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Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm.

ABSTRACT: A mixture of drugs is often more effective than using a single effector. However, it is extremely challenging to identify potent drug combinations by trial and error because of the large number of possible combinations and the inherent complexity of the underlying biological network. With a closed-loop optimization modality, we experimentally demonstrate effective searching for potent drug combinations for controlling cellular functions through a large parametric space. Only tens of iterations out of one hundred thousand possible trials were needed to determine a potent combination of drugs for inhibiting vesicular stomatitis virus infection of NIH 3T3 fibroblasts. In addition, the drug combination reduced the required dosage by approximately 10-fold compared with individual drugs. In another example, a potent mixture was identified in thirty iterations out of a possible million combinations of six cytokines that regulate the activity of nuclear factor kappa B in 293T cells. The closed-loop optimization approach possesses the potential of being an effective approach for manipulating a wide class of biological systems.

SUBMITTER: Wong PK

PROVIDER: S-EPMC2278193 | biostudies-literature | 2008 Apr

REPOSITORIES: biostudies-literature

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Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm.

Wong Pak Kin PK Yu Fuqu F Shahangian Arash A Cheng Genhong G Sun Ren R Ho Chih-Ming CM

Proceedings of the National Academy of Sciences of the United States of America 20080320 13

A mixture of drugs is often more effective than using a single effector. However, it is extremely challenging to identify potent drug combinations by trial and error because of the large number of possible combinations and the inherent complexity of the underlying biological network. With a closed-loop optimization modality, we experimentally demonstrate effective searching for potent drug combinations for controlling cellular functions through a large parametric space. Only tens of iterations o ...[more]

PMID: 18356295

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Project description:OBJECTIVES:We sought to evaluate the efficacy, efficiency, and physiologic consequences of automated, endpoint-directed resuscitation systems and compare them to formula-based bolus resuscitation. DESIGN:Experimental human hemorrhage and resuscitation. SETTING:Clinical research laboratory. SUBJECTS:Healthy volunteers. INTERVENTIONS:Subjects (n = 7) were subjected to hemorrhage and underwent a randomized fluid resuscitation scheme on separate visits 1) formula-based bolus resuscitation; 2) semiautonomous (decision assist) fluid administration; and 3) fully autonomous (closed loop) resuscitation. Hemodynamic variables, volume shifts, fluid balance, and cardiac function were monitored during hemorrhage and resuscitation. Treatment modalities were compared based on resuscitation efficacy and efficiency. MEASUREMENTS AND MAIN RESULTS:All approaches achieved target blood pressure by 60 minutes. Following hemorrhage, the total amount of infused fluid (bolus resuscitation: 30?mL/kg, decision assist: 5.6?±?3?mL/kg, closed loop: 4.2?±?2?mL/kg; p < 0.001), plasma volume, extravascular volume (bolus resuscitation: 17?±?4?mL/kg, decision assist: 3?±?1?mL/kg, closed loop: -0.3?±?0.3?mL/kg; p < 0.001), body weight, and urinary output remained stable under decision assist and closed loop and were significantly increased under bolus resuscitation. Mean arterial pressure initially decreased further under bolus resuscitation (-10?mm Hg; p < 0.001) and was lower under bolus resuscitation than closed loop at 20 minutes (bolus resuscitation: 57?±?2?mm Hg, closed loop: 69?±?4?mm Hg; p = 0.036). Colloid osmotic pressure (bolus resuscitation: 19.3?±?2?mm Hg, decision assist, closed loop: 24?±?0.4?mm Hg; p < 0.05) and hemoglobin concentration were significantly decreased after bolus fluid administration. CONCLUSIONS:We define efficacy of decision-assist and closed-loop resuscitation in human hemorrhage. In comparison with formula-based bolus resuscitation, both semiautonomous and autonomous approaches were more efficient in goal-directed resuscitation of hemorrhage. They provide favorable conditions for the avoidance of over-resuscitation and its adverse clinical sequelae. Decision-assist and closed-loop resuscitation algorithms are promising technological solutions for constrained environments and areas of limited resources.

Project description:Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n ?= ?3,175), when compared with the largest published meta-GWAS (n > 100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.

Dataset Information

Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm.

Publications

Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm.

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