My research interest is aimed at the study of the mechanisms of emergence of collective behavior in complex systems, in particular, how information dynamics and complex interconnections of individual subunits can lead to adaptive capabilities of living systems.
During my doctoral work at Goldstein’s lab at the University of Arizona, I studied the collective behavior of swarming self-propelled bacteria. This research helped to elucidate the relationships between body alignment, coherent motion, and cell concentration in the large-scale coherent phase of these microorganisms. An important implication I learned from this research is that collective responses in living systems can emerge as an effect of cell interactions with other cells and their environment, and thus their ecological context, rather than limited to their specific individual programming. And appropriate collective responses lead to the adaptive fitness of populations of cells. These ideas motivated my current line of research interest. In my postdoctoral work, I took a systems-biology perspective to explore the phenomenon of cancer, understood as a breakdown of multicellularity. Namely, neoplasms exhibit a switch in the selective process from the organismal level to the cellular level, promoting cell heterogeneity and competition, the inverse process to the transition to multicellularity. As a member of Dr. Paul Davies’ team in the ASU-PSOC program, I developed a model of metastasis revealing that early stages of organ invasion could be driven by rare event dynamics rather than a selective advantage of the invasive tumor cells. This result emphasized the importance of diversity and niche construction in the progress of the disease, bringing about eco-evolutionary arguments concerning cell-cell and cell-tissue interactions. In collaboration with Dr. Kimberly Bussey, we unveiled an evolutionary signature in cancer genomes. Specifically, our bioinformatic studies correlated gene evolution with observed patterns of mutations and expression changes in cancer, demonstrating that ancient genes tend to be robust and that their genomic modifications are associated with advanced stages in cancer. This work revealed that cancer samples carry a signature of stress-induced mutagenesis, an ancestral biological mechanism of evolvability and diversification. On a different line of research, in collaboration with Dr. Julia Bos from the Institut Pasteur in Paris, we used image processing and particle tracking techniques to study the dispersion process of membrane vesicles in bacteria challenged with antibiotic stress. This research uncovered some important details in the dynamics of the mechanism of the emergence of drug resistance, a subject that is also very pertinent in the study of cancer cells via orthologous mechanisms in multicellular tissue cells.
A recurrent feature in these lines of research is that tumor diversification and evolution are central properties of cancer. And while tumors are well recognized as an ecological and evolutionary process of populations of cells, very little progress has been done in drawing clinically relevant distinctions that identify how different tumors are evolving and how to manipulate the corresponding mechanisms to maximize benefits to the patient. Modeling of the spatial heterogeneity, complexity, and evolutionary dynamics of populations of cancer cells is an ideal framework to assess the disease recurrence, to study the clonal expansion of resistant strains challenged with different cancer therapies and explore for best strategies of dosage modulation to achieve optimal control of tumor growth, prolong the expected time to recurrence and minimize the effects of treatment. As such, the study of the evolution of resistance to therapy is a natural focus of interest of major clinical importance that I have pursued as part of Carlo Maley’s lab at ASU. An important direction my current work has taken is the adaptation of methods of spatial statistics into cancer research. This is an active collaboration with Dr. Joel Brown from the Moffitt Cancer Center in which I have translated several statistical packages generally implemented in fields like landscape ecology and geographical information systems into an analysis pipeline that allows us to investigate the ecological aspects of tumor microenvironments using digitalized cell location data from histopathological images.
1. Top-Down Causation effect: During my graduate research with Dr. Juan Jimenez at the Universidad Central de Venezuela I developed statistical methods that showed that the progression to a collective phase in a network of coupled dynamical elements is characterized by a sharp transition in the information that flows from the macroscopic scales into microscopic scales of the system. This work was seminal in suggesting that self-organization in complex systems develops when individual parts carry complete information about the global dynamics and is now described as the Top-Down Causation effect.
a. Cisneros L, Jiménez J, Cosenza M and Parravano A: Information transfer and nontrivial collective behavior in chaotic coupled map networks. Physical Review E, 65: 045204(R), 2002
b. Walker SI, Cisneros L and Davies PCW: Evolutionary Transitions and Top-Down Causation. Proceedings of Artificial Life, 13: 283-290, 2012
2. Biofluid dynamics of self-propelled microorganisms: During my doctoral work with Raymond Goldstein at the University of Arizona, I participated in their groundbreaking research on the collective behavior of swarming rod-like self-propelled bacteria B. subtilis. When these microorganisms are in concentrated conditions, local body alignments and hydrodynamics conditions allow for coherent motion bringing forward large-scale patterns akin to the flocking phenomena observed in fish schools or bird flocks. I combined microfluidic experiments using fluorescence microscopy, developed image processing methods, and analysis tools to achieve a comprehensive characterization of the collective phase of bacterial suspensions under different conditions. I also produced simulations and theoretical models that helped elucidate the relationships between alignment, coherent motion, and cell concentration in the collective phase. Some important implications of this work are that when cells engage in such collective behavior, they increase their chances of survival as a community. For instance, coherent swimming motion causes faster transport of oxygen or nutrients in the cellular medium, allows them to share their individual risks, or organize in biofilm-forming structures, thus providing primitive forms of division of labor. This work is important in showing how physical constraints might drive early multicellularity, showing that the collective response of cell colonies can have adaptive capacity and that this process can be codified in the network of cell interactions and their ecological context, rather than programmed behavior of individual microorganisms.
a. Dombrowski C, Cisneros L, Chatkaew S, Kessler JO, and Goldstein RE.: Self-Concentration and Large-Scale Coherence in Bacterial Dynamics. Physical Review Letters, 93: 098103, 2004
b. Tuval I, Cisneros L, Dombrowski C, Wolgemuth CW, Kessler JO, and Goldstein RE: Bacterial Swimming and Oxygen Transport Near Contact Lines. Proceedings of the National Academy of Sciences, 102: 2277-2282, 2005
c. Cisneros L, Kessler JO, Sujoy Ganguly S and Goldstein RE: Dynamics of swimming bacteria: Transition to directional order at high concentration. Physical Review E, 83:061907, 2011
d. Cisneros L, Kessler JO, Sujoy Ganguly S and Goldstein RE: Individual to Collective Dynamics of Swimming Bacteria, in Natural locomotion in fluids and on surfaces: Swimming, flying, and sliding. IMA Volume. Springer. New York, 2012.
3. Quantifying metastatic inefficiency: my theoretical work with Dr. Newman of the University of Dundee produced a very interesting result concerning metastatic inefficiency. Taking a simple stochastic birth-death process approach to model the early stages of metastatic invasion, we conjectured that as a tumor grows from a single seeding cell, it will eventually be large enough to establish its internal microenvironment (the critical size at which interior cells are physically protected), but before then, cells would be susceptible to apoptotic signals or immune surveillance from the surrounding tissue. Our model suggested that in the context of a large number of invasive attempts, rare event statistics, rather than a selective advantage, can be sufficient for a small number of successful micro-tumors to emerge in a patient with metastatic disease. Rigorous mathematical modeling showed that the expected waiting time of these rare micro-tumors scales exponentially with the inverse of the critical size but only linearly with the inverse of the constitutive cell fitness. Therefore, any small modification of the conditions that determine the critical size would translate into a significant lengthening of the life expectancy of a patient undergoing metastasis, suggesting the tissue microenvironment, and thus conditions of the cell ecology, are paramount therapeutic targets.
a. Cisneros L and Newman T: Quantifying metastatic inefficiency: rare genotypes versus rare dynamics. Physical Biology, 11: 046003, 2014.
4. Stress-induced mutagenesis is a cancer phenotype: my bioinformatic work on publicly available cancer data correlated gene evolutionary ages with the observed patterns of mutations in whole-genome sequencing data and patterns of expression changes. In particular, my collaboration with Dr. Bussey yielded a collection of (patented) bioinformatics methodologies to detect mutational signatures of stress-induced mutagenesis in whole-genome sequencing of cancer samples, a conserved ancestral biological mechanism of diversification that could be engaged in cancer samples and be determinant in the progression of the disease, response to therapy and recurrence.
a. Cisneros L, Bussey KJ. and C Vasque: Identification of a signature of evolutionarily conserved stress-induced mutagenesis in cancer. bioRxiv 2021.04.17.440291(article under review)
b. Cisneros L, Bussey KJ, Orr A, Miocevic M, Lineweaver CH and Davies P: Ancient genes establish stress-induced mutation as a hallmark of cancer. Plos ONE, 12: e0176258, 2017.
c. Zhou JX, Cisneros L, Knijnenburg T, Trachana K, Davies P and Huang S.: Phylostratigraphic analysis of tumor and developmental transcriptomes reveals relationship between oncogenesis, phylogenesis and ontogenesis. Convergent Science Physical Oncology, 4:025002, 2018
d. Bussey KJ, Cisneros L, Lineweaver CH, Davies P: Ancestral gene regulatory networks drive cancer. Proceedings of the National Academy of Sciences, 114:6160, 2017.
5. Dynamics of bacterial membrane vesicles: Membrane vesicles are ubiquitous carriers of molecular information. My work with Dr. Julia Bos provided a quantitative analysis of the motion of individual vesicles in living microbes using fluorescence microscopy, showing that when bacteria are challenged with low doses of antibiotics, vesicle production and traffic, quantified by instantaneous vesicle speeds and total traveled distance per unit time, are significantly enhanced. Our results provide insights into the spatial organization and dynamics of membrane vesicles in microcolonies during the process of adaptation to drugs.
a. Bos J, Cisneros LH, Mazel D. Real-time tracking of bacterial membrane vesicles reveals enhanced membrane traffic upon antibiotic exposure. Sci Adv. 2021 Jan 20;7(4):eabd1033.