Hector Garcia Martin - Research Interests
My research interests focus on complexity [1], modeling of biological systems, microbial ecology, systems biology, metabolic engineering and metabolic flux analysis. I am particularly interested in the appearance of emergent properties, i.e. properties which arise from the tight connection and the sheer amount of components of a system rather than the particular characteristics of each of these components.
My personal opinion is that the most appealing examples of complexity are found in biology and hence, in spite of having enrolled in a theoretical physi
cs program for my Ph.D., I started my research life studying an ecological problem: the Species Area Relationship (SAR). This striking regularity, known for over a hundred years, states that as larger and larger areas of an ecosystem are sampled, the number of species contained in those areas grow as a power law with a coefficient close to one quarter. This pattern seemed almost universal, being applicable to all ranks of life, from bacteria to plants, mammals and birds. Along with my Ph.D. advisor, Nigel Goldenfeld, I managed to prove that such a universal trend was a robust consequence of spatial and abundance distributions and therefore rather independent from the particular dynamics of the individuals in the ecosystem (competition, dispersal... etc) [2] (Fig. 1).
Excited by the rich array of biocomplexity examples in 
ecology, but discouraged by the difficulty of performing experiments in real time in macroecological settings, I decided to focus on the burgeoning field of microbial ecology. I joined one of my group's efforts studying the impact of microbial ecosystems on terrace formation [3] (Fig. 2) in collaboration with geomicrobiologists, and developed new methods and applied known ones for the estimation of abundance and diversity based on 16S rRNA gene surveys [4]. While appealed by the new molecular techniques being applied to microbial ecology, I realized that
16S data can only produce a descriptive picture of a microbial ecosystem. I believe that ecosystem complexity can only be properly studied when predictive quantitative models are available and 16S data looked insufficient for this goal: we needed to understand community metabolism. I therefore concentrated my attention on the emerging field of metagenomics and, for a postdoc, decided to join the group of Philip Hugenholtz, a key player in one of the first two pioneer metagenomic papers [5]. Convinced that many of the tools commonly used in Physics may have future applications outside of the realms of this field, I devoted the rest of my Ph. D. to study Bose Einstein Condensates using Path Integral Monte Carlo simulations, at technique which involves no uncontrolled approximations in dealing with many-body interactions [6](Fig. 3).
At the Microbial Ecology group at the Joint Genome Institute (JGI) led by Philip 
Hugenholtz, I targeted a system that I thought fulfilled the required characteristics for successful modeling: Enhanced Biological Phosphorus Removal (EBPR) sludge, a microbial community widely used in wastewater engineering for phosphate removal. This community was an ideal target because a single bacterium was dominant, comprising ~80% of the abundance. Hence, this system is close enough to a single culture that techniques developed for single cultures can be adapted with limited modifications, while still a full microbial ecosystem with ~15 distinct relevant species. 
During my postdoctoral stay at JGI, my collaborators and I studied and published the metagenome for the EBPR sludge [7] (Fig. 4), which gave me the opportunity to learn how to analyze metagenomic data sets [7], [8] (Fig. 4 and 5) and help develop tools for this purpose [9], [10], [11], [12].
While the metagenomic study of this system yielded a detailed blueprint of the metabolic pathways present, this knowledge was descriptive in nature, rather than predictive. Questions such as: “which species will become dominant if a given condition (e.g. pH, or acetate availability) is altered?”, or “what will be the biochemical impact of a community on its environment?” are not answerable from just the knowledge of the genomes (or even transcripts, proteins or metabolites) present in a microbial community. We soon realized that the next step in terms of developing quantitative predictive models involved studying metabolic fluxes [13]. Quantitation of metabolic fluxes (i.e. the rate at which molecules proceed through a reaction per unit of time) for all metabolic reactions in a given organism entails knowledge of growth rates (from the carbon fluxes to biomass) and metabolite excretion (from outgoing metabolite fluxes).
We therefore established a collaboration with Jay Keasling, 
whose group operated an EBPR bioreactor and had previously tried (with limited success due to the lack of metagenomic, metatranscriptomic or metaproteomic information ) to produce a Flux Balance Analysis (FBA) model of a this system [14]. During this collaboration, I became acquainted with an accurate technique (13C Metabolic Flux Analysis [15]) to measure metabolic fluxes for pure cultures [16], [17] (Fig. 6). This seemed the perfect addition to provide the experimental check for flux models of EBPR (after proper modification to account for the community nature of this system)
It was at that time that JBEI [18] (Fig. 7) was formed and I joined as an LBL staff scientist
to help develop predictive quantitative models of microorganism metabolism for the hosts to be us ed at the institute: E. coli, S. cerevisiae and the archaea Sulfolobus acidocaldarius). It was a good opportunity to test my flux models before they were used on microbial communities.
In my current position I am interested developing models of microbial metabolism which are as predictive and quantitative as possible, in order to use them to direct metabolic engineering efforts and improve biofuel yields. I strongly believe that, in order for biology to mature further, it must become both quantitative and predictive. Biofuel production provides a perfect catalyst for this development since requires this depth of understanding of the biology involved and provides a worthy goal with a potentially very relevant impact in the world. Simultaneously I am adapting these models and using the information on microbial metabolism obtained from them to try and develop quantitative predictive models for microbial communities.
I am confident that the availability of quantitative models for microbial ecosystems and metabolism will reveal interesting examples of emergent properties and the importance of complexity in Biology.
[1] Melanie Mitchell, Complexity: A Guided Tour (Oxford University Press, USA, 2009).
[2] Héctor García Martín and Nigel Goldenfeld, Proc. Natl. Acad. Sci. U.S.A 103, 10310-10315 (2006).
[3] John Veysey II and Nigel Goldenfeld, Nat Phys 4, 310-313 (2008).
[4] Héctor García Martín and Nigel Goldenfeld, Environ Microbiol 8, 1145-1154 (2006).
[5] Gene W Tyson, Jarrod Chapman, Philip Hugenholtz, Eric E Allen, Rachna J Ram, Paul M Richardson, Victor V Solovyev, Edward M Rubin, Daniel S Rokhsar, and Jillian F Banfield, Nature 428, 37-43 (2004).
[6] Héctor García Martín, Statistical Analysis of Highly Correlated Systems in Biology and Physics (Thesis) (2004).
[7] Héctor García Martín, Natalia Ivanova, Victor Kunin, Falk Warnecke, Kerrie W Barry, Alice C McHardy, Christine Yeates, Shaomei He, Asaf A Salamov, Ernest Szeto, Eileen Dalin, Nik H Putnam, Harris J Shapiro, Jasmyn L Pangilinan, Isidore Rigoutsos, Nikos C Kyrpides, Linda Louise Blackall, Katherine D McMahon, and Philip Hugenholtz, Nat Biotechnol 24, 1263-9 (2006).
[8] F. Warnecke, P. Luginbühl, N. Ivanova, M. Ghassemian, T. H. Richardson, J. T. Stege, M. Cayouette, A. C. McHardy, G. Djordjevic, and N. Aboushadi, Nature 450, 560-565 (2007).
[9] Alice Carolyn McHardy, Héctor García Martín, Aristotelis Tsirigos, Philip Hugenholtz, and Isidore Rigoutsos, Nat Meth 4, 63-72.
[10] Yann Marcy, Cleber Ouverney, Elisabeth M Bik, Tina Lösekann, Natalia Ivanova, Hector Garcia Martin, Ernest Szeto, Darren Platt, Philip Hugenholtz, David A Relman, and Stephen R Quake, Proc. Natl. Acad. Sci. U.S.A 104, 11889-11894 (2007).
[11] Victor M Markowitz, Natalia Ivanova, Krishna Palaniappan, Ernest Szeto, Frank Korzeniewski, Athanasios Lykidis, Iain Anderson, Konstantinos Mavromatis, Konstantinos Mavrommatis, Victor Kunin, Hector Garcia Martin, Inna Dubchak, Phil Hugenholtz, and Nikos C Kyrpides, Bioinformatics 22, e359-367 (2006).
[12] V. Kunin, S. He, F. Warnecke, S. B. Peterson, H. Garcia Martin, M. Haynes, N. Ivanova, L. L. Blackall, M. Breitbart, F. Rohwer, K. D. McMahon, and P. Hugenholtz, Genome Research 18, 293-297 (2008).
[13] K Mcmahon, H García Martín, and P Hugenholtz, Current Opinion in Biotechnology 18, 287-292 (2007).
[14] J. Pramanik, P. L. Trelstad, A. J. Schuler, D. Jenkins, and J. D. Keasling, Water Research 33, 462-476 (1999).
[15] Y. J. Tang, H. G. Martin, S. Myers, S. Rodriguez, E. E. Baidoo, and J. D. Keasling, Mass Spectrometry Reviews (2008).
[16] Y. J. Tang, R. Chakraborty, H. G. Martin, J. Chu, T. C. Hazen, and J. D. Keasling, Applied and Environmental Microbiology 73, 3859-3864.
[17] Yinjie J. Tang, Héctor García Martín, Paramvir S. Dehal, Adam Deutschbauer, Xavier Llora, Adam Meadows, Adam Arkin, and Jay. D. Keasling, Biotechnol. Bioeng. 102, 1161-1169 (2009).
[18] Harvey W Blanch, Paul D Adams, Katherine M Andrews-Cramer, Wolf B Frommer, Blake A Simmons, and Jay D Keasling, ACS Chem. Biol 3, 17-20 (2008).
