Gender	Male
E-mail	azi.lipshtat@mssm.edu
Education and Training	B.Sc., Hebrew University of Jerusalem
	M.Sc., Hebrew University of Jerussalem
	Ph.D., Hebrew University of Jerusalem
	Postdoctoral Fellowship, Mount Sinai School of Medicine

Education and Training	B.Sc., Hebrew University of Jerusalem
	M.Sc., Hebrew University of Jerussalem
	Ph.D., Hebrew University of Jerusalem
	Postdoctoral Fellowship, Mount Sinai School of Medicine

Summary of Research

Algorithms for Stochastic Simulations
Many physical and biological processes are stochastic in nature. Computational models and simulations of such processes are a mathematical and computational challenge. We develop analytical and numerical methods for modeling and simulating fluctuating systems. We use the master equation formalism as well as improved Monte Carlo algorithms to make the simulations more efficient and to provide accurate results in minimum running time.

Systems Biology and Genetic Networks
Recent advances in molecular biology techniques have made possible the measurement of populations of proteins and mRNA's in simple genetic networks. Measurements of the average protein content of cells and their time dependence enabled to quantify the behavior of genetic networks. The large amount of data calls for new analysis methods. Viewing the system as a whole, rather than following each of its components separately, reveals new aspects of the network and elucidates many of its features. Traditionally, genetic networks have been modeled using rate equations,mainly under quasi steady state conditions. However, many real biological systems are away from steady state. Furthermore, many components of cells appear in low copy numbers and are therefore subjected to large fluctuations. We consider the modeling of simple genetic regulation networks using both deterministic and stochastic methods. We analyze and investigate the topology of signaling network and their unique characteristics, in order to understand the mechanisms by which a cell can control and regulate its intracellular dynamics.

Modeling of Intracellular Signaling
Quantitative modeling of signaling pathways may provide new insights and reveal new predictions, which lead to better understanding of particular systems, as well as general design principles. In this context we have modeled the signaling pathways which lead to Rap1 activation. The conditions in which transient and sustained activation can be observed have been found. Predictions of this analysis have been confirmed experimentally. Another system which was analyzed is the cell spreading and cytoskeleton growth. A set of several models have been constructed. These models (both deterministic and stochastic) explain many experimental observations regarding growth rate and cell shape under various conditions.

Lipshtat A, Biham O. Efficient simulations of gas-grain chemistry in interstellar clouds. Phys. Rev. Lett 2004; 93(170601).

Lipshtat A, Perets HB, Balaban NQ, Biham O. Modeling of negative autoregulated genetic networks in single cells. Gene: evolutionary genomics 2005; 347(265).

Reaction kinetics in a tight spot. Small 5 2005; 502.

Lipshtat A, Loinger A, Balaban NQ, Biham O. Genetic toggle switch without cooperative binding. Phys. Rev. Lett 2006; 96(188101).

Ma'ayan A, Lipshtat A, Iyengar R. Topology of resultant signaling network shaped by evolutionary pressure. Phys. Rev. E 2006; 73(061912).

Loinger A, Lipshtat A, Balaban NQ, Biham O. Stochastic Simulations of Genetic Switch Systems. Phys. Rev. E 2007; 75(021904).

Lipshtat A. An 'All Possible Steps' Approach for Accelerated Use of Gillespie's Algorithm. J. Chem. Phys 2007; 126(184103).

Lipshtat A, Purushothaman SP, Iyengar R, Ma'ayan A. Functions of Bifans in Context of Multiple Regulatory Motifs in Signaling Network. Biophysical Journal 2008; 94(2566).