title: Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?
abstract: There is a growing appreciation for the network biology that regulates the coordinated expression of molecular and cellular markers however questions persist regarding the identifiability of these networks. Here we explore some of the issues relevant to recovering directed regulatory networks from time course data collected under experimental constraints typical of in vivo studies. NetSim simulations of sparsely connected biological networks were used to evaluate two simple feature selection techniques used in the construction of linear Ordinary Differential Equation (ODE) models, namely truncation of terms versus latent vector projection. Performance was compared with ODE-based Time Series Network Identification (TSNI) integral, and the information-theoretic Time-Delay ARACNE (TD-ARACNE). Projection-based techniques and TSNI integral outperformed truncation-based selection and TD-ARACNE on aggregate networks with edge densities of 10-30%, i.e. transcription factor, protein-protein cliques and immune signaling networks. All were more robust to noise than truncation-based feature selection. Performance was comparable on the in silico 10-node DREAM 3 network, a 5-node Yeast synthetic network designed for In vivo Reverse-engineering and Modeling Assessment (IRMA) and a 9-node human HeLa cell cycle network of similar size and edge density. Performance was more sensitive to the number of time courses than to sample frequency and extrapolated better to larger networks by grouping experiments. In all cases performance declined rapidly in larger networks with lower edge density. Limited recovery and high false positive rates obtained overall bring into question our ability to generate informative time course data rather than the design of any particular reverse engineering algorithm.
authors.affiliation: Department of Medicine, University of Alberta, Edmonton, AB, Canada.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Vashishtha
authors.affiliation: Department of Medicine, University of Alberta, Edmonton, AB, Canada; Institute for Neuro-Immune Medicine, Nova Southeastern University, Fort Lauderdale, Florida, United States of America; Center for Psychological Studies, Nova Southeastern University, Fort Lauderdale, Florida, United States of America.
authors.forename: Gordon
authors.initials: G
authors.lastname: Broderick
authors.affiliation: Institute for Neuro-Immune Medicine, Nova Southeastern University, Fort Lauderdale, Florida, United States of America; Center for Psychological Studies, Nova Southeastern University, Fort Lauderdale, Florida, United States of America.
authors.forename: Travis J A
authors.initials: TJ
authors.lastname: Craddock
authors.affiliation: Institute for Neuro-Immune Medicine, Nova Southeastern University, Fort Lauderdale, Florida, United States of America; Miami Veterans Affairs Medical Center, Miami, Florida, United States of America.
authors.forename: Mary Ann
authors.initials: MA
authors.lastname: Fletcher
authors.affiliation: Institute for Neuro-Immune Medicine, Nova Southeastern University, Fort Lauderdale, Florida, United States of America; Miami Veterans Affairs Medical Center, Miami, Florida, United States of America.
authors.forename: Nancy G
authors.initials: NG
authors.lastname: Klimas
mesh_terms: D000465:Algorithms; D002453:Cell Cycle; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006367:HeLa Cells; D006801:Humans; D012441:Saccharomyces cerevisiae / Q000235:genetics; D018401:Sample Size; D013997:Time Factors