Professor James Bentley Brown PhD

Professor James Bentley Brown

School of Biosciences
Chair of Environmental Bioinformatics

Contact details

Centre for Computational Biology (CCB)
University of Birmingham
B15 2TT

Ben Brown’s lab works to link genome dynamics to ecosystem functions – to understand how the information encoded in genomes gives rise to complex, and profoundly responsive systems. Enormous progress has been made linking genes to cellular, tissue, organ, and organismal systems. The grand challenge before us is to understand the impact of genes beyond the organism; to understand gene function in ecological contexts. This emerging field is known as “molecular ecosystems biology”, and many advances are needed to enable its emergence as a foundational science. The Brown lab works to develop analytical capabilities on the following fronts:

Deep model organisms: species where we work systematically toward a comprehensive understanding of gene regulation and molecular bionomics, including Drosophila, Daphnia, zebrafish, mouse, human-derived cells, and several microbes.

Model ecologies: self-sustaining and recapturable systems with defined trajectories can be used to interrogate complex communities with the same felicity we enjoy in the development of model organisms. Examples include the Drosophila gut microbiome (given genotype and culture conditions), and the EcoFAB initiative (

Molecular exposure biology: perturbations of metabolic pathways even when those pathways are spread across consortia involving multiple species or populations.

Phylogenomic reconstructions: provide translations of results collected in model systems to real-world ecologies.

Nondestructive measurements: time-courses enable the study of system dynamics, e.g. hyperspectral imaging and minimally invasive sequencing.

Informative Learning Machines: introspective algorithms used to obtain insight from multi-modal datasets. The Brown Lab is developing this new area of machine learning to integrate pan-omics datasets.


PhD - Applied Science & Technology, University of California, Berkeley, 2008.

BA - Mathematics, University of California, Santa Cruz, 2000.


Ben Brown completed a BA in Mathematics at UC Santa Cruz, followed by a PhD in Applied Science and Technology at UC Berkeley in 2009. He has worked with the ENCODE Consortium since the Pilot Project (2005), and the modENCODE Consortium throughout its duration (2009-2014).

In ENCODE, Dr Brown founded and was co-lead on the RNA-Proteomics Integration Project, where his group used whole-proteome shotgun Mass Spectrometry (>1M spectra) to assess the translational activity of annotated long non-coding RNAs in ENCODE cell lines. His group won a highly competitive NHGIR Career development award (K99/R00), largely in association with this work. In modENCODE, he led integrative analysis for the Fly Transcription Consortium, an international team including members at nine institutions.

He currently leads analysis for two large-scale projects: the Health component of the Microbes to Biomes Initiative (, studying host-microbiome interactions in adaption to environmental challenges, and the Consortium for Environmental Omics and Toxicology (CEOT), leveraging techniques from exposure biology to annotate and functionally describe gene regulatory and metabolic networks in metazoans, including human.

In the ENCODE3 Consortia, Dr. Brown is the founder and co-chair of the RNA-Chromatin Interaction Project, studying the mechanisms of chromatin-mediated co-transcriptional RNA processing, as well as chromatin-induced post-transcriptional modifications. In 2013, he joined the LBNL Life Sciences Division and to build a program focused on the development of tools for the integrative analysis of large, multi-scale biological datasets.

In 2015, he was promoted to Department Head at LBNL to establish a new program in molecular ecosystems biology, which he continues to lead. In 2016, he joined the faculty at the University of Birmingham as the inaugural Chair of Environmental Bioinformatics in the Centre for Computational Biology to build a program in computational environmental bioscience.


Statistical Machine Learning

Next generation sequencing, high-throughput structural biology, and high-throughput genome engineering have opened a host of new, foundational opportunities and challenges in the analysis of organismal, bionomic, and environmental systems. We develop new techniques, approaches, and algorithms in statistical machine learning to solve otherwise intractable problems in bioinformatics. Our principle focus is the mapping of interactions between features defined on genomes with respect to organismal or ecological traits. We work on integrating high-dimensional molecular datasets, such as modENCODE, with high-content phenomics to simultaneously identify relationships between genomic information and complex phenotypes. We are leveraging phylogenomic toxicology (exposure biology in a multi-species setting) to identify gene and metabolic networks with “orthologous functions” in distantly related metazoans. This work has important implications for the future of chemical safety assessment and drug design. We are developing new tools for computational biology and statistical machine learning, including new forms of tensor regression for multi-species analysis, quantum-computing enabled pathway discovery, and “Introspective Learning Machines” (ILMs) that leverage iterative data exploration strategies to map and exploit structure in high-content datasets. We are also developing new graph-based methods the leverage ensemble techniques to conduct community discovery on sparse, extreme-scale graphs.

We have two primary application areas that motivate and define our contributions to statistical machine learning:

Exposure Biology

Exposure biology is one of the most powerful tools we can apply to study complex biological systems. In genetics, we perturb systems one gene or one genic locus at a time. In exposure biology, we use exogenous stressors to perturb entire pathways, even when those pathways are distributed across consortia of organisms. Observing adaptive and toxic responses through the lens of omics is providing new insights into levels of biological organization extending beyond individual cells or organisms, linking the biology of the nucleus to ecosystem dynamics. We view toxicology as systems-level genetics, and posit that it has a major role to play in the elucidation of processes ranging from development to speciation. Hence, the intrinsic societal values present in understanding the effects of environmental challenges on human and ecosystem health comes in addition to potentially transformative impacts on basic science. How do organisms coordinate metabolic and transcriptional responses in real time during acute insults or chronic climatological shifts? How do the adaptive responses of individuals impact populations, and what are the trans-generational consequences of punctate exposures? Nearly every cell in our bodies suffers tens of thousands of DNA damage events per day – hence, these questions regard processes that are fundamental to the nature of life.

Genome Dynamics

Transcriptional initiation is followed by hierarchical regulation mediated by RNA binding proteins, including the rate of RNA polymerization, splice site selection, concurrent co-transcriptional chromatin modifications that enforce or re-enforce promoter and splice site selections, polyadenylation, localization, stabilization, translational initiation and so forth. Covalent modifications to RNA and protein products add layers of combinatorial complexity that radically outstrip the sophistication of extant, dynamical models of gene expression, with feedback between nearly all processes. We focus particularly on the role of RNA binding proteins and chromatin in co-transcriptional splicing, and the feedback from RNA and protein-mediated splicing events and updates to chromatin state, including work on the functions of enhancer RNAs (eRNAs). We also work to understand translational regulation for genes encoding small open reading frames (smORFs) and long non-coding RNAs (lncRNAs). Dr. Brown co-chairs the RNA-Chromatin Integration Project within the ENCODE Consortium.

Other activities

2009-present               Member, Berkeley Drosophila Transcription Network Project

2011-present               Member, FANTOM5 Project

2012-present               Member, ENCODE3 Project Consortium Analysis Working Group

2012-present               Member, Berkeley Drosophila Genome Project

2012-present               Member, AAAS

2013-present               Member, Genetics Society of America

2014-present               Member, CHARGE Consortium

2014-present               Member, Microbes to Biomes Project

2014-present               Integrative Analysis Lead, Consortium for Environmental Omics and Toxicology

2015-present               Member, FANTOM6 Project

2015-present               Member, Society of Environmental Toxicology and Chemistry (SETAC)

2016-present               Board of Directors, SETAC NorCal Chapter

2016-present               Steering Committee Member and Co-Founder, SETAC Omics Global Advisory Group


Zhang W, Mao JH, Zhu W, Jain AK, Liu K, Brown JB & Karpen GH. (2016). “Centromere and kinetochore gene misexpression predicts cancer patient survival and response to radiotherapy and chemotherapy”. Nature Communications. In press. DOI: 10.1038/ncomms12619.

Orsini L, Gilbert D, Podicheti R, Jansen M, Brown JB, Solari OS, et al. (2016). Daphnia magna transcriptome by RNA-Seq across 12 environmental stressors. Scientific data, 3. PMID: 27164179 PMCID: PMC4862326 DOI: 10.1038/sdata.2016.30

Stoiber MH*, Celniker SE, Cherbas L, Brown JB*, Cherbas P. (2016). "Diverse hormone response networks in 41 independent Drosophila cell lines". G3 (Bethesda). pii: g3.115.023366. *Stoiber is Brown Lab. 

Stoiber MH, May G, Duff M, Obar R, Artavanis-Tsakonas S, Brown JB*, Graveley BR, Celniker SE. (2015). “Extensive cross-regulation in Drosophila post-transcriptional regulatory networks”. Genome Research. doi:10.1101/gr.182675.114 *co-senior author, corresponding author, Stoiber is Brown Lab.

Mao, JH, Langley SA, Huang Y, Hang M, Bouchard KE, Celniker SE, Brown JB, Jansson JK, Karpen GH, and Snijders AM. (2015). "Identification of genetic factors that modify motor performance and body weight using Collaborative Cross mice." Scientific reports. 5: 16247.

Brown JB, Celniker SE. (2015). “Lessons from modENCODE”. Annual Reviews of Genomics and Human Genetics. Available online: DOI: 10.1146/annurev-genom-090413-025448

Wessel J, et al. (2015). “Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility”. Nature Communications. (6):5897. doi: 10.1038/ncomms6897.

Brown JB, Boley N, Eisman R, May GE, Stoiber MH, Duff MO, Booth BW, Wen J, Park S, Suzuki AM, Wan KH, Yu C, Zhang D, Carlson JW, Cherbas L, Eads BD, Miller D, Mockaitis K, Roberts J, Davis CA, Frise E, Hammonds AS, Olson S, Shenker S, Sturgill D, Samsonova AA, Weiszmann R, Robinson G, Hernandez J, Andrews J, Bickel PJ, Carninci P, Cherbas P, Gingeras TR, Hoskins RA, Kaufman TC, Lai EC, Oliver B, Perrimon N, Graveley BR, Celniker SE. (2014). “Diversity and Dynamics of the Drosophila Transcriptome”. Nature. doi: 10.1038/nature12962. 

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