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From genes to behavior: placing cognitive models in the context of biological pathways.

ABSTRACT: Connecting neural mechanisms of behavior to their underlying molecular and genetic substrates has important scientific and clinical implications. However, despite rapid growth in our knowledge of the functions and computational properties of neural circuitry underlying behavior in a number of important domains, there has been much less progress in extending this understanding to their molecular and genetic substrates, even in an age marked by exploding availability of genomic data. Here we describe recent advances in analytical strategies that aim to overcome two important challenges associated with studying the complex relationship between genes and behavior: (i) reducing distal behavioral phenotypes to a set of molecular, physiological, and neural processes that render them closer to the actions of genetic forces, and (ii) striking a balance between the competing demands of discovery and interpretability when dealing with genomic data containing up to millions of markers. Our proposed approach involves linking, on one hand, models of neural computations and circuits hypothesized to underlie behavior, and on the other hand, the set of the genes carrying out biochemical processes related to the functioning of these neural systems. In particular, we focus on the specific example of value-based decision-making, and discuss how such a combination allows researchers to leverage existing biological knowledge at both neural and genetic levels to advance our understanding of the neurogenetic mechanisms underlying behavior.

SUBMITTER: Saez I

PROVIDER: S-EPMC4220121 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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From genes to behavior: placing cognitive models in the context of biological pathways.

Saez Ignacio I Set Eric E Hsu Ming M

Frontiers in neuroscience 20141104

Connecting neural mechanisms of behavior to their underlying molecular and genetic substrates has important scientific and clinical implications. However, despite rapid growth in our knowledge of the functions and computational properties of neural circuitry underlying behavior in a number of important domains, there has been much less progress in extending this understanding to their molecular and genetic substrates, even in an age marked by exploding availability of genomic data. Here we descr ...[more]

PMID: 25414628

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Project description:Purpose: The goals of this study were to establish in vivo models that recapitulated the heterogeneous behavior of osteosarcoma and to develop an approach to distinguish genes expressed from tumor cells apart from those expressed from host cells in next generation sequencing (NGS) data from xenograft tumors. Methods: We used cell lines derived from two spontaneous canine osteosarcoma tumors with distinctly different biological behaviors for heterotypic in vivo modeling. NGS was used to compare the transcriptome profiles of the xenograft tumors to the transcriptome profiles of the parental cell lines and to study the tumor microenvironment of the xenograft tumors. Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to a HISAT2 index for mapping which was built from a multi-sequence fasta file containing both the canine (canFam3) and murine (mm10) genomes with the HISAT2 aligner (2.0.2-beta) using default parameters. Rsubread featureCounts (1.4.6) was used to estimate transcript abundance using default parameters. The Bioconductor package ‘edgeR’ (version 3.12.1) was used for differential gene expression analysis and Partek Genomic Suite (version 6.6) was used for clustering and visualization. Qiagen's Ingenuity Pathway Analysis application (version 01-04) and Thomson Reuter's MetaCore software suite were used for pathway and upstream regulator analysis with differentially expressed genes. Results: We document that orthotopic canine osteosarcoma xenografts preserve the biological, molecular, and heterotypic biology observed in the tumors from which they were derived. The data also provide insight into the tumor-host interactions and identify targets that could play a role in treatment strategies. Conclusions: Our study represents the first detailed analysis of xenograft transcriptomes with an approach that allows for accurate examination of tumor-intrinsic properties, as well as organotypic tumor-stromal interaction that may influence tumor progression.

Dataset Information

From genes to behavior: placing cognitive models in the context of biological pathways.

Publications

From genes to behavior: placing cognitive models in the context of biological pathways.

Similar Datasets

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