arXiv subjects

Chiara Dalla Pellegrina

Publications and source records attributed to Chiara Dalla Pellegrina.

4 recordsLinked to original sources

arXiv2007

Ab initio phenomenological simulation of the growth of large tumor cell populations

In a previous paper we have introduced a phenomenological model of cell metabolism and of the cell cycle to simulate the behavior of large tumor cell populations (Chignola R and Milotti E, Phys. Biol. 2 (2005) 8-22). Here we describe a refined and extended version of the model that includes some of the complex interactions between cells and their surrounding environment. The present version takes into consideration several additional energy-consuming biochemical pathways such as protein and DNA synthesis, the tuning of extracellular pH and of the cell membrane potential. The control of the cell cycle - that was previously modeled by means of ad hoc thresholds - has been directly addressed here by considering checkpoints from proteins that act as targets for phosphorylation on multiple sites. As simulated cells grow, they can now modify the chemical composition of the surrounding environment which in turn acts as a feedback mechanism to tune cell metabolism and hence cell proliferation: in this way we obtain growth curves that match quite well those observed in vitro with human leukemia cell lines. The model is strongly constrained and returns results that can be directly compared with actual experiments, because it uses parameter values in narrow ranges estimated from experimental data, and in perspective we hope to utilize it to develop in silico studies of the growth of very large tumor cell populations (10^6 cells or more) and to support experimental research. In particular, the program is used here to make predictions on the behaviour of cells grown in a glucose-poor medium: these predictions are confirmed by experimental observation.

physics.bio-ph↗

arXiv2007

Dynamical hysteresis in multisite protein modification

Multisite protein modification is a ubiquitous mechanism utilized by cells to control protein functions. We have recently proposed a dynamical description of multisite protein modification which embodies all the essential features of the process (E. Milotti, A. Del Fabbro, C. Dalla Pellegrina, and R. Chignola, Physica A, in press), and we have used this model to analyze the stability and the time-scales of this mechanism. The same model can be used to understand how the system responds to stimuli: here we show that it displays frequency-dependent dynamical hysteresis. This behavior closely parallels -- with the due differences -- what is observed in magnetic systems. By selecting model parameters that span the known biological ranges, we find that the frequency-dependent features cover the band of the observed oscillations of molecular intracellular signals, and this suggests that this mechanism may have an important role in cellular information processing.

physics.bio-ph↗

arXiv2006

Dynamics of allosteric action in multisite protein modification

Protein functions in cells may be activated or modified by the attachment of several kinds of chemical groups. While protein phosphorylation, i.e. the attachment of a phosphoryl (PO$_3^-$) group, is the most studied form of protein modification, and is known to regulate the functions of many proteins, protein behavior can also be modified by nitrosylation, acetylation, methylation, etc. A protein can have multiple modification sites, and display some form of transition only when enough sites are modified. In a previous paper we have modeled the generic equilibrium properties of multisite protein modification (R.Chignola, C. Dalla Pellegrina, A. Del Fabbro, E.Milotti, Physica A {\bf 371}, 463 (2006)) and we have shown that it can account both for sharp, robust thresholds and for information transfer between processes with widely separated timescales. Here we use the same concepts to expand that analysis starting from a dynamical description of multisite modification: we give analytical results for the basic dynamics and numerical results in an example where the modification chain is cascaded with a Michaelis-Menten step. We modify the dynamics and analyze an example with realistic phosphorylation/dephosphorylation steps, and give numerical evidence of the independence of the allosteric effect from the details of the attachment-detachment processes. We conclude that multisite protein modification is dynamically equivalent to the classic allosteric effect.

physics.bio-ph↗

arXiv2006

Thresholds, long delays and stability from generalized allosteric effect in protein networks

Post-transductional modifications tune the functions of proteins and regulate the collective dynamics of biochemical networks that determine how cells respond to environmental signals. For example, protein phosphorylation and nitrosylation are well-known to play a pivotal role in the intracellular transduction of activation and death signals. A protein can have multiple sites where chemical groups can reversibly attach in processes such as phosphorylation or nitrosylation. A microscopic description of these processes must take into account the intrinsic probabilistic nature of the underlying reactions. We apply combinatorial considerations to standard enzyme kinetics and in this way we extend to the dynamic regime a simplified version of the traditional models on the allosteric regulation of protein functions. We link a generic modification chain to a downstream Michaelis-Menten enzymatic reaction and we demonstrate numerically that this accounts both for thresholds and long time delays in the conversion of the substrate by the enzyme. The proposed mechanism is stable and robust and the higher the number of modification sites, the greater the stability. We show that a high number of modification sites converts a fast reaction into a slow process, and the slowing down depends on the number of sites and may span many orders of magnitude; in this way multisite modification of proteins stands out as a general mechanism that allows the transfer of information from the very short time scales of enzyme reactions (milliseconds) to the long time scale of cell response (hours).

q-bio.CB↗