Molecular dynamics simulations present a powerful approach that may be used to gain insight into many biological systems. In most cases, simulations do not take into account any bond breaking or formation. This is obviously a problem when the behavior that we are wishing to explore requires such changes in the chemistry of the system. For example numerous processes in proteins in general, and membrane transporters in particular are triggered by protonation and deprotonation events. In my lecture I will describe how one can trigger such events in the course of the simulation thereby mimicking the biological functionality. I will focus on two systems: The E. coli Na+/H+ antiporter and the influenza A M2 H+ channel. I will presents the simple ways in which one can perform such triggering and the analysis tools that one may use to analyze the results.

In the fields of analytical and physical chemistry, medical diagnostics and biotechnology there is an increasing demand of highly selective and sensitive analytical techniques which, optimally, allow real- time label-free monitoring with easy to use, reliable, miniaturized and low cost devices. Biosensors meet many of the above features and, consequently, have gained a place in the analytical bench top as alternative or complementary methods for routine classical analysis. Different sensing technologies are being used for biosensors and, among them, piezoelectric micro-machined devices (pMMD) represent a cost-effective approach that have the potential to identify with high sensitivity various species in the gas phase as well as in the liquid phase. Moreover, pMMD can be designed to allow portability, integration capabilities, low power consumption and easy employment. Piezoelectric sensors are often collectively referred to as mass-sensitive devices since they are able to detect mass change on their surfaces. For biosensor applications, the surface of the piezoelectric device is modified with recognition elements (e.g. antibodies) that, binding specifically the target analyte, give rise to changes of the mass. pMMD are based on the propagation of acoustic waves and they are classified according to the characteristic of the generated waves (i.e. direction of particle displacement, wave-guiding mechanism, etc.). The most used devices, suitable for different applications, are: Rayleigh surface acoustic wave (SAW) devices, shear horizontal (SH) SAW devices, thin film bulk acoustic resonators (TFBAR), lateral field excited bulk acoustic resonators (LFEBAR), contour mode resonators (CMR) and Lamb wave devices.

Over the last two decades synthetic and semi-synthetic lipid compartments have been extensively used as in vitro models to investigate the reactive and interactive behaviour of cells, including their possible ancient precursors and their artificial implementations. Nevertheless, despite the great advances that these approaches have brought about in our understanding of the properties and dynamics of those supramolecular structures (e.g., liposome populations), as well as in biomedical applications (e.g., drug delivery), it is not always easy to interpret what is happening in reality, especially when such complex colloidal systems comprise chemical reactions, together with growth and division or reproduction processes.

In this context, we have developed a computational platform called ENVIRONMENT [1,2] suitable for studying the stochastic time evolution of reacting lipid compartments. This software is an improvement of a previous program that simulated the stochastic time evolution of homogeneous, fixed-volume, chemically reacting systems [3], extending it to more general conditions in which a collection of similar such systems interact and change in time. The overall simulation approach we take can be applied, in principle, to many different situations and type of systems, although our initial objective has been to provide an instrument for analysis of bottom-up and semi-synthetic attempts to construct relatively simple artificial protocells [‎4]. In particular, our approach is focused on elucidating the role of randomness [‎5] in the time behaviour of chemically reacting and self-reproducing lipid compartments, such as vesicles or micro emulsions. In fact, in compartmentalized reacting systems where the molecular population of the reactants is very low, random fluctuations due to the stochastic nature of reacting events (intrinsic stochasticity) can bring an open system towards unexpected time evolutions [‎3]. Additionally, this effect can be enlarged by the spreading of different initial concentrations of biological molecules encapsulated in lipid compartments, depending on the experimental preparation procedure (extrinsic stochasticity). In this framework, ENVIRONMENT has been designed to study the stochastic time evolution of compartmentalized lipid reacting systems by means of a general and widely- accepted Monte Carlo procedure, the Gillespie algorithm [‎6], and mimicking as closely as possible experimental conditions and preparation methods.

As it stands, the general project we have started is divided in two main lines of research. The first consists in modelling and simulating the structural properties and dynamic behaviour of lipid vesicle populations [‎2, 5], comparing them directly with real experimental data as for instance for the lipid vesicle competitions observed and described by Szostak’s and Luisi’s groups. This gives us the opportunity to test our approach and our simplifying assumptions and to estimate dynamic and structural parameters, by fitting experimental data. The second line of research explores hypothetical protocell models that keep a relatively low degree of molecular complexity. In particular, we have introduced and studied the ‘minimal lipid-peptide cell’, a prebiotic cell model where lipid vesicle dynamics is coupled with the condensation of oligo-peptides that could eventually form solute transport channels through the membrane [‎7]. Recently, we have also started analysing the feasibility of other more complex schemes, like the ‘Ribocell’ [‎9]: a hypothetical minimal cell model based on two hypothetical ribozymes: one catalyzing RNA strands replication, and the other the synthesis of amphiphilic molecules from lipid precursors [‎9-11]. In all these cases, random fluctuations can play an important role in determining the time behaviour of the studied systems[12].

REFERENCES

1. Mavelli F and Ruiz-Mirazo K (2007) Stochastic simulations of minimal self-reproducing cellular systems, Phil. Trans. R. Soc. B 362 1789-802.
2. Mavelli F and Ruiz-Mirazo K (2011) ENVIRONMENT: a computational platform to stochastically simulate reacting and self-reproducing lipid compartments, Phys. Biol. 7 036002.
3. Mavelli F and Piotto S (2006) Stochastic simulations of homogeneous chemically reacting systems. J. Mol. Struct 771 55- 64.
4. Solé RV, Munteanu A, Rodriguez-Caso C and Macía J (2007) Synthetic protocell biology from reproduction to computation Phil. Trans. R. Soc. B 362 1727-39
5. Mavelli F and Stano P (2010) Kinetic models for autopoietic chemical systems role of fluctuations in homeostatic regime, Phys Biol 7 016010
6. Gillespie DT (2007) Stochastic simulation of chemical kinetics. Ann. Rev. Phys. Chem. 58 35-55
7. Ruiz-Mirazo K, Mavelli F 2008 On the way towards ‘basic autonomous agents’ stochastic simulations of minimal lipid- peptide cells. BioSystems 91 374-87.
8. Szostak JW, Bartel DP and Luisi PL, (2001) Synthesizing life. Nature 409 387-90.
9. Mavelli F, Della Gatta P, Cassidei L and Luisi PL (2010) Could the Ribocell be a feasible proto-cell model? Orig. Life. Evol. Biosphere 40 (4) 459-464
10. Mavelli F(2010) Theoretical Approaches to Ribocell Modelling. In Luisi PL (eds) The Minimal Cell, Springer: New York.
11. Mavelli F (2012) Stochastic simulations of minimal cells: the Ribocell model. BMC Bioinformatics 13(4) S10.

Mavelli F and Ruiz-Mirazo K (2013) Theoretical conditions for the stationary reproduction of model protocells. Int. Biol. 5 324-341.

Understanding mental health is one of the ‘grand challenges’ of our age. Brain disorders are top of the World Health Organisation’s agenda due to their impact and prevalence, especially with the ageing population. Alongside well-targeted treatments and prevention programmes, technological advances can drive forward our understanding of how neuronal function and communication is affected in central nervous system (CNS) disorders. In particular, miniaturised in vitro procedures enable both greater control over the formation of simplified neuronal networks that mimic in vivo conditions and minimise the use of animal models, as well as providing the enhanced capability to test neuronal functionality in order to address neuronal network communication whilst inducing disease conditions. In recent years, an increasing number of microfluidic technologies have been developed for neuronal cell culture and manipulation, providing bespoke means to improve the control and implementation of conventional neuroscience analytical procedures in a miniaturised and automatable format1,2. Development of such systems will be a significant step forward in CNS drug discovery studies, as well as allowing the investigation of cellular and sub-cellular activity under conditions mimicking those proposed to underlie CNS disorders.

The talk will focus on a range of developed microfluidic bioassays that allow cell functionality, cell-cell communication and response to chemical compounds to be studied. Examples will be shown based on stem cells & primary cells neuronal network patterning and culture to study their functional synaptic connectivity and toxicity spread. Finally, applications of the technology for drug discovery and to study neurodegenerative disorders will be presented. All together, these examples highlight the range of possibilities available through the development of multidisciplinary research based on microfluidics.

References:

1. A. M. Taylor et al., Nat. Methods, 2005, 2, 599–605.
2. M. Shi, et al., Lab Chip, 2013, 13, 3008–3021.
3. G. Robertson et al., Integr. Biol., 2014, 6, 636-644.