Closed-Loop Learning Control of Bio-Networks

Ku, Jason T.; Xiao, Feng; Rabitz, Herschel

doi:10.1089/cmb.2004.11.642

Cited by 3 publications

(4 citation statements)

References 37 publications

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“…At a more practical level, high throughput experiments with heterogeneous cell lines could be designed in such a way that the selectivity optimization process is part of a closed-loop control system [33]. Single drug measurements could be used to obtain directly the sensitivity in a given heterogeneous cell population.…”

Section: Discussionmentioning

confidence: 99%

“…We have studied the statistics of nodes entering in selective combinations. In the linear case, single-node selectivity is obtained by acting on BAD (30), IkBa (33), NFkB (34), BAX (40), CASP3 (42), CASP7 (43) or TP53 (44). These nodes can be divided into those that are proand anti-apoptosis, based on their average effect on the output signal.…”

Section: Exhaustive Search In Discrete Modelsmentioning

confidence: 99%

“…Single drug selective apoptosis is generally induced by stimulating a pro-apoptosis node, or by inhibiting an anti-apoptosis node. For instance, nodes IkBa (33) and NFkB (34) have on average an anti-apoptosis behavior, so they are mainly associated with negative δm. The dotted line in Fig.…”

Section: Exhaustive Search In Discrete Modelsmentioning

confidence: 99%

“…The sigmoid rule slightly enhances the number of combinations in which the inhibition of antiapoptosis genes represents the mechanism for selectivity, see e.g. CHUCK (32), IkBa (33), NFkB (34), BIRC2 (35) and BCL2 (37). Both linear and sigmoid rules suggest that selectivity might be better achieved by a direct up-regulation of caspases, or by acting on pro-or antiapoptosis proteins of the BCL2 family.…”

Section: Exhaustive Search In Discrete Modelsmentioning

confidence: 99%

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Selective Control of the Apoptosis Signaling Network in Heterogeneous Cell Populations

et al. 2007

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BackgroundSelective control in a population is the ability to control a member of the population while leaving the other members relatively unaffected. The concept of selective control is developed using cell death or apoptosis in heterogeneous cell populations as an example. Control of apoptosis is essential in a variety of therapeutic environments, including cancer where cancer cell death is a desired outcome and Alzheimer's disease where neuron survival is the desired outcome. However, in both cases these responses must occur with minimal response in other cells exposed to treatment; that is, the response must be selective.Methodology and Principal FindingsApoptosis signaling in heterogeneous cells is described by an ensemble of gene networks with identical topology but different link strengths. Selective control depends on the statistics of signaling in the ensemble of networks, and we analyze the effects of superposition, non-linearity and feedback on these statistics. Parallel pathways promote normal statistics while series pathways promote skew distributions, which in the most extreme cases become log-normal. We also show that feedback and non-linearity can produce bimodal signaling statistics, as can discreteness and non-linearity. Two methods for optimizing selective control are presented. The first is an exhaustive search method and the second is a linear programming based approach. Though control of a single gene in the signaling network yields little selectivity, control of a few genes typically yields higher levels of selectivity. The statistics of gene combinations susceptible to selective control in heterogeneous apoptosis networks is studied and is used to identify general control strategies.Conclusions and SignificanceWe have explored two methods for the study of selectivity in cell populations. The first is an exhaustive search method limited to three node perturbations. The second is an effective linear model, based on interpolation of single node sensitivity, in which the selective combinations can be found by linear programming optimization. We found that selectivity is promoted by acting on the least sensitive nodes in the case of weak populations, while selective control of robust populations is optimized through perturbations of more sensitive nodes. High throughput experiments with heterogeneous cell lines could be designed in an analogous manner, with the further possibility of incorporating the selectivity optimization process into a closed-loop control system.

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Section: Discussionmentioning

confidence: 99%

Section: Exhaustive Search In Discrete Modelsmentioning

confidence: 99%

Section: Exhaustive Search In Discrete Modelsmentioning

confidence: 99%

Section: Exhaustive Search In Discrete Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Selective Control of the Apoptosis Signaling Network in Heterogeneous Cell Populations

et al. 2007

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Optimal Identification of Biochemical Reaction Networks

Xiao

Rabitz

2004

Biophysical Journal

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Advances in biotechnology and computer science are providing the possibility to construct mathematical models for complex biological networks and systematically understand their properties. Traditional network identification approaches, however, cannot accurately recover the model parameters from the noisy laboratory measurements. This article introduces the concept of optimal identification (OI), which utilizes a global inversion algorithm to extract the full distribution of parameters consistent with the laboratory data. In addition, OI integrates suitable computational algorithms with experimental capabilities in a closed loop fashion to maximally reduce the breadth of the extracted parameter distribution. The closed loop OI procedure seeks out the optimal set of control chemical fluxes and data observations that actively filter out experimental noise and enhance the sensitivity to the desired parameters. In this fashion, the highest quality network parameters can be attained from inverting the tailored laboratory data. The operation of OI is illustrated by identifying a simulated tRNA proofreading mechanism, in which OI provides superior solutions for all the rate constants compared with suboptimal and nonoptimal methods.

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Optimizing Genetic Circuits by Global Sensitivity Analysis

Xiao

Hooshangi

Chen

et al. 2004

Biophysical Journal

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125

106

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Artificial genetic circuits are becoming important tools for controlling cellular behavior and studying molecular biosystems. To genetically optimize the properties of complex circuits in a practically feasible fashion, it is necessary to identify the best genes and/or their regulatory components as mutation targets to avoid the mutation experiments being wasted on ineffective regions, but this goal is generally not achievable by current methods. The Random Sampling-High Dimensional Model Representation (RS-HDMR) algorithm is employed in this work as a global sensitivity analysis technique to estimate the sensitivities of the circuit properties with respect to the circuit model parameters, such as rate constants, without knowing the precise parameter values. The sensitivity information can then guide the selection of the optimal mutation targets and thereby reduce the laboratory effort. As a proof of principle, the in vivo effects of 16 pairwise mutations on the properties of a genetic inverter were compared against the RS-HDMR predictions, and the algorithm not only showed good consistency with laboratory results but also revealed useful information, such as different optimal mutation targets for optimizing different circuit properties, not available from previous experiments and modeling.

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Closed-Loop Learning Control of Bio-Networks

Cited by 3 publications

References 37 publications

Selective Control of the Apoptosis Signaling Network in Heterogeneous Cell Populations

Selective Control of the Apoptosis Signaling Network in Heterogeneous Cell Populations

Optimal Identification of Biochemical Reaction Networks

Optimizing Genetic Circuits by Global Sensitivity Analysis

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