| MINI W/S ON "COMPUTATIONAL
APPROACHES TO BIOLOGY"
Calculation for Systems
PEPA is a stochastic process algebra which was introduced in the
early 1990s for modelling computer and communication systems.
More recently, there has been some interest in applying PEPA, and
other stochastic process algebras, to modelling intracellular networks.
In this talk I will present some background on PEPA and the
modelling style which is adopted for systems biology modelling in
PEPA. The style is more abstract that the mapping which is
used in other stochastic process algebra models such as the
stochastic pi-calculus. This will highlight the features which we
consider to be important to capture and the analyses we wish to
access for such systems. These desirable features are the prime
motivators for Bio-PEPA, a new language tailored to modelling
biochemical reactions. Bio-PEPA will be presented in some detail
and illustrated on several biological examples.
Title: The Beta Workbench
Abstract: Systems biology aims at
the dynamic behaviour of biological
through the reconstruction of
and simulation of interaction
Many approaches include modelling
represent and analyse dynamics of
pathways. In particular, process
been recently applied to model and
We designed a new language derived
beta-binders process calculus to
biological systems, and a set of
generate, compile, simulate and
written in this language.
The talk will describe the new
language and the
Stochastic Simulation for
Abstract: This talk is an overview of the use of stochastic
simulation to evaluate models of chemically-reacting systems,
particularly those methods based on the use of Gillespie's Stochastic
Simulation Algorithm (SSA). The talk will compare with the
method of evaluation based on ordinary differential equations and
consider the strengths and weaknesses of both approaches. Recent
attempts to accelerate stochastic simulation will also be reviewed.
Title: Membrane computing. A quick
Abstract: Membrane computing is a branch of natural computing
initiated in 1998 and aiming to learn computing ideas/models from the
structrure and functioning of the living cell. Several types of
computing models - called P systems - were introduced in this
framework. Very shortly, in the compartments of a cell-like or
tissue-like membrane structure one processes multisets of symbol
objects according to reaction-like rules. Most of the classes of P
systems are Turing universal; when an enhanced parallelism is
available, NP-complete problems can be solved in polynomial time (by
a space-time trade-off, with the space created in a biologically
inspired manner). In the last years, many applications were reported,
especially in biology and bio-medicine, but also in computer science,
linguistics, economics, etc.
The talk will give the basic elements of membrane computing and will
present samples of results of the above mentioned types.
Title: Discrete Mathematical Models of Metabolism
Abstract: Metabolism is the basis of the biomolecular processes of
life. In its abstract and simplest setting a metabolic system is
constituted by a "reactor" (a cell) containing a population of
(bio)molecules of some given types, and communicating with an
environment from/to which it gets/expels matter and/or energy. In
this reactor some reactions are active which transform molecules into
other kinds of molecules, according to some stoichiometric patterns.
These reactions have to satisfy some chemical principles which can be
formulated in very general symbolic terms. Metabolic P systems,
shortly MP systems, are a special class of P systems, introduced
expressing biological metabolism in discrete terms. Their dynamics is
computed by "metabolic algorithms" which transform populations of
objects according to a "mass partition" principle. The
of MP systems are given and a some regulation mechanisms are
explained, which allow us to construct computational models from
experimental data of real metabolic processes.
Spiking neural P systems
Abstract: With inspiration from the way the neurons communicate by
means of electrical impulses of identical shapes (spikes), spikin
neural P systems were introduced. There are several classes of such
systems, most of them equivalent in power with Turing machines. The
complexity investigations in this area is at the beginning, but
already interesting results were obtained.
The talk will present the basic ideas of spiking neural P systems,
some typical results about their computing power, small universal
spiking neural P systems recent results concerning the possibility of
using these systems for solving complex problems. Several research
topics will be mentioned.
"Hybrid Systems and Systems Biology"
introduce hybrid systems and discuss some interesting
applications for them proposed to Systems Biology so far.
Then we will talk about the role hybrid systems can have within the
framework of the correspondence between stochastic process
algebras (SPA) and models based on differential equations.
We start defining a syntactic procedure translating programs written in
stochastic Concurrent Constraint Programming (sCCP, a SPA)
into a set of Ordinary Differential Equations (ODE) - as well as the
inverse procedure translating ODE's into sCCP programs. We observe then
how preserving behavior for such a translation in interesting cases,
leads naturally to the introduction of hybrid
systems in the picture
Title: Stochastic modelling
of biological systems: membrane systems
in Systems Biology
Abstract: The talk will
present an extension of membrane systems,
P-systems, introduced by
G. Paun as a bioinspired model of computation, with stochastic
features, and their application to modelling some biological
in the frame
of systems biology.
the fast-rate growing area of Systems Biology, the last decade
the increasing development of several
modelling approaches, analysis methods and
procedures for the holistic investigation of
to the porpuse of gaining a system-level
understanding of their complex
structure and dynamics.
In this area, mathematical
modelling can be broadly classified into
and continuous approaches, based on laws of mass action,
stochastic approaches, which take into consideration the discrete
of the quantity of components and the intrinsie randomness
biological phenomena. Biological systems, in particular cellular
can involve a huge number
of biochemical precesses,
amount of many molecular species can be in the
order of tens or hundreds. In these conditions,
the deterministic approaches
are pushed to their limits and cannot be always
while the stochastic methods can account for the randomness that can
emerge and dominate the global behaviour of the system.
this reason, we developed a modelling approach of discrete,
stochastic and compartmentalized type, based on membrane systems,
to describe the system components, their mutual interactions and the
noisy behaviour intrinsic in the dynamics.
Title: Agent-based Modeling and Simulation in Systems Biology platform
Abstract: The agent approach allows natural modeling and programming of
biological systems at different levels of abstractions: from the
fine-grained molecular world to cell compartments, organisms and
ecosystems. We show
how a Multi-agent System and suitable Coordination Models can be used
simulate biological processes. We present an application of Multi Agent
Systems to model and simulate the pathway of glycoysis.
We model bio-chemical reactions at molecular level. The molecules
move and react in a virtual space using space and time Coordination.
We discuss the implementation of the simulation over a GRID.
Title: Using evolutionary approaches to
study signaling pathways
Abstract: Biological pathways are the result of evolution, rather
than design. Thus, understanding how evolutionary dynamics affect
pathways is important for accomplishing a complete understanding how
these systems work. I will present a novel approach, that aims to
distill key information on pathway structure and dynamics by studying
their evolution in silico. Using simple mathematical models of
pathways and evolutionary simulations, this approach aims to
understand key system properties and the effects of evolutionary
dynamics on their emergence and diversity.
a new discipline aiming to understand the behavior
of biological systems as it results from the (non-trivial,
"emergent") interaction of biological components. In addition to
analyzing existing biological networks to understand their function,
it is also important to understand from the ground up what simple
networks of interacting components "can do". That investigation can
be carried out in abstract "artificial" frameworks, as long as the
ground rules are kept close enough to the ones of biochemistry. We
discuss some biologically inspired networks that are characterized by
simple components, but by complex interactions. Subtle and unexpected
behavior emerges even from simple circuits, and yet stable behavior
emerges too, giving some hints about what may be critical and what may
be irrelevant in the organization of biological networks.