Global Sensitivity Analysis Challenges in Biological Systems Modeling

Kiparissides, Alexandros; Kucherenko, Sergei; Mantalaris, A.; Pistikopoulos, Efstratios N.

doi:10.1021/ie900139x

Cited by 135 publications

(89 citation statements)

References 47 publications

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“…Such methods have been used extensively in other engineering fields, and they can be easily extended for application in biological systems [43]. Within the field of uncertainty and sensitivity analysis, methods exist for the analysis of uncertainty propagation.…”

Section: Discussionmentioning

confidence: 99%

“…Several methods and approaches exist within these fields that allow uncertainty modeling and quantification. Some of these methods have been used in the analysis of metabolic and signaling networks and, as a result, have provided some insight into the properties of the networks as well as guidance for metabolic engineering [40][41][42][43][44][45][46][47]. The challenges in the development of uncertainty analysis are in the modeling and simulation of uncertainty.…”

Section: Uncertainty In Biological Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Production of biofuels and biochemicals: in need of an ORACLE

Mišković

Hatzimanikatis

2010

Trends in Biotechnology

103

110

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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 achievement of these objectives under physiological and process constraints will be impossible without the use of mathematical modeling. However, the limited information and the uncertainty in the available information require new methods for modeling and simulation that will characterize the uncertainty and will quantify, in a statistical sense, the expectations of success of alternative metabolic engineering strategies. We discuss these considerations toward developing a framework for the Optimization and Risk Analysis of Complex Living Entities (ORACLE) -a computational method that integrates available information into a mathematical structure to calculate control coefficients. Biofuels and biochemicals: a multi-objective optimization problem Nearly 20 years ago, Tong and Cameron classified the applications of metabolic engineering into five main areas: (i) improved production and utilization of chemicals already produced/used by the host; (ii) extended substrate range for growth and production; (iii) addition of new catabolic activities for the degradation of toxic chemicals; (iv) production of chemicals new to the host; and (v) modification of the cell [1]. In the development of microorganisms for fuels and chemicals, one must consider and optimize almost all of these objectives simultaneously. For example, the economics of the fuels and commodity chemicals will require a host that is able to overproduce a fuel or chemical (native or new to the organism) from a broad range of carbon substrates, some of them not used previously by this organism, with high specific rates and with near-theoretical yields [2]. All of these issues become more challenging when we consider the use of new hosts for engineering [3,4], where there is not enough genomic information, the genetic tools for metabolic engineering are limited, and extensive knowledge about their physiology is lacking.The development of microorganisms for fuels and chemicals will also require the fine-tuning of carbon flows and redox balance. All of the biofuels currently considered require delicate manipulation of the carbon flows in the central metabolism, and any intervention in these pathways can have significant implications for the cellular physiology. Redox balance, energy charge and cofactor levels are also important because they are involved in many of the pathways for the production of biofuels and acids. For example, these pathways involve reactions that produce and consume redox potential, and their directionality is determined by the NAD + /NADH ratio. Metabolic engineering of these pathways could alter the balance of redox and coenzyme cofactors, thereby reducing the productivity and the yield, leading to the production of byproducts that could increase the cost of downstream processing [2,[5][6][...

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Section: Discussionmentioning

confidence: 99%

Section: Uncertainty In Biological Systemsmentioning

confidence: 99%

Production of biofuels and biochemicals: in need of an ORACLE

Mišković

Hatzimanikatis

2010

Trends in Biotechnology

103

110

View full text Add to dashboard Cite

show abstract

“…In particular, the scope is to (i) develop a 'high fidelity' model of the process [173,189], (ii) analyze the original problem e.g. using global sensitivity analysis [176,179,311] and (iii) perform parameter estimation and dynamic optimization of the developed model. Within our framework, the modeling software PSE's gPROMS R • ModelBuider [282] is used, as it provides the aforementioned tools either directly or allows their implementation via gO:MATLAB, a connection tool between MATLAB R…”

Section: 'High Fidelity' Modeling and Analysismentioning

confidence: 99%

Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization

Diangelakis

Pistikopoulos

2017

Computer Aided Chemical Engineering

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“…We apply random sampling -high dimensional model representation (RS-HDMR; Appendix F) global sensitivity analysis [35,36] to the nominal parameter values. The minimum and maximum values of parameters included in Table 4 are used as inputs to the RS-HDMR model analysis ( 3.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

A Personalized Framework for Dynamic Modeling of Disease Trajectories in Chronic Lymphocytic Leukemia

Savvopoulos

Misener

Panoskaltsis

et al. 2016

IEEE Trans. Biomed. Eng.

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Abstract-Chronic Lymphocytic Leukemia (CLL) is the most common peripheral blood and bone marrow cancer in the developed world. This manuscript proposes mathematical model equations representing the disease dynamics of B-cell CLL. We interconnect delay differential cell cycle models in each of the tumor-involved disease centers using physiologically-relevant cell migration. We further introduce 5 hypothetical case studies representing CLL heterogeneity commonly seen in clinical practice and demonstrate how the proposed CLL model framework may capture disease pathophysiology across patient types. We conclude by exploring the capacity of the proposed temporally-and spatially-distributed model to capture the heterogeneity of CLL disease progression. By using Global Sensitivity Analysis, the critical parameters influencing disease trajectory over space and time are: (i) the initial number of CLL cells in peripheral blood, the number of involved lymph nodes, the presence and degree of splenomegaly; (ii) the migratory fraction of nonproliferating as well as proliferating CLL cells from bone marrow into blood and of proliferating CLL cells from blood into lymph nodes; (iii) the parameters inducing nonproliferative cells to proliferate. The proposed model offers a practical platform which may be explored in future personalized patient protocols once validated.

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Global Sensitivity Analysis Challenges in Biological Systems Modeling

Cited by 135 publications

References 47 publications

Production of biofuels and biochemicals: in need of an ORACLE

Production of biofuels and biochemicals: in need of an ORACLE

Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization

A Personalized Framework for Dynamic Modeling of Disease Trajectories in Chronic Lymphocytic Leukemia

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