I am a biological physicist, with a background in statistical physics and strong experience
in interdisciplinary projects and collaborations. Broadly speaking, I am interested in understanding complex phenomena starting from simple rules and minimal assumptions. Most
of my research is in theoretical ecology, with a particular focus on ecological networks and
neutral theory. I am also working on problems in genomics and cell physiology and, more
recently, I started working on aging and stress response in C. elegans.
Methods, rather than specific topics, characterize my research: I apply methods borrowed from statistical mechanics, stochastic processes, and random matrix theory, trying to find a balance between simplicity of models and realism of assumptions. I enjoy both conceptual and theoretical problems, as well as data-driven projects. I like to collaborate with people from a diversity of backgrounds, from mathematics to experimental biology.
Ecosystems are typically composed by a large number of interacting individuals and species. What do determine their stability respect to perturbation and their persistence in time? The first lesson of statistical physics is that we cannot use the same methods that we use to study the motion of two bodies to describe a tank of gas. Following a long tradition in theoretical ecology, we study stability and persistence of ecosystems using random matrices. This approach allows to identify what are the relevant quantities that determine the fate of those systems. In particular we were able to find a stability condition for empirical food-webs [NC2015a] and to characterize how perturbations spread across species [NC2015b]. We also studied the persistence of a population in a fragmented landscape using random matrix techniques [PCB2015].
Life populates almost every corner of this planet, with striking differences of forms and shapes, that reflect the adaptation to a particular environment and unique evolutionary histories. Despite the contingency of the processes shaping genomes, remarkable regularities and patterns can be found across organisms. We studied how different patterns involving genes with the same functions were connected to patterns of evolutionary closely related genes [NAR2012]. We also studied the role of HGT in shaping the genomes of prokaryotes and the relation between genes occurrence and HGT [NAR2014].
Macroscopic phenomena are intrinsically stochastic and noise strongly affects the species composition and diversity of ecosystems. Statistical mechanics offers many tools that can be applied to study these systems [RMP2016]. We showed how spatial dispersal of species makes ecosystems inevitably out of equilibrium [EPL2012]. While this fact makes simple models analytically untractable, there is still room for analytical solutions in spatially explicit systems. Using a phenomenological model we were able to connect different spatial and non-spatial ecological patterns [JTB2012].
The dream of every cell is to become two cells. Without a coupling between cell size and division, cells would not have the experimentally observed stationary distribution of sizes. Thanks to the availability of high-throughput data, it has become possible to study cell-size control quantitatively, and many models have been proposed to describe it. We showed that many empirical observations can be explained using scaling arguments, without the need of invoking specific mechanisms [PRE2016]. Scaling allows, in fact, to connect apparently unrelated measurable quantities in a parameter-free setting. On the other hand, we characterized the class of models compatible with scaling, isolating few parameters that describe cell-size control across species and conditions [1606.09284].
Scale-free phenomena are quite common in biological and social systems. In condensed matter physics long range correlations emerges by fine-tuning the parameters to a critical point. Whether these observations are truly signatures of criticality is still an open problem and we are trying to find ways to identify in an unbiased way criticality in empirical data. We proposed a general framework, based on information theory and amenable of analytical treatment [JSTAT2016], able to show how criticality can emerge without fine-tuning but as a consequence of evolution and adaptation [PNAS2014].
J. Grilli, M. Adorisio, S. Suweis, G. Barabás, J.R. Banavar, S. Allesina and A. Maritan
Feasibility and coexistence of large ecological communities.
Nature Communications. 8:0. 2017.
S. Azaele, S. Suweis, J. Grilli, I. Volkov, J.R. Banavar and A. Maritan
Statistical mechanics of ecological systems: neutral theory and beyond.
Review of Modern Physics. 88, 035003. 2016.
J. Grilli, T. Rogers and S. Allesina
Modularity and stability in ecological communities.
Nature Communications. 7:12031. 2016.
J. Hidalgo, J. Grilli, S. Suweis, A. Maritan and M.A. Muñoz
Cooperation, competition and the emergence of criticality in communities of adaptive systems.
Journal of Statistical Mechanics: Theory and Experiment. (2016):033203. 2016.
A.S. Kennard, M. Osella, A. Javer, J. Grilli, P. Nghe, S. Tans, P. Cicuta and M. Cosentino Lagomarsino
Individuality and universality in the growth-division laws of single E. coli cells.
Physical Review E 93 (1):012408. 2016.
S. Suweis, J. Grilli, J.R. Banavar, S. Allesina and A. Maritan
Effect of localization on the stability of mutualistic ecological networks.
Nature Communications. 6: 10179. 2015.
S. Allesina, J. Grilli, G. Barabás, S. Tang, J. Aljadef and A. Maritan
Predicting the stability of large structured food webs.
Nature Communications. 6: 7842. 2015.
J. Grilli, G. Barabás and S. Allesina
Metapopulation persistence in random fragmented landscapes.
Plos Computational Biology. 11(5): e1004251. 2015.
J. Hidalgo, J. Grilli, S. Suweis, M.A. Muñoz, J.R. Banavar and A. Maritan
Information-based fitness and the emergence of criticality in living systems.
PNAS. 111(28):10095-10100. 2014.
J. Grilli, M. Romano, F. Bassetti and M. Cosentino Lagomarsino
Cross-species gene-family fluctuations reveal the dynamics of horizontal transfers.
Nucleic Acids Research. 42(11):6850-6860. 2014.
S. Suweis, J. Grilli and A. Maritan
Disentangling the effect of hybrid interactions and of the constant effort hypothesis on ecological community stability.
Oikos. 123(5):525-532. 2014.
J. Grilli, S. Suweis and A. Maritan
Growth or reproduction: emergence of an evolutionary optimal strategy.
Journal of Statistical Mechanics: Theory and Experiment. 2013(10):P10020. 2013.
J. Grilli, S. Azaele, J.R. Banavar and A. Maritan
Absence of detailed balance in ecology.
Europhysics Letters. 100:38002. 2012.
J. Grilli, S. Azaele, J.R. Banavar and A. Maritan
Spatial aggregation and the species-area relationship across scales.
Journal of Theoretical Biology. 313:87-97. 2012.
L. Grassi, J. Grilli and M. Cosentino Lagomarsino
Large-scale dynamics of horizontal transfers.
Mobile Genetics Elements. 2(3):163-167. 2012.
J. Grilli, B. Bassetti, S. Maslov and M. Cosentino Lagomarsino
Joint scaling laws in functional and evolutionary categories in prokaryotic genomes.
Nucleic Acids Research. 40(2):530-540. 2012
J. Grilli, M. Osella, A. S. Kennard and M. Cosentino Lagomarsino
Relevant parameters in models of cell division control
C. Tu, S. Suweis, J. Grilli and A. Maritan
On the Universality of Resilience Patterns in Complex Networks: Limitations and Extensions
M. Adorisio, J. Grilli, S. Suweis, S. Azaele, J.R. Banavar and A. Maritan
Spatial maximum entropy modeling from presence/absence tropical forest data.
Department of Ecology and Evolution
University of Chicago
1101 E 57th st
60637 Chicago, IL