Search

Huang H, Tu C, D'Odorico P. Ecosystem complexity enhances the resilience of plant-pollinator systems[J]. One Earth, 2021, 4(9): 1286-1296.

Tu C, D'Odorico P, Suweis S. Dimensionality reduction of complex dynamical systems[J]. Iscience, 2021, 24(1): 101912.

Tu C, Suweis S, D’Odorico P. Impact of globalization on the resilience and sustainability of natural resources[J]. Nature Sustainability, 2019, 2(4): 283-289.

One of the outstanding problems in complexity science and dynamical system theory is understanding the dynamic behavior of high-dimensional networked systems and their susceptibility to transitions to undesired states. Because of varied interactions, large number of parameters and different initial conditions, the study is extremely difficult and existing methods can be applied only to continuous-time systems. Here we propose an analytical framework for collapsing N-dimensional discrete-time systems into a S+1-dimensional manifold as a function of S effective parameters with S << N. Specifically, we provide a quantitative prediction of the quality of the low-dimensional collapse. We test our framework on a variety of real-world complex systems showing its good performance and correctly identify the regions in the parameter space corresponding to the system's transitions. Our work offers an analytical tool to reduce dimensionality of discrete-time networked systems that can be applied to a broader set of systems and dynamics.

AimsStem cell therapy has shown promise for treating myocardial infarction via re-muscularization and paracrine signalling in both small and large animals. Non-human primates (NHPs), such as rhesus macaques (Macaca mulatta), are primarily utilized in preclinical trials due to their similarity to humans, both genetically and physiologically. Currently, induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are delivered into the infarcted myocardium by either direct cell injection or an engineered tissue patch. Although both approaches have advantages in terms of sample preparation, cell-host interaction, and engraftment, how the iPSC-CMs respond to ischaemic conditions in the infarcted heart under these two different delivery approaches remains unclear. Here, we aim to gain a better understanding of the effects of hypoxia on iPSC-CMs at the transcriptome level.Methods and resultsNHP iPSC-CMs in both monolayer culture (2D) and engineered heart tissue (EHT) (3D) format were exposed to hypoxic conditions to serve as surrogates of direct cell injection and tissue implantation in vivo, respectively. Outcomes were compared at the transcriptome level. We found the 3D EHT model was more sensitive to ischaemic conditions and similar to the native in vivo myocardium in terms of cell-extracellular matrix/cell-cell interactions, energy metabolism, and paracrine signalling.ConclusionBy exposing NHP iPSC-CMs to different culture conditions, transcriptome profiling improves our understanding of the mechanism of ischaemic injury.

The sustainable use of common-pool resources (CPRs) is a major environmental governance challenge because of their possible over-exploitation. Research in this field has overlooked the feedback between user decisions and resource dynamics. Here we develop an online game to perform a set of experiments in which users of the same CPR decide on their individual harvesting rates, which in turn depend on the resource dynamics. We show that, if users share common goals, a high level of self-organized cooperation emerges, leading to long-term resource sustainability. Otherwise, selfish/individualistic behaviors lead to resource depletion ("Tragedy of the Commons"). To explain these results, we develop an analytical model of coupled resource-decision dynamics based on optimal control theory and show how this framework reproduces the empirical results.

The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as early warning signals of two majors price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time series' autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies., 14 pages, 5 figures, 1 table

Advances in experimental technologies, both in the laboratory and in the field, are generating an increasing volume of ecologically and sociologically relevant data, spanning a wide range of scales, revealing recurrent emergence of patterns in these systems. This "data explosion"' is both a challenge (inventing new tools for their analysis) and an opportunity (identifying rules driving the functioning of complex systems). However, data alone do not necessarily lead to an understanding of the systems of interest. At present, we are in a situation where in front of a rich (but common to many systems) phenomenology we have innumerable models for very specific cases that call for a general vision. This challenge is very fascinating for physicists, that have in their veins the search for general principles of apparently different phenomena. In particular, a very important property that seems to be shared by most of the socio-ecological systems is their ability to respond to perturbations, i.e. the system resilience. Cell biology, ecology, environmental science, and food security are just some of the many areas of investigation on the mechanisms increasing the system resilience. Nevertheless, not all socio-ecological systems display high resilience. In food security, the intensification of international food trade and local shocks in food production led to global food crises, and for example Suweis develops a framework to investigate the coupled global food-population dynamics and finds that the global food system is losing resilience (increasingly unstable and susceptible to conditions of crisis); In ecology, the concept of resilience has evolved considerably since Holling's (1973) seminal paper to describe the persistence of natural systems in the face of changes in ecosystem variables due to natural or anthropogenic causes. It has been suggested that in many ecosystems we are facing a lost of resilience and consequent loss of biodiversity. Therefore an important challenge is to understand what are the main drivers ruling the resilience of ecological communities, so that proper ecosystem management strategy can be developed. From data is emerging that one of the key feature of socio-ecological system resilience may lie in the architecture of the interaction networks. The topology of the interaction network may actually represent the "parameter" that system somehow self-tunes so that the system's responses to stimuli is optimized with respect to some feature (e.g. stability). In inanimate matter, spins or particles always have their mutual interactions turned on (with an intensity decaying with their relative distance) and the network describing their interaction is dense, with most of the connections present. In contrast, if we consider for instance an ecosystem, species interact selectively even if they coexist at short distances, and the species interaction network is sparse, that is, most of the interactions are turned off. At the same time, the interactions that are turned on form non-random evolving structures that are the result of some optimization process under adaptive/evolution pressure. Thanks to massive databases now easy available, characteristics similar to those just mentioned for ecological networks, have been observed also in gene-interaction network, in neuronal networks and even in social networks. These networks are very different and yet share a crucial aspect: they all have undergone biological/social evolution that has driven their incremental complexity. One particular long-standing question regards the relationship between stability (resilience) and complexity in ecological system. Many of the population dynamics modeling frameworks proposed in the literature cannot elude the celebrated May's theorem. This theorem, recently refined by Allesina and Tang states that the stability of the system depends on the product [SC], where [S] is the number of species and [C] is the fraction of non-zero pairwise interactions between species. This result leads to the so-called stability and complexity paradox debate: a system in order to be stable cannot be too large ([S] large) or too connected (large [C]). The paradox lies in the fact that empirically, ecosystems with a large number of species seem to be very stable. Moreover, recently it has been suggested that because of this stability paradox, in microbial ecosystems competition may play a much important roles than what expected until now. In fact in these models, competition has a stabilizing role in ecosystem dynamics, contrarily to cooperation that decreases the ecosystem resilience. During my Ph.D. I have used a physicists approach based on complex networks and statistical physics, to study the resilience in Socio-Ecological systems, how it is related to the system complexity and what is the role of cooperation in the ecosystem dynamics. I have used a comprehensive approach that includes data mining, theoretical modeling (both computational and analytical) and statistical analyses. In particular, I have investigated the efficiency of a recently proposed framework to study the resilience of complex interacting systems, what the role of cooperation and competition in the universal patterns theoretically predicted by the model, and its validation with data. I have then focused on the long-standing open question of the relation between complexity and resilience in ecosystems, by specifically focusing on how the architecture of interaction networks may confer to living systems their ability to promptly react to to perturbations (e.g. increase resilience). To do that we have developed a stochastic population dynamics model, generalizing an interacting non-equilibrium model known as the voter model, and I have also studied the effect of cooperation on the ecosystem resilience. The results of my work suggest a novel picture on the relation between complexity, cooperation and resilience, challenging previous results in the literature.