Quantitative Analysis of Genetic and Neuronal Multi-Perturbation Experiments

Perturbation studies, in which functional performance is measured after deletion, mutation, or lesion of elements of a biological system, have been traditionally employed in many fields in biology. The vast majority of these studies have been qualitative and have employed single perturbations, often resulting in little phenotypic effect. Recently, newly emerging experimental techniques have allowed researchers to carry out concomitant multi-perturbations and to uncover the causal functional contributions of system elements. This study presents a rigorous and quantitative multi-perturbation analysis of gene knockout and neuronal ablation experiments. In both cases, a quantification of the elements' contributions, and new insights and predictions, are provided. Multi-perturbation analysis has a potentially wide range of applications and is gradually becoming an essential tool in biology.

[1]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Gary D Bader,et al.  Global Mapping of the Yeast Genetic Interaction Network , 2004, Science.

[3]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[4]  Paul Glimcher,et al.  Decisions, Decisions, Decisions Choosing a Biological Science of Choice , 2002, Neuron.

[5]  John C. Harsanyi,et al.  A Simplified Bargaining Model for the n-Person Cooperative Game , 1963 .

[6]  J. Couzin.  Small RNAs Make Big Splash , 2002, Science.

[7]  Anne E Carpenter,et al.  RNAi living-cell microarrays for loss-of-function screens in Drosophila melanogaster cells , 2004, Nature Methods.

[8]  Isaac Meilijson,et al.  Fair Attribution of Functional Contribution in Artificial and Biological Networks , 2004, Neural Computation.

[9]  Ronald W. Davis,et al.  Functional profiling of the Saccharomyces cerevisiae genome , 2002, Nature.

[10]  Karl J. Friston,et al.  PHRENOLOGY : What Can Neuroimaging Tell Us About Distributed Circuitry ? , 2005 .

[11]  Alvaro Pascual-Leone,et al.  Handbook of transcranial magnetic stimulation , 2002 .

[12]  Robert H. Haynes,et al.  Interactions among genes controlling sensitivity to radiation and alkylation in yeast , 1973, Molecular and General Genetics MGG.

[13]  V. Thorsson,et al.  Discovery of regulatory interactions through perturbation: inference and experimental design. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[14]  Martin Kupiec,et al.  ELG1, a yeast gene required for genome stability, forms a complex related to replication factor C , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Ben Lehner,et al.  5,000 RNAi experiments on a chip , 2004, Nature Methods.

[16]  S M Kosslyn,et al.  If neuroimaging is the answer, what is the question? , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[17]  Philip Wolfe,et al.  Contributions to the theory of games , 1953 .

[18]  L. S. Shapley,et al.  17. A Value for n-Person Games , 1953 .

[19]  Wei Xiao,et al.  Regulation of alternative replication bypass pathways at stalled replication forks and its effects on genome stability: a yeast model. , 2003, Mutation research.

[20]  Nir Friedman,et al.  Inferring Cellular Networks Using Probabilistic Graphical Models , 2004, Science.

[21]  Cori Bargmann,et al.  Chemotaxis and Thermotaxis , 1997 .

[22]  Cori Bargmann,et al.  C. elegans Responds to Chemical Repellents by Integrating Sensory Inputs from the Head and the Tail , 2002, Current Biology.

[23]  P. Grobstein.  Strategies for analyzing complex organization in the nervous system: I.: lesion experiments , 1993 .

[24]  Cori Bargmann,et al.  Chemosensory neurons with overlapping functions direct chemotaxis to multiple chemicals in C. elegans , 1991, Neuron.

[25]  J. Friedman,et al.  Projection Pursuit Regression , 1981 .

[26]  Isaac Meilijson,et al.  Axiomatic Scalable Neurocontroller Analysis via the Shapley Value , 2006, Artificial Life.

[27]  Nir Friedman,et al.  Inferring subnetworks from perturbed expression profiles , 2001, ISMB.

[28]  Ronald W. Davis,et al.  Role of duplicate genes in genetic robustness against null mutations , 2003, Nature.

[29]  Ronald W. Davis,et al.  Maximizing the potential of functional genomics , 2004, Nature Reviews Genetics.

[30]  Michel Grabisch,et al.  Equivalent Representations of Set Functions , 2000, Math. Oper. Res..

[31]  Ronald W. Davis,et al.  Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. , 1999, Science.

[32]  Amy A. Caudy,et al.  Post-transcriptional gene silencing by double-stranded RNA , 2001, Nature Reviews Genetics.

[33]  S. Braeutigam.  Neuroeconomics—From neural systems to economic behaviour , 2005, Brain Research Bulletin.

[34]  R. Rafal,et al.  Virtual neurology , 2001, Nature Neuroscience.

[35]  Martin A. Nowak,et al.  Inferring Cellular Networks Using Probabilistic Graphical Models , 2004 .

[36]  Isaac Meilijson,et al.  Localization of Function via Lesion Analysis , 2003, Neural Computation.

[37]  Anne E Carpenter,et al.  Systematic genome-wide screens of gene function , 2004, Nature Reviews Genetics.

[38]  Robert E. Johnson,et al.  Efficient bypass of a thymine-thymine dimer by yeast DNA polymerase, Poleta. , 1999, Science.

[39]  Michel Grabisch,et al.  An axiomatic approach to the concept of interaction among players in cooperative games , 1999, Int. J. Game Theory.

[40]  Michel Grabisch,et al.  Equivalent Representations of a Set Function with Applications to Game Theory and Multicriteria Decision Making , 1998 .

[41]  J. Hasty,et al.  Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling , 2003, Proceedings of the National Academy of Sciences of the United States of America.