models are the most promising tool for comprehending the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and we need large-scale models for dependable in silico analyses of metabolism. However, the uncertainty in kinetic parameters is the main obstacle hindering the development of kinetic models, and the levels of uncertainty increase with the size of the models. Therefore, there is a need for tools that

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biol ...

Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual react ...

The engineering of cells for the production of fuels and chemicals involves simultaneous optimization of multiple objectives, such as specific productivity, extended substrate range and improved tolerance – all under a great degree of uncertainty. The achi ...

Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the la ...

The constraints imposed during modeling must satisfy biologically representative phenotypes of the studied organism. Simultaneously, identification and analysis of these constraints enhances our understanding of the evolution/operational paradigms of the organism. It was postulated by Varma and Palsson[1] that it is possible to define limits on metabolic behavior using flux balance analysis, but in order to accurately capture the metabolic responses, detailed information about enzyme kinetics and their regu

A correlation-based controller tuning method is proposed for the ``Design and optimization of restricted-complexity controllers'' benchmark problem. The approach originally proposed for model following is extended to solve the disturbance rejection problem. The idea is to tune the controller parameters such that the closed-loop output be uncorrelated with the disturbance signal. Since perfect decorrelation between the closed-loop output and the disturbance signal is not attainable in the restricted-complexi

Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic m

This paper gives an overview on the theoretical results of recently developed algorithms for iterative controller tuning based on the correlation approach. The basic idea is to decorrelate the output error between the achieved and designed closed-loop systems by iteratively tuning the controller parameters. Two different approaches are investigated. In the first one, a correlation equation involving a vector of instrumental variables is solved using the stochastic approximation method. It is shown that, wit

Modeling and analysis of metabolic networks is inherently biased by assumptions regarding the intracellular metabolite and flux states. Despite the advanced metabolomics and fluxomics methods at our disposal, the sheer complexity of metabolic networks prevents us from determining their exact operational state. To address this shortcoming, we have developed a novel framework called Flux Directionality Profile Analysis (FDPA) for characterizing systematically all possible intracellular flux and thermodynamic

Dynamic (kinetic) models track time-varying metabolite concentrations, fluxes, and enzyme levels, quantifying responses to genetic and environmental perturbations. Yet building these models at scale is hindered by scarce enzyme kinetic parameters. Generative neural networks can rapidly parameterize near-genome-scale kinetic models, but their representations are hard to interpret and often require new training to move across species or physiological states. Here we introduce a latent-space exploration framew

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these da ...

Metabolic modeling has proven to be a valuable tool in the investigation of cellular physiology, especially in the post-genomic era. However, the sheer complexity of even the simplest metabolic networks obstructs the exact determination of intracellular states, despite the ample supply of –omics data. To overcome this difficulty we attempt to postulate simplifying assumptions, relevant to the conditions and available data. Identifying the gamut of possible feasible states allows us to query for factors that

In this study, we used the ORACLE (Optimization and Risk Analysis of Complex Living Entities)[1] framework to study the impact of extracellular acetic acid on the S. cerevisiae metabolism with the aim to improve ethanol production in the presence of this inhibitor found in significant concentrations in lignocellulosic hydrolysates. First, we derived a consistently reduced core model (279 metabolites and 382 reactions) of S. cerevisiae from the iMM904 genome scale reconstruction. We integrated thermodynamic

Ljubisa Miskovic

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Systematic characterization of flux and network thermodynamic states for identification of metabolic engineering targets

Kinetic modeling of the central carbon metabolism of Escherichia Coli

Analysis and Design of Metabolic Engineering and Synthetic Biology Strategies using Large Scale Dynamic Models of Metabolism

Enzyme States Allow Identification of Rate-Limiting Steps

Improving Ethanol Production of Xylose-Utilizing Saccharomyces cerevisiae

Analysis and Design of Metabolic Engineering and Synthetic Biology Strategies using Large Scale Dynamic Models of Metabolism

Unlocking the full potential of kinetic metabolic models through characterization and reduction of uncertainty

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties

Modeling of uncertainties in biochemical reactions

Production of biofuels and biochemicals: in need of an ORACLE

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Identification of Feasible Metabolic Fluxes and Metabolite Concentrations using Large-scale Kinetic Models

Correlation-Based Tuning of a Restricted-Complexity Controller for an Active Suspension System

From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks

Iterative Correlation-Based Controller Tuning

Implications of the Assumptions on Intracellular Metabolic Operational States in Metabolic Control Analysis

Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" -Part 2

Unlocking Yeast Metabolism: Identification of optimal flux directionalities

Unraveling the inhibitory effects of acetate on ethanol production in CEN.PK