Computational Biology

Theoretical methods and mathematical modeling can be applied to investigate complex physical systems that are found in biological organisms. Computer simulations are a common tool to analyze the interactions of biological structures at the molecular level, or the connections among different subsystems in large-scale networks, which are crucial to understand the basic mechanisms in the proteomics and metabolomics fields


Ligand binding. Receptor-ligand interactions are responsible of molecular recognition, binding, transport and release. These mechanisms are fundamental in many processes that are vital in any living organism, and can be exploited in several applications of nanotechnology. Molecular dynamics simulations and docking calculations are theoretical tools that allow us to determine a model of interaction between a receptor and a variety of ligands. These computational techniques are useful to predict structural and dynamic properties that determine the functionality of a molecular complex between transport proteins and small compounds, such as ligands of pharmaceutical interest.


Protein folding. Proteins are molecular ‘nanomachines’ that acquire their tridimensional structure through a spontaneous process of folding. This phenomenon is central for some of the basic mechanisms of life. Furthermore, their competitive processes (unfolding and misfolding) lead to a loss of function and are correlated to some serious pathologies. Protein folding is an intrinsically complex molecular process that needs to be investigated with a combined theoretical and experimental approach. Molecular dynamics simulations and other computational methods are able to reveal crucial details of the folding reaction.


Regulatory RNA. Overcoming the decades’ old view according to which they are mere intermediates between DNA and proteins in the central dogma of molecular biology, RNAs are increasingly recognized for the remarkable variety of functional roles they play in eukaryotic gene expression. Non-coding RNAs (ncRNAs), in particular, are deeply involved in developmental programs and disease, to the point of having become targets of novel therapeutic approaches that aim at either inhibiting or enhancing their functionality. Importantly, most ncRNAs act by protein-mediated binding to other nucleic acids, leading to complex inter-dependencies be- tween coding and ncRNAs, RNA-binding proteins and DNA. Their central role in regulation is perhaps best exemplified by microRNAs (miRNAs), small ncRNAs of 20–25 nucleotides (nt) that mediate post-transcriptional regulation (PTR) via gene expression silencing in animals. miRNAs are believed to affect the expression of about two-thirds of protein-coding genes in humans. Their apparent ubiquity, together with the strong topological heterogeneities that characterize the PTR network that maps out the known interactions between miRNAs and other RNA species (such as mRNAs), has lead to the idea that competition for shared miRNAs can cause an effective positive interaction between different transcripts, currently referred to as the ‘ceRNA effect’. In this context, we are interested in quantifying the role and effectiveness of miRNA-mediated RNA cross-talk, with the goal of clarifying (i) under which conditions it outperforms other regulatory mechanisms, for instance in processing gene expression noise; (b) whether it can carry a significant systemic role; and (c) its relevance in specific biological cases of differentiation and disease.


Cell growth and its biosynthetic costs. The coupling between the physiology of cell growth and cellular composition has been actively investigated since the 1940s. In exponentially growing bacteria, such interdependence is best expressed in a quantitative way by the bacterial ‘growth laws’ that directly relate the protein, DNA and RNA content of a cell to the growth rate. Many such laws have been experimentally characterized and many more are currently being probed at increasingly high resolution. The emerging scenario suggests that proteome organization in bacteria is actively regulated in response to the growth conditions. Several phenomenological models explain the origin of different growth laws at coarse-grained levels. By contrast, genome-scale approaches probing such relationships at the molecular level are far less developed. We have developed a mathematical modeling scheme called Constrained Allocation Flux Balance Analysis or CAFBA, in which the costs of gene expression are accounted for effectively through a single global constraint on metabolic fluxes, encodeing for the relative adjustment of proteome sectors at different growth rates. Using bacteria as the initial model organisms, we are interested in quantifying the trade-off between cell growth and its associated biosynthetic costs, generating testable predictions about the way in which the usage of metabolic pathways and protein expression levels are modulated by the growth conditions.


Cell-to-cell variability in exponentially growing bacteria. Current experimental techniques (see e.g. the ‘mother machine’) can probe physiological variability by characterizing e.g. growth rate distributions for bacterial populations at single cell resolution. These distributions reflect noise at various levels, from intracellular stochasticity in gene expression and metabolite levels to fluctuations in the extracellular medium. However, upon controlling the latter, they provide a window to analyze the role of noise in the genotype-phenotype relationship. Straightforward sam- pling of the feasible space predicted by mathematical models, however, does not explain the observed statistics. We are therefore interested in identifying a physical or biological principle that drives the selection of observed growth states and hence shed light on the origin of the observed phenotypic diversity.



De Martino

CNR Researcher



Full Professor



CNR Researcher

Facilities and Labs

S.Li.M. Lab @ Roma


  1. M Mori et al. Constrained Allocation Flux Balance Analysis, PLOS Comp Biol 12:e1004913 (2016). DOI:10.1371/journal.pcbi.1004913
  2. D De Martino et al, Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli, Phys Biol 13:036005 (2016). DOI:1088/1478-3975/13/3/036005
  3. S Grigolon et al, Noise Processing by MicroRNA-Mediated Circuits: the Incoherent Feed-Forward Loop, Revisited, Heliyon 2:e00095 (2016). DOI:  1016/j.heliyon.2016.e00095
  4. Martirosyan A et al, Probing the Limits to MicroRNA-Mediated Control of Gene Expression, PLOS Comp Biol 12(1): e1004715 (2016). DOI: 10.1371/journal.pcbi.1004715
  5. Evoli, L. Mobley, R. Guzzi, B. Rizzuti, Multiple   binding modes of ibuprofen in human serum albumin identified by absolute binding free energy calculations,    bioRxiv, 8, 1-27, (2016) doi:10.1101/068502
  6. Neira, B. Rizzuti, J. L. Iovanna, Determinants of the pKa values of ionizable residues in an intrinsically disordered protein, Archives of Biochemistry and Biophysics, 595, 1-16, (2016) doi: 10.1016/
  7. Capuani F et al, Quantitative constraint based computational model of tumor-to-stroma coupling via lactate shuttle, Sci Rep 5:11880 (2015). DOI: 10.1038/srep11880
  8. Rizzuti, R. Bartucci, L. Sportelli, R. Guzzi, Fatty acid binding into the highest affinity site of human serum albumin observed in molecular dynamics simulation, Archives of Biochemistry and Biophysics, 579, 18-25, (2015) doi: 1016/
  9. Evoli, R. Guzzi, B. Rizzuti, Molecular simulations of ß-lactoglobulin complexed with fatty acids reveal the structural basis of ligand affinity to internal and possible external binding sites, Proteins: Structure, Function, and Bioinformatics, 82, 2609-2619, (2014)     doi: 1002/prot.24625
  10. Pantusa, R. Bartucci, B. Rizzuti, Stability of trans-resveratrol associated with transport proteins, Journal of Agricultural and Food Chemistry, 62, 4384-4391, (2014) doi: 1021/jf405584a
  11. D De Martino et al. Inferring metabolic phenotypes from the exometabolome through a thermodynamic variational principle. New J Phys 16: 115018 (2014). DOI:  1088/1367-2630/16/11/115018
  12. M Figliuzzi et al, RNA based regulation: dynamics and response to perturbations of competing RNAs. Biophys J 107:1011 (2014). DOI: 1016/j.bpj.2014.06.035
  13. A De Martino et al, Identifying all moiety conservation laws in genome-scale metabolic networks. PLOS ONE 9:e100750 (2014). DOI: 10.1371/journal.pone.0100750
  14. A Seganti et al. Searching for feasible stationary states in reaction networks by solving a Boolean constraint satisfaction problem. Phys Rev E 89:022139 (2014). DOI: 1103/PhysRevE.89.022139

Other Selected Publications:

  1. FA Massucci et al, Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective. BMC Sys Biol 7:103 (2013). DOI:   1186/1752-0509-7-103
  2. M Figliuzzi et al, MicroRNAs as a selective channel of communication between competing RNAs, Biophys J 104:1203 (2013). DOI: 1016/j.bpj.2013.01.012
  3. A Seganti et al. Boolean constraint satisfaction problems for reaction networks, J Stat Mech P09009 (2013). DOI: 10.1088/1742-5468/2013/09/P09009
  4. D De Martino et al. Counting and correcting thermodynamically infeasible flux cycles in genome-scale metabolic networks, Metabolites 3:946 (2013). DOI: 3390/metabo3040946
  5. FA Massucci et al. A novel methodology to estimate metabolic flux distributions in constraint-based models, Metabolites 3:838 (2013). DOI: 3390/metabo3030838
  6. Evoli, Guzzi, B. Rizzuti, Dynamics and unfolding pathway of chimeric azurin variants: insights from molecular dynamics simulation, Journal of Biological Inorganic Chemistry, 18, 739-749, (2013) doi: 10.1007/s00775-013-1017-1
  7. Rizzuti, V. Daggett, Using simulations to provide the framework for experimental protein folding studies, Archives of Biochemistry and Biophysics 531, 128-135, (2013)   doi: 1016/
  8. Guzzi, Rizzuti, R. Bartucci, Dynamics and binding affinity of spin-labelled stearic acids in ß-lactoglobulin: evidences from EPR spectroscopy and molecular dynamics simulation, Journal of Physical Chemistry B, 116, 11608-11615 (2012) doi: 10.1021/jp3074392
  9. A De Martino, D De Martino, R Mulet and G Uguzzoni. Reaction networks as systems for resource allocation: a variational principle for non-equilibrium steady states. PLoS ONE 7:e39849 (2012). DOI: 1371/journal.pone.0039849
  10. D De Martino, M Figliuzzi, A De Martino and E Marinari. A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks. PLoS Comp Biol 8:e1002562 (2012). DOI: 1371/journal.pcbi.1002562

Latest News

La settimana del rosa digitale - 4^ed

La settimana del rosa digitale - 4^ed


Percorso di condivisione della carriera di scienziato-donna fatto attraverso esperimenti di estrazione di sostanze chimiche partendo dal cibo.

11 e 15 marzo 2019

Via Marconi,39 - Casamassima Bari 70010

Che “cavolo" di arcobaleno-mamme e scienza un viaggio alla scoperta di cio’ che Madre Natura ci insegna.

con Eloisa Sardella (CNR Nanotec) e Laura Rosso (PSP)

maggiori info:

TERAMETANANO - International Conference on Terahertz Emission, Metamaterials and Nanophotonics


Castello Carlo V, Lecce 27 -31 Maggio 2019

The IV edition of TERAMETANANO, the International Conference on Terahertz Emission, Metamaterials and Nanophotonics, will take place in Lecce (Italy) from 27 to 31 of May 2019 in the 16th-century Castle of Charles V   with two special nights that will be held in an original Theatre of Roman period.


TERAMETANANO is an annual conference that gather physicists studying a wide variety of phenomena in the areas of nano-structuresnano-photonics and meta-materials, with special attention to the coupling between light and matter and in a broad range of wavelengths, going from the visible up to the terahertz.


Al via la fase 2 del Tecnopolo per la medicina di precisione

Firmata convenzione tra Regione, Università e Cnr per avvio seconda fase del Tecnopolo

Bari, 27 novembre 2018 

Sottoscritto stamane l’accordo tra Regione PugliaCnr Consiglio nazionale delle ricerche, Irccs Giovanni Paolo II di Bari e Università di Bari per l’avvio della seconda fase del Tecnopolo per la Medicina di Precisione. Sede del tecnopolo, il CnrNanotec.

“La sfida della medicina moderna è tradurre nella pratica clinica gli enormi progressi compiuti dalla scienza e dalla tecnologia. In questo contesto le nanotecnologie, focalizzate sull’indagine e sulla manipolazione della materia a livello nanometrico-molecolare, si presentano come uno strumento potentissimo al servizio della medicina di precisione, la nuova frontiera che punta allo sviluppo di trattamenti personalizzati per il singolo paziente”, afferma  Giuseppe Gigli, direttore di Cnr Nanotec e coordinatore del Tecnopolo.

Link video dichiarazione Massimo Inguscio:

Link video di presentazione Tecnomed:

Link video dichiarazione Michele Emiliano: