Papers by Matteo Barberis
Bioinformatics, Sep 22, 2022
The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod... more The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod) brings together interdisciplinary scientists interested in combining data-driven computational modelling, multi-scale mechanistic frameworks, large-scale-omics data and bioinformatics. SysMod's main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, a meeting for computer scientists, biologists, mathematicians, engineers and computational and systems biologists. The 2021 SysMod meeting was conducted virtually due to the ongoing COVID-19 pandemic (coronavirus disease 2019). During the 2-day meeting, the development of computational tools, approaches and predictive models was discussed, along with their application to biological systems, emphasizing disease mechanisms. This report summarizes the meeting.
arXiv (Cornell University), Jun 4, 2020
Computer-aided design for synthetic biology promises to accelerate the rational and robust engine... more Computer-aided design for synthetic biology promises to accelerate the rational and robust engineering of biological systems; it requires both detailed and quantitative mathematical and experimental models of the processes to (re)design, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. Computer-aided design strategies require quantitative representations of cells, able to capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform designbuild-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems, and we discuss several challenges for the realization of our vision. The possibility to describe and build in silico whole-cells offers an opportunity to develop increasingly automatized, precise and accessible computer-aided design tools and strategies throughout novel interdisciplinary collaborations.
Biotechnology Advances, 2012
Budding yeast cell cycle oscillates between states of low and high cyclin-dependent kinase activi... more Budding yeast cell cycle oscillates between states of low and high cyclin-dependent kinase activity, driven by association of Cdk1 with B-type (Clb) cyclins. Various Cdk1-Clb complexes are activated and inactivated in a fixed, temporally regulated sequence, inducing the behaviour known as "waves of cyclins". The transition from low to high Clb activity is triggered by degradation of Sic1, the inhibitor of Cdk1-Clb complexes, at the entry to S phase. The G 1 phase is characterized by low Clb activity and high Sic1 levels. High Clb activity and Sic1 proteolysis are found from the beginning of the S phase until the end of mitosis. The mechanism regulating the appearance on schedule of Cdk1-Clb complexes is currently unknown. Here, we analyse oscillations of Clbs, focusing on the role of their inhibitor Sic1. We compare mathematical networks differing in interactions that Sic1 may establish with Cdk1-Clb complexes. Our analysis suggests that the wave-like cyclins pattern derives from the binding of Sic1 to all Clb pairs rather than from Clb degradation. These predictions are experimentally validated, showing that Sic1 indeed interacts and coexists in time with Clbs. Intriguingly, a sic1Δ strain looses cell cycle-regulated periodicity of Clbs, which is observed in the wild type, whether a SIC1-0P strain delays the formation of Clb waves. Our results highlight an additional role for Sic1 in regulating Cdk1-Clb complexes, coordinating their appearance.
Current Opinion in Systems Biology, Mar 1, 2021
Yeast, Sep 1, 2015
The yeast spindle pole body (SPB) is the functional equivalent of the mammalian centrosome. The h... more The yeast spindle pole body (SPB) is the functional equivalent of the mammalian centrosome. The half bridge is a SPB substructure on the nuclear envelope (NE), playing a key role in SPB duplication. Its cytoplasmic components are the membrane-anchored Kar1, the yeast centrin Cdc31 and the Cdc31 binding protein Sfi1. In G1, the half bridge expands into the bridge through Sfi1 carboxy-terminal (Sfi1-CT) dimerization, the licensing step for SPB duplication. We exploited PALM to show that Kar1 localizes in the bridge center. Binding assays revealed direct interaction between Kar1 and C-terminal Sfi1 fragments. kar1∆ cells whose viability was maintained by the dominant CDC31-16 showed an arched bridge, indicating Kar1's function in tethering Sfi1 to the NE. Cdc31-16 enhanced Cdc31-Cdc31 interactions between Sfi1-Cdc31 layers as suggested by binding free energy calculations. In our model, Kar1 binding is restricted to Sfi1-CT and Sfi1 C-terminal centrin-binding repeats, centrin and Kar1 provide crosslinks, while Sfi1-CT stabilizes the bridge and ensures timely SPB separation.
Springer eBooks, 2003
A number of techniques have been developed in order to address issues such as genome, trascriptom... more A number of techniques have been developed in order to address issues such as genome, trascriptome and proteome analysis. However, a time and cost effective technique for interactome analysis is still lacking. Lots of methods for the predicion of protein-protein interacions have been developed: some of them are based on high quality alignment of sequences, others are based on the tridimensional features of proteins, but they all bear strong limitations that make impossible their large scale application. Recently, an SVM-based machine learning approach has been used to address this topic. Although the method was able to correctly classify 80% of the test samples, it was not applied to the prediction of yet unknown interactions. In this work, we address this topic and show that an optimized, SVM-based machine learning approach trained with combinations of shuffled sequences as examples of lack of interaction is unable to make large scale predictions of interaction.
Bioinformatics, Feb 1, 2018
Motivation: Multi-scale modeling of biological systems requires integration of various informatio... more Motivation: Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner. Results: We present GEMMER (GEnome-wide tool for Multi-scale Modeling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae. GEMMER creates network visualizations that integrate information on function, temporal expression, localization and abundance from various existing databases. GEMMER supports modeling efforts by effortlessly gathering this information and providing convenient export options for images and their underlying data.
International Journal of Molecular Sciences, Apr 20, 2017
Mathematical models are key to systems biology where they typically describe the topology and dyn... more Mathematical models are key to systems biology where they typically describe the topology and dynamics of biological networks, listing biochemical entities and their relationships with one another. Some (hyper)thermophilic Archaea contain an enzyme, called non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GAPN), which catalyzes the direct oxidation of glyceraldehyde-3-phosphate to 3-phosphoglycerate omitting adenosine 5-triphosphate (ATP) formation by substrate-level-phosphorylation via phosphoglycerate kinase. In this study we formulate three hypotheses that could explain functionally why GAPN exists in these Archaea, and then construct and use mathematical models to test these three hypotheses. We used kinetic parameters of enzymes of Sulfolobus solfataricus (S. solfataricus) which is a thermo-acidophilic archaeon that grows optimally between 60 and 90 • C and between pH 2 and 4. For comparison, we used a model of Saccharomyces cerevisiae (S. cerevisiae), an organism that can live at moderate temperatures. We find that both the first hypothesis, i.e., that the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plus phosphoglycerate kinase (PGK) route (the alternative to GAPN) is thermodynamically too much uphill and the third hypothesis, i.e., that GAPDH plus PGK are required to carry the flux in the gluconeogenic direction, are correct. The second hypothesis, i.e., that the GAPDH plus PGK route delivers less than the 1 ATP per pyruvate that is delivered by the GAPN route, is only correct when GAPDH reaction has a high rate and 1,3-bis-phosphoglycerate (BPG) spontaneously degrades to 3PG at a high rate.
FEBS Journal, Mar 21, 2012
Name Function Ace2 Transcription factor required for septum destruction after cytokinesis; its ph... more Name Function Ace2 Transcription factor required for septum destruction after cytokinesis; its phosphorylation prevents nuclear exit during the M ⁄ G1 transition, promoting localization to daughter cell nuclei, and also prevents nuclear import during cell cycle phases other than cytokinesis Ash1 Transcription factor that specifies daughter cell fate in mating-type switching; translated in the distal tip of anaphase cells and accumulates in daughter cell nuclei Cdc14 Protein phosphatase located in the nucleolus and released upon entry into anaphase to promote mitotic exit; reduces Cdk1 ⁄ Clb activity Cdc20 Cell cycle-regulated activator of APC; required for metaphase ⁄ anaphase transition; directs ubiquitination of mitotic cyclins Cdc34 Ubiquitin-conjugating enzyme (E2) and catalytic subunit of SCF; regulates cell cycle progression by targeting substrates for degradation Cdc4 F-box protein that associates with Skp1 and Cdc53 to form SCF; promotes G1 ⁄ S transition by targeting G1 (Cln) cyclins and Sic1 for degradation Cdc53 Cullin that associates with Cdc4 and Cdc53 to form SCF; promotes G1 ⁄ S transition by targeting G1 (Cln) cyclins and Sic1 for degradation Cdh1 Cell cycle-regulated activator of APC; promotes mitotic exit by directing the ubiquitination of cyclins Cdk1 Catalytic subunit of the main cell cycle cyclin-dependent kinase; associates with G1 (Cln) and mitotic (Clb) cyclins Clb1, Clb2 B-type cyclins that activate Cdk1 to promote G2 ⁄ M transition Clb3, Clb4 B-type cyclins that activate Cdk1 to promote G2 ⁄ M transition; involved in spindle assembly Clb5, Clb6 B-type cyclins that activate Cdk1 to promote DNA replication initiation during the S phase; involved in spindle assembly Cln1, Cln2 G1 cyclins that activate Cdk1 to promote G1 ⁄ S transition Cln3 G1 cyclin that activates Cdk1 to promote G1 ⁄ S transition; regulates the transcription of Cln1 and Cln2 CK2 Casein kinase 2; serine ⁄ threonine kinase with roles in cell growth and proliferation; phosphorylates many substrates including transcription factors and RNA polymerases Dbf2 Serine ⁄ threonine kinase involved in transcription and stress response; activated during exit from mitosis Dcr2 Phosphatase; dosage-dependent positive regulator of G1 ⁄ S transition Fkh2 Forkhead transcription factor required for the expression of genes at the G2 ⁄ M transition; positively regulates transcriptional elongation and negatively regulates chromatin silencing Gis2 Translational activator for mRNAs with internal ribosome entry sites; associates with polysomes and binds to a specific subset of mRNAs Hek2 RNA binding protein that represses translation of ASH1 mRNA and regulates its asymmetric localization Hog1 Mitogen-activated protein kinase involved in osmoregulation; mediates recruitment and activation of RNA polymerase II Ime2 Serine ⁄ threonine kinase involved in the timely destruction of Sic1 during sporulation and in activation of meiosis Nab2 Nuclear polyadenylated RNA-binding protein required for nuclear mRNA export and poly(A) tail length control; autoregulates mRNA levels Pcl1, Pcl2 Cyclins that interact with the cyclin-dependent kinase Pho85; regulate polarized growth, morphogenesis and cell cycle progression; localize to sites of polarized cell growth Pcl9 Cyclin that interacts with the cyclin-dependent kinase Pho85; activated by Swi5 Pho4 Basic helix-loop-helix transcription factor that activates transcription in response to phosphate limitation; regulated by phosphorylation at multiple sites and by phosphate availability Pho80 Cyclin that interacts with the cyclin-dependent kinase Pho85; regulates the cellular response to nutrient levels such as phosphate limitation Pho85 Cyclin-dependent kinase that regulates the cellular response to nutrient levels and cell cycle progression Rad23 Polyubiquitin-binding protein involved in nucleotide excision repair Rpn10 Polyubiquitin-binding proteins; non-ATPase base subunit of the 19S regulatory particle of the 26S proteasome Rub1 Ubiquitin-like protein; conjugation substrates include Cdc53 cullin Sic1 Inhibitor of Cdk1 ⁄ Clb complexes that control G1 ⁄ S and M ⁄ G1 transitions; prevents a premature S phase and ensures genomic integrity Skp1 Kinetochore protein and subunit of SCF ubiquitin ligase complex (E3 enzyme) Swi5 Transcription factor that activates the transcription of genes expressed at the M ⁄ G1 transition and in the G1 phase; its nuclear localization in the G1 phase is regulated by Cdk1-mediated phosphorylation Yrb1 Ran GTPase binding protein involved in nuclear protein import, RNA export and ubiquitin-mediated protein degradation during cell cycle progression Regulation of timely Clb cyclin waves by Sic1
Frontiers in Physiology, 2013
The Forkhead (Fkh) box family of transcription factors is evolutionary conserved from yeast to hi... more The Forkhead (Fkh) box family of transcription factors is evolutionary conserved from yeast to higher eukaryotes and its members are involved in many physiological processes including metabolism, DNA repair, cell cycle, stress resistance, apoptosis, and aging. In budding yeast, four Fkh transcription factors were identified, namely Fkh1, Fkh2, Fhl1, and Hcm1, which are implicated in chromatin silencing, cell cycle regulation, and stress response. These factors impinge transcriptional regulation during cell cycle progression, and histone deacetylases (HDACs) play an essential role in this process, e.g., the nuclear localization of Hcm1 depends on Sir2 activity, whereas Sin3/Rpd3 silence cell cycle specific gene transcription in G2/M phase. However, a direct involvement of Sir2 in Fkh1/Fkh2-dependent regulation of target genes is at present unknown. Here, we show that Fkh1 and Fkh2 associate with Sir2 in G1 and M phase, and that Fkh1/Fkh2-mediated activation of reporter genes is antagonized by Sir2. We further report that Sir2 overexpression strongly affects cell growth in an Fkh1/Fkh2-dependent manner. In addition, Sir2 regulates the expression of the mitotic cyclin Clb2 through Fkh1/Fkh2-mediated binding to the CLB2 promoter in G1 and M phase. We finally demonstrate that Sir2 is also enriched at the CLB2 promoter under stress conditions, and that the nuclear localization of Sir2 is dependent on Fkh1 and Fkh2. Taken together, our results show a functional interplay between Fkh1/Fkh2 and Sir2 suggesting a novel mechanism of cell cycle repression. Thus, in budding yeast, not only the regulation of G2/M gene expression but also the protective response against stress could be directly coordinated by Fkh1 and Fkh2.
PLOS ONE, Apr 27, 2010
Background: DNA replication begins at specific locations called replication origins, where helica... more Background: DNA replication begins at specific locations called replication origins, where helicase and polymerase act in concert to unwind and process the single DNA filaments. The sites of active DNA synthesis are called replication forks. The density of initiation events is low when replication forks travel fast, and is high when forks travel slowly. Despite the potential involvement of epigenetic factors, transcriptional regulation and nucleotide availability, the causes of differences in replication times during DNA synthesis have not been established satisfactorily, yet. Methodology/Principal Findings: Here, we aimed at quantifying to which extent sequence properties contribute to the DNA replication time in budding yeast. We interpreted the movement of the replication machinery along the DNA template as a directed random walk, decomposing influences on DNA replication time into sequence-specific and sequenceindependent components. We found that for a large part of the genome the elongation time can be well described by a global average replication rate, thus by a single parameter. However, we also showed that there are regions within the genomic landscape of budding yeast with highly specific replication rates, which cannot be explained by global properties of the replication machinery. Conclusion/Significance: Computational models of DNA replication in budding yeast that can predict replication dynamics have rarely been developed yet. We show here that even beyond the level of initiation there are effects governing the replication time that can not be explained by the movement of the polymerase along the DNA template alone. This allows us to characterize genomic regions with significantly altered elongation characteristics, independent of initiation times or sequence composition.
Current Opinion in Systems Biology, Mar 1, 2021
Abstract Biological circuits are responsible for transitions between cellular states in a timely ... more Abstract Biological circuits are responsible for transitions between cellular states in a timely fashion. For example, stem cells switch from an undifferentiated (unstable) state to a differentiated (stable) state. Conversely, cell cycle and circadian clocks are completed through transitions among successive (stable) states, i.e. waves, with (unstable) states switching them at definite timing. These transitions irreversibly determine the biological response or fate of a cell, to commit to reversible switches or to generate periodic oscillations of its state. Here we review synthetic circuits that, in silico and in vivo, allow a cell to 'make a decision', i.e. to select which state to reach, among multiple ones available, through definite network designs. Specifically, we propose and discuss the designs, and their constituents motifs, which we consider to be more prone to reprogram cell behaviour, and whose parameters can be fine-tuned through systems biology and tested experimentally through Synthetic Biology. For these designs, exploration of the parameter space and of the influence of (external) cellular signals – which modulate circuit parameters – allows for the prediction of the circuit's response and its consequent impact on cell fate.
Drug Discovery Today: Technologies, Aug 1, 2015
A pharmacology that hits single disease-causing molecules with a single drug passively distributi... more A pharmacology that hits single disease-causing molecules with a single drug passively distributing to the target tissue, was almost ready. Such a pharmacology is not (going to be) effective however: a great many diseases are systems biology diseases; complex networks of some hundred thousand types of molecule, determine the functions that constitute human health, through nonlinear interactions. Malfunctions are caused by a variety of molecular failures at the same time; rarely the same variety in different individuals; in complex constellations of OR and AND logics. Few molecules cause disease single-handedly and few drugs will cure the disease all by themselves when dosed for a limited amount of time. We here discuss the implications that this discovery of the network nature of disease should have for pharmacology. We suggest ways in which pharmacokinetics, pharmacodynamics, but also systems biology and genomics may have to change so as better to deal with systems-biology diseases.
Elsevier eBooks, 2014
Systems Biology brings the potential to discover fundamental principles of Life that cannot be di... more Systems Biology brings the potential to discover fundamental principles of Life that cannot be discovered by considering individual molecules. This chapter discusses a number of early, more recent, and upcoming discoveries of such network principles. These range from the balancing of fluxes through metabolic networks, the potential of those networks for truly individualized medicine, the time dependent control of fluxes and concentrations in metabolism and signal transduction, the ways in which organisms appear to regulate metabolic processes vis-a-vis limitations therein, tradeoffs in robustness and fragility, and a relation between robustness and time dependences in the cell cycle. The robustness considerations will lead to the issue whether and how evolution has been able to put in place design principles of control engineering such as infinite robustness and perfect adaptation in the hierarchical biochemical networks of cell biology.
Frontiers in Immunology, Aug 7, 2018
Many a disease associates with inflammation. Upon binding of antigen-antibody complexes to immuno... more Many a disease associates with inflammation. Upon binding of antigen-antibody complexes to immunoglobulin-like receptors, mast cells release tumor necrosis factor-α and proteases, causing fibroblasts to release endogenous antigens that may be cross reactive with exogenous antigens. We made a predictive dynamic map of the corresponding extracellular network. In silico, this map cleared bacterial infections, via acute inflammation, but could also cause chronic inflammation. In the calculations, limited inflammation flipped to strong inflammation when cross-reacting antigen exceeded an "On threshold." Subsequent reduction of the antigen load to below this "On threshold" did not remove the strong inflammation phenotype unless the antigen load dropped below a much lower and subtler "Off" threshold. In between both thresholds, the network appeared caught either in a "low" or a "high" inflammatory state. This was not simply a matter of bi-stability, however, the transition to the "high" state was temporarily revertible but ultimately irreversible: removing antigen after high exposure reduced the inflammatory phenotype back to "low" levels but if then the antigen dosage was increased only a little, the high inflammation state was already re-attained. This property may explain why the high inflammation state is indeed "chronic," whereas only the naive low-inflammation state is "acute." The model demonstrates that therapies of chronic inflammation such as with anti-IgLC should require fibroblast implantation (or corresponding stem cell activation) for permanence in order to redress the irreversible transition.
Proceedings of the National Academy of Sciences of the United States of America, Mar 6, 2017
Journal of Biotechnology, Nov 1, 2010
Current Genomics, May 1, 2010
Similarly to metazoans, the budding yeast Saccharomyces cereviasiae replicates its genome with a ... more Similarly to metazoans, the budding yeast Saccharomyces cereviasiae replicates its genome with a defined timing. In this organism, well-defined, site-specific origins, are efficient and fire in almost every round of DNA replication. However, this strategy is neither conserved in the fission yeast Saccharomyces pombe, nor in Xenopus or Drosophila embryos, nor in higher eukaryotes, in which DNA replication initiates asynchronously throughout S phase at random sites. Temporal and spatial controls can contribute to the timing of replication such as Cdk activity, origin localization, epigenetic status or gene expression. However, a debate is going on to answer the question how individual origins are selected to fire in budding yeast. Two opposing theories were proposed: the "replicon paradigm" or "temporal program" vs. the "stochastic firing". Recent data support the temporal regulation of origin activation, clustering origins into temporal blocks of early and late replication. Contrarily, strong evidences suggest that stochastic processes acting on origins can generate the observed kinetics of replication without requiring a temporal order. In mammalian cells, a spatiotemporal model that accounts for a partially deterministic and partially stochastic order of DNA replication has been proposed. Is this strategy the solution to reconcile the conundrum of having both organized replication timing and stochastic origin firing also for budding yeast? In this review we discuss this possibility in the light of our recent study on the origin activation, suggesting that there might be a stochastic component in the temporal activation of the replication origins, especially under perturbed conditions.
Frontiers in Physiology, Aug 2, 2018
Triggering an appropriate protective response against invading agents is crucial to the effective... more Triggering an appropriate protective response against invading agents is crucial to the effectiveness of human innate and adaptive immunity. Pathogen recognition and elimination requires integration of a myriad of signals from many different immune cells. For example, T cell functioning is not qualitatively, but quantitatively determined by cellular and humoral signals. Tipping the balance of signals, such that one of these is favored or gains advantage on another one, may impact the plasticity of T cells. This may lead to switching their phenotypes and, ultimately, modulating the balance between proliferating and memory T cells to sustain an appropriate immune response. We hypothesize that, similar to other intracellular processes such as the cell cycle, the process of T cell differentiation is the result of: (i) pleiotropy (pattern) and (ii) magnitude (dosage/concentration) of input signals, as well as (iii) their timing and duration. That is, a flexible, yet robust immune response upon recognition of the pathogen may result from the integration of signals at the right dosage and timing. To investigate and understand how system's properties such as T cell plasticity and T cell-mediated robust response arise from the interplay between these signals, the use of experimental toolboxes that modulate immune proteins may be explored. Currently available methodologies to engineer T cells and a recently devised strategy to measure protein dosage may be employed to precisely determine, for example, the expression of transcription factors responsible for T cell differentiation into various subtypes. Thus, the immune response may be systematically investigated quantitatively. Here, we provide a perspective of how pattern, dosage and timing of specific signals, called interleukins, may influence T cell activation and differentiation during the course of the immune response. We further propose that interleukins alone cannot explain the phenotype variability observed in T cells. Specifically, we provide evidence that the dosage of intercellular components of both the immune system and the cell cycle regulating cell proliferation may contribute to T cell activation, differentiation, as well as T cell memory formation and maintenance. Altogether, we envision that a qualitative (pattern) and quantitative (dosage) crosstalk between the extracellular milieu and intracellular proteins leads to T cell plasticity and robustness. The understanding of this complex interplay is crucial to predict and prevent scenarios where tipping the balance of signals may be compromised, such as in autoimmunity.
ImmunoInformatics, Jul 1, 2023
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Papers by Matteo Barberis