Published articles

S.D. Burz, S. Čaušević, et al. (incl. A.R. Pacheco). From microbiome composition to functional engineering, one step at a time. Microbiology and Molecular Biology Reviews (2023). [View]

M. Schäfer*, A.R. Pacheco*, R. Künzler, M. Bortfeld-Miller, C.M. Field, E. Vayena, V. Hatzimanikatis, and J.A. Vorholt. Metabolic interaction models recapitulate leaf microbiota ecology. Science 380, eadf5121 (2023). [View] *Contributed equally

A.R. Pacheco and J.A. Vorholt. Resolving metabolic interaction mechanisms in plant microbiomes. Current Opinion in Microbiology 74, 102317 (2023). [View]

A.R. Pacheco*, C. Pauvert*, D. Kishore, and D. Segrè. Toward FAIR representations of microbial interactions. mSystems e00659-22 (2022). [View] *Contributed equally

N. Ankrah, D.B. Bernstein, M. Biggs, M. Carey, M. Engevik, B. García-Jiménez, M. Lakshmanan, A.R. Pacheco , S. Sulheim, and G. Medlock. Enhancing microbiome research through trustworthy genome-scale metabolic modeling. mSystems 6,6 (2021). [View]

I. Dukovski, D. Bajić, J.M. Chacón, M. Quintin, J.C.C. Vila, S. Sulheim, A.R. Pacheco , D.B. Bernstein, W.J. Riehl, K.S. Korolev, A. Sanchez, W.R. Harcombe, and D. Segrè. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nature Protocols 16, 5020-5082 (2021). [View]

D.C. Moyer, A.R. Pacheco , D.B. Bernstein, and D. Segrè. Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure. Journal of Molecular Evolution 89, 472-483 (2021). [View]

A.R. Pacheco and D. Segrè. An evolutionary algorithm for designing microbial communities via environmental modification. J. R. Soc. Interface 18, 20210348 (2021). [View]

A.R. Pacheco , M.L. Osborne, and D. Segrè. Non-additive microbial community responses to environmental complexity. Nature Communications 12, 2365 (2021). [View]

A.R. Pacheco and D. Segrè. A multidimensional perspective on microbial interactions. FEMS Microbiology Letters 366, 1–11 (2019). [View]

A.R. Pacheco , M. Moel, and D. Segrè. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nature Communications 10, 103 (2019). [View]

B.M. Maoz, A. Herland, E.A. FitzGerald, T. Grevesse, C. Vidoulez, A.R. Pacheco , S.P. Sheehy, T.E. Park, S. Dauth, R. Mannix, N. Budnik, K. Shores, A. Cho, J.C. Nawroth, D. Segrè, B. Budnik, D.E. Ingber, and K.K. Parker. Endothelial-neuronal cell metabolic coupling across the blood-brain barrier revealed using linked human organs-on-chips. Nature Biotechnology 36, 865–874 (2018). [View]

J. Beal, T. Haddock-Angelli, M. Gershater, K. de Mora, M. Lizarazo, J. Hollenhorst, R. Rettberg, iGEM Interlab Study Contributors (incl. A.R. Pacheco). Reproducibility of Fluorescent Expression from Engineered Biological Constructs in E. coli. PLOS ONE 11, e0150182 (2016). [View]

T. Haddock, D. Densmore, E. Appleton, S. Carr, S. Iverson, A.R. Pacheco , et al. The Modular Cloning Assembly: Standardized Assembly of Bacterial Transcriptional Units Using Type IIS Restriction Enzymes BsaI and BpiI. The BioBricks Foundation RFC 94 (2015). [View]

FAIR microbial interaction data

Despite a growing body of data on microbial interactions, we lack a common way to describe and compare observations of these phenomena across studies. Together with scientists in the USA, Germany, and Switzerland, I have designed a metadata framework to enable the systematic cataloguing and comparison of interaction attributes. This will not only improve our understanding of how prevalent specific interaction attributes are across systems, but also improve efforts to reproduce scientific observations and parameterize models.

Predicting interaction outcomes in plant microbiomes

Plant leaves represent one of the largest terrestrial habitats, hosting a diverse set of microbial organisms. However, it remains difficult to predict which specific organisms will survive or be outcompeted on the plant, hindering efforts to design microbiomes for ecosystem management and agriculture. We developed a modeling platform to predict metabolic interaction outcomes between 224 leaf-associated bacteria, establishing one of the largest collection of curated genome-scale metabolic models to date. These models accurately predicted interactions between leaf bacteria and indicated the metabolic mechanisms that may be underlying patterns of community assembly in this ecosystem.

Environmental complexity of microbial ecosystems

Despite a growing understanding of how individual resources affect microbial communities, we lack a systematic understanding of how they behave under combinations of nutrients such as those found in nature. To begin bridging this gap, we studied the growth and taxonomic diversity of hundreds of synthetic microbial communities under increasingly complex environmental compositions. We found that community growth yield scales linearly with environmental complexity, though a similar relationship was not observed for taxonomic diversity. Using stoichiometric and consumer resource models, we discovered simple ecological rules that govern this environment-phenotype relationship, establighing a foundation for the design of communities via environmental modification.

"Costless" metabolic exchange between microbes

Paradoxically, many natural environments that are poor in resources can harbor high levels of microbial biodiversity. While metabolic cross-feeding provides an answer to this paradox, it often involves costly metabolites and is susceptible to cheating phenotypes. We explored an alternative mode of exchange, driven by metabolites that do not impose such a fitness cost on their producing organism. By applying stoichiometric modeling to over 2 million pairs of organisms in different environments, we quantified how these "costless" metabolites could lead to the spontaneous emergence of stable interspecies interactions.

Machine learning for microbial community design

Synthetic microbial communities have the potential to revolutionize applications in human health, environmental protection, and industrial processes. Given the challenges of genetic engineering, we ask whether rationally designing the environmental composition of a microbial ecosystem can confer desired structures and functional properties on communities. Here, we combined a search algorithm with flux-balance modeling to identify environmental compositions that lead to a number of specific community behaviors.

Blood-brain barrier metabolism

The neurovascular unit regulates crucial metabolic processes within the central nervous system. Despite its importance to neuronal function, it remains difficult to mechanistically resolve its complex metabolic functions. Here, we developed a microfluidic brain-on-chip model to model metabolite flux across the blood-brain barrier. We also carried out metabolic flux balance analysis to quantify the metabolic transformations that occur across this structure. This analysis, coupled with proteomic and metabolomic measurements enabled by our microfluidic organ chip, provided a highly detailed view into neuronal metabolism and central nervous system function.

Multidimensionality of microbial interactions

New high-throughput technologies have yielded a wealth of information on microbial community interactions. However, as these findings are largely reported on a case-by-case basis, it remains difficult to compare interactions across different studies and infer larger scale patterns. Here, we designed a framework that embraces the many mechanistic aspects of microbial interactions, in order to enable systematic comparison and future mechanistic studies.

Spatiotemporal modeling of engineered microbial community interactions

To enable the many applications of synthetic ecology, it is necessary to gain a more complete understanding of how individual organisms interact with each other. As part of a broader project to engineer a mutualistic multispecies bacterial consortium, I carried out flux-balance modeling of their metabolic exchanges. This study informed the design of the experimental community, and also yielded predictions on its behavior according to specific spatial arrangements of microbes.

iGEM Diversity and Inclusion Publicity

As co-chair of the International Genetically Engineered Machine (iGEM) Diversity and Inclusion commitee, I created a suite of publicity materials for distribution at the Grand Jamboree, a global science event attended by over 5,000 participants. Inspired by the diverse and intersecting identities of its participants, the brochures aimed to outline the mission of the committee while ensuring clarity and legibility. The D&I Committee compiles and analyzes information on the makeup of the competition at all levels - from students to mentors to judges. We also serve as a resource for teams in creating new efforts centered on inclusivity in science and host online and in-person events throughout the year. Our 2022 series on mental health in STEM brought together students and experts to highlight mental health challenges in research and learn about management strategies.

Santa Fe Institute CSSS T-shirt

As a participant in the Santa Fe Institute's 2018 Complex Systems Summer School (CSSS), I entered in a design contest to create our cohort's T-shirt. My design (chosen as the winner!) celebrates the international nature of our class participants, as well as SFI's unique identity. Here, the SFI logo serves as a central node connecting a network of students, each of which is represented as an outer node. These are, in turn, connected based on the place of origin of each participant.

Boston University UGSO Symposium Publicity

The Boston University Underrepresented Graduate Student Organization (UGSO) is the first student-led group on BU's Charles River Campus aimed at fostering an inclusive academic community for graduate students from traditionally underrepresented backgrounds. In 2019, I chaired UGSO's first academic research symposium, an event brought together over 85 students, faculty, staff, and administrators to celebrate student research. I also designed our publicity materials using an integrated scheme that was implemented across all of our print and online media. This design, anchored on the color red, prominently features a multicolored mosaic of interconnected elements that celebrate participants' diverse identities, which are integral to the BU community.

Boston University Bioinformatics T-shirt

This T-shirt design, which was selected as the winner of the 2017 BU Bioinformatics design contest, integrates three elements that are key to our program's identity: (1) a heatmap (a common visualization tool in bioinformatics) that encodes (2) the names of all of the program's students, making up (3) the Boston skyline.

Mexico City Transit Diagram

Starting with with a desire to improve my Illustrator skills and channel my inner transit nerd, I undertook a small project to redesign the Transit diagram of Mexico City. Incidentally, CDMX's diagram at the time seemed like it could use some improvement: the stations are geographically accurate, but that comes at the expense of the visual flow of the lines themselves and the clarity of the text. Importantly, the diagram omitted connections to other public transit options as well as accessibility information. What I got most excited about was using all of the iconic station logos, which were missing from the original map. These logos were originally created with the goal of helping illiterate passengers find their way (in the 1960's), and draw from landmarks or associations in the vicinity of each station. Some of them (e.g. Olímpica of Periférico Oriente) are associated with modern landmarks and events, while others (e.g. Tacuba, my grandparents' station) draw from Aztec glyphs and place names.

Boston University iGEM Logo and Wiki

As one of three student members of the 2014 Boston University iGEM team, I designed our team's website and logo. Our logo design, pictured here, draws from a graphical representation of a DNA plasmid, which we used extensively in our project. The colors represent the three key biological components of our project: fusion proteins, tandem promoters, and low-copy origins of replication, as well as integrating the three key steps of synthetic biology workflows: design, build, and test.

COMETS Logo

COMETS (Computation Of Microbial Ecosystems in Time and Space) is an open-source software tool developed by the Segrè lab that simulates the spatiotemporal dynamics of microbial communities. In addition to contributing to an expansion of COMETS' modeling capabilities, I designed the logo for our software platform. By incorporating stylized microbial colonies in its design, this logo reflects COMETS' special capabilities for carrying out virtual petri dish experiments.