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Highlights

A Language for Modeling And Optimizing Experimental Biological Protocols (Computation'21)
Coupled Membrane Transporters Reduce Noise (PhysRevE'20)

Maximal aggregation of polynomial dynamical systems (PNAS'17)

Morphisms of Reaction Networks that Couple Structure to Function (BMC SysBio'14)

PID Control of Biochemical Reaction Networks (CDC'19, TAC'21)

From Electric Circuits to Chemical Networks (Natural Computing'20)

Programmable chemical controllers made from DNA (Nature Nanotech'13)

The Cell Cycle Switch Computes Approximate Majority (Scientific Reports'12)

Two-Domain DNA Strand Displacement (MSCS'13)

From Electric Circuits to Chemical Networks (Natural Computing'20)

Programmable chemical controllers made from DNA (Nature Nanotech'13)

The Cell Cycle Switch Computes Approximate Majority (Scientific Reports'12)

Two-Domain DNA Strand Displacement (MSCS'13)

The goal of molecular programming is the systematic manipulation of matter at
the molecular scale, for applications in engineering + technology and biology + medicine.

Structural DNA/RNA nanotechnology currently provides the only fabrication pipeline for truly programmable molecular structures, in the form of static and dynamic nucleic acid assemblies. But through it we can organize also other forms of matter.

Structural DNA/RNA nanotechnology currently provides the only fabrication pipeline for truly programmable molecular structures, in the form of static and dynamic nucleic acid assemblies. But through it we can organize also other forms of matter.

Papers

Sequenceable Event Recorders (arXiv'21)
PID Control of Biochemical Reaction Networks (CDC'19, TAC'21)

Chemical Reaction Network Designs for Asynchronous Logic Circuits (DNA22 '16, NaCo '17)

Programming Discrete Distributions with Chemical Reaction Networks (DNA22 '16, NaCo '17)

The Formal Language and Design Principles of Autonomous DNA Walker Circuits (ACS Synthetic Biology'16)

Automated Design and Verification of Localized DNA Computation Circuits (DNA21 '15)

Programmable chemical controllers made from DNA (Nature Nanotech '13)

Two-Domain DNA Strand Displacement (MSCS'13)

Abstractions for DNA circuit design (J.R.S. Interface '12)

Design and Analysis of DNA Strand Displacement Devices using Probabilistic Model Checking (J.R.S. Interface '12)

Strand Algebras for DNA Computing (Natural Computing '11)

A Programming Language for Composable DNA Circuits (J.R.S.Interface '09)

Edited Proceedings

DNA 17
Talks

Telling
Molecules What To Do (Lisbon
Distinguished Lecture) Molecular Programming (ECOOP'14)

Computing with Molecules (DCM'12)

Two-Domain DNA Strand Displacement (DCM'10)

Strand Algebras for DNA Computing (DNA '09)

Tools

DSD
- DNA Strand Displacement Simulator
( J.R.S. Interface '13) Gener - DNA Strand Displacement Reductions ( Bioinformatics '15)

Links

Correctness of DNA encodings of chemical reaction networks DNA Computing and Molecular Programming Conferences

NYU Caltech UW Duke Oxford Aarhus Microsoft

Outreach

Inner
Life of A Cell - Movie Powering the Cell: Mitochondria - Movie

DNA Strand Displacement - Movies

Nanocrafter - Game

EteRNA - Game

An abstract machine is a
fictional information-processing device that can, in
principle, have a number of different physical
realizations (mechanical, electronic, biological, or
software). Biochemical toolkits in cellular biology
(nucleotides, amino acids, and phospholipids) can be
seen as abstract machines with appropriate sets of
states and operations, corresponding respectively to
genes, proteins, and membranes. To understand the
functioning of a cell, one must understand (at least)
how the various machines interact. This involves
considerable difficulties in modeling and simulations
because of the drastic differences in the "programming
model" of each machine, and in the time and size scales
involved.

Papers

Lineage Grammars: Describing, Simulating and Analyzing Population Dynamics.
(BMC Bioinformatics'14) An Intuitive Modelling Interface for Systems Biology (DCM'09) (IJSI'13)

Abstract Machines of Systems Biology (TCSB)

Artificial Biochemistry (Algorithmic Bioprocesses)

Can a Systems Biologist Fix a Tamagotchi? (Gilles Kahn Colloquium)

Visualization in Process Algebra Models of Biological Systems (The Fourth Paradigm)

A Compositional Approach to the Stochastic Dynamics of Gene Networks (TCSB)

Compositionality, Stochasticity and Cooperativity in Dynamic Models of Gene Regulation (HFSP ournal)

Lectures

Artificial Biochemistry - Graduate Course (Trento, May 22-26 '06)
Talks

Speaking the Language of Molecules (CDE Distinguished Lacture, U.Washington) Living Software (L'INRIA a Quarante Ans)

Artificial Biochemistry (Longo Symposium 2007)

Can a Systems Biologist Fix a Tamagotchi? (Kahn Colloquium 2007)

Abstract Machines of Systems Biology (Dagstuhl 2006)

Languages and Notations for Systems Biology (UPP'04 Invited Talk)

A Compositional Approach to the Stochastic Dynamics of Gene Networks (Concur '05)

Links

Adrian Thompson: Evolution of circuits in hardware (archive)
The basic physical operations
on membranes are local fusion (two patches merging) and
local fission (one patch splitting in two). When seen at
the global scale of whole membranes, these local
operations induce four transformations: in addition to
the obvious splitting (Mito) and merging (Mate) of
membranes, there are also operation, quite common in
reality, that cause a membrane to eat (Endo) or spit
(Exo) another subsystem. Although this is an unusual
computational model, the membrane operations supports
the execution of real algorithms, some of which occur in
nature. Some sets of operations are Turing-complete, and
can encode the other membrane operations.

Papers on

Brane Calculi

Programming Chemistry in DNA Addressable Bioreactors (Interface '14) Brane Calculi

Brane Calculi (ENTCS, CMSB'04)

Bitonal Membrane Systems (TCS)

Where Membranes Meet Complexes (BioConcur '05)

A Universality Result for a (Mem)Brane Calculus Based on Mate/Drip Operations (IJFCS)

Papers on

BioAmbients

BioAmbients:
An Abstraction for Biological Compartments
(TCS) BioAmbients

Bioware Languages (Computing Systems - A Tribute to Roger Needham)

Lectures

Membrane Interactions
(CSSB School Rovereto, April'04)
Talks

Bitonal Membrane Systems (MeCBIC'06)
WhereMembranesMeetComplexes (BioConcur'05)

Biological Systems as Reactive Systems (ECCS'05)

We introduce stochastic
extensions of process algebras endowed with structural
operational semantics expressed in terms of measure
theory. The set of processes is organised as a
measurable space by the sigma-algebra generated by
structural congruence. The structural operational
semantics associates to each process a set of measures
over the space of processes. The measures encode the
rates of the transitions from a process (state of a
system) to a measurable set of processes. We prove that
stochastic bisimulation is a congruence that extends
structural congruence. In addition to an elegant
operational semantics, our approach provides a canonic
way to define metrics on processes that measure how
similar two processes are in terms of behaviour.

Papers on

Logic

Continuous
Markovian Logic Axiomatization and Quantified Metatheory (LMCS'12) Logic

Modular Markovian Logic (ICALP'11)

Continuous Markovian Logic (CLS'11)

Papers on

Algebra

Stochastic Pi-Calculus Revisited (ICTAC'13)
Algebra

The Measurable Space of Stochastic Processes (QEST'10, Fundamenta Informaticae)

Talks

Stochastic Pi-Calculus Revisited (ICTAC'13)
Continuous Markovian Logic (CSL'11)

Modular Markovian Logic (ICALP'11)

The Measurable Space of Stochastic Processes (QEST'10)

We investigate the question of
whether basic chemical kinetics (kinetics of unar yand
binary chemical reactions), formulated as a process
algebra, is capable of general computation. In
particular, we investigate nondeterministic and
probabilistic termination problems. The answers to those
problems reveal a surprisingly rich picture of what is
decidable and undecidable in basic chemistry.

We introduce an algebra for massive concurrent systems, called reversible structures, where terms retain bits of causal dependencies that allow one to reverse computation histories. These bits permit to trace effects of interactions, but not to the point of being able to identify the precise molecule that caused an effect. The algebra and the causal hints match a particular form of DNA computation.

We introduce an algebra for massive concurrent systems, called reversible structures, where terms retain bits of causal dependencies that allow one to reverse computation histories. These bits permit to trace effects of interactions, but not to the point of being able to identify the precise molecule that caused an effect. The algebra and the causal hints match a particular form of DNA computation.

Papers on

Termination

Turing Universality of the Biochemical Ground Form (MSCS)
Termination

On the Computational Power of Biochemistry (AB'08)

Termination Problems in Chemical Kinetics (CONCUR'08)

Papers on

Reversibility

Reversibility
in Massive Concurrent Systems (SACS)
Reversibility

Reversible Structures (CMSB '11)

Talks

Termination Problems in Chemical Kinetics (CONCUR'08)
On The Computational Power of Biochemistry (AB'08)

Reversible Structures (CMSB'11)

The semantics of programming
languages is based on states and state transitions,
through which one can study for example termination,
nodeterminism, and concurrency. Adding rates to
transitions induces quantitative meanings that can be
related to stochastic and continuous dynamical systems,
and to their realizations as molecular systems.

Papers on design

A Language for Modeling And Optimizing Experimental Biological Protocols (MDPI Computation'21)
From Electric Circuits to Chemical Networks (Natural Computing'19)

Experimental Biological Protocols with Formal Semantics (CMSB'18)

Molecular Filters for Noise Reduction (Biophysical Journal'18)

Syntax-Guided Optimal Synthesis for Chemical Reaction Networks (CAV'17)

Papers on verification

and approximation

Central Limit Model Checking (TOCL'19)
and approximation

Reachability Computation for Switching Diffusions (HSCC'17)

A Stochastic Hybrid Approximation for Chemical Kinetics Based on the Linear Noise Approximation (CMSB'15, BioSystems'16)

Approximation of Probabilistic Reachability for Chemical Reaction Networks... (QEST'16)

Stochastic Analysis of Chemical Reaction Networks Using Linear Noise Approximation (CMSB'15)

Papers on morphisms

and bisimulation

Lumpability for Uncertain Continuous-Time Markov Chains.pdf (QEST'21)
and bisimulation

Exact Maximal Reduction of Stochastic Reaction Networks by Species Lumping (BioInformatics'21)

Maximal aggregation of polynomial dynamical systems (PNAS'17)

Guaranteed Error Bounds on Approximate Model Abstractions through Reachability Analysis (QEST'18)

Syntactic Markovian Bisimulation for Chemical Reaction Networks (Springer'17)

Comparing Chemical Reaction Networks: A Categorical and Algorithmic Perspective (LICS'16, TCS'17)

ERODE: A Tool for the Evaluation and Reduction of Ordinary Differential Equations (TACAS'17)

Efficient Syntax-Driven Lumping of Differential Equations (TACAS'16)

Symbolic Computation of Differential Equivalences (POPL'16)

Forward and Backward Bisimulations for Chemical Reaction Networks (CONCUR'15)

Morphisms of Reaction Networks that Couple Structure to Function (BMC Systems Biology'14)

Papers on chemistry

via process algebra

On Process
Rate Semantics (TCS'08) via process algebra

A Process Algebra Master Equation (QEST'07)

From Processes to ODEs by Chemistry (IFIP TCS'08)

A Correct Abstract Machine for the Stochastic Pi-calculus (BioConcur'04)

Efficient, Correct Simulation of Biological Processes in Stochastic Pi-calculus (CMSB'07)

A Graphical Representation for Biological Processes in Stochastic Pi-calculus (TCSB)

Processes in Space (CiE'10, TCS 2012)

Lectures

On
Process Rate Semantics
(Warsaw Lectures Extract, March'09)
Molecules as Automata - Open Lectures (Warsaw, March&May'09)

Molecules as Automata - Summer School on Natural Computation (BNC'08)

Process Rate Semantics - Computational and Systems Biology Course (Trento 2008)

Talks

Morphisms of Reaction Networks (CMSB'16)
Molecules as Automata (DNA'08, ECS'09)

From Processes to ODEs by Chemistry (TCS'08)

On Process Rate Semantics (MFPS'08)

A Graphical Representation for Stochastic ?-Calculus (BioConcur '05)

A Correct Abstract Machine for the Stochastic pi-calculus (BioConcur '04)

Processes in Space (CiE'10)

Pi in the Sky: Spatial Process Algebra for Developmental Biology (MeCBIC'09)

Posters

Stochastic Analysis of Chemical Reaction Networks Using Linear
Noise Approximation (DNA21'15)
Tools

ERODE
- SMT-based Automatic Exact Reduction of Ordinary Differential
Equations (POPL '16 Artifact
Evaluated) CRNReducer - Automatic exact reduction of Chemical Reaction Networks (CONCUR '15)

Papers

Coupled Membrane Transporters Reduce Noise (Physical Review E.'20)
At the core of the
biochemical networks that regulates the cell
cycle there is a switch that triggers an
irreversible transition from one critical
stage of cell division to the next. Its
dynamical system behavior is fairly well
understood, and could be achieved by many
different networks. But in nature, from yeast
to us, we find a specific universal structure
of the network: what is special about this
structure, beyond its required function? To
answer this question we shift our perspective
from dynamical systems to computing systems.
We ask: what does the cell cycle switch
compute, and how does it compute it?

Papers

Evolution of opposing regulatory interactions underlies the emergence of eukaryotic cell cycle checkpoints (SciRep'21)
Single molecules can operate as primitive biological sensors switches and oscillators (BMC SysBio'18)

Computing with biological switches and clocks (Natural Computing'18)

Efficient Switches in Biology and Computer Science (PLOS CompBio'17)

Noise Reduction in Complex Biological Switches (Scientific Reports'16)

The Cell Cycle Switch Computes Approximate Majority (Scientific Reports'12)

Transcriptional Regulation is a Major Controller of Cell Cycle Transition Dynamics (PLoS ONE)

Talks

The Cell Cycle Switch Computes Approximate Majority
Robert
Stewart Distinguished Lecture (Iowa State 2014)
Posters

Evolution
of Simple Systems to Complex Behaviours (King's
College PhD Symposium'16) Analyzing Efficiency of Biological Switches (SSBSS'15)

Evolution of Biological Switches (King's College PhD Symposium'15)

Organisms employ a variety of
signalling systems to sense and react to their
environment. Among the most basic, commonly found in
bacteria, lower eukaryotes and plants, are the so-called
phosphorelays. These systems transfer sensor information
(typically from the cellular membrane) very economically
through a chain of chemical reactions.

Despite their widespread use and simple architecture, we are still far from a full understanding of the signal-processing capabilities of phosphorelays.

Despite their widespread use and simple architecture, we are still far from a full understanding of the signal-processing capabilities of phosphorelays.

Papers

Unlimited
multistability and Boolean logic in microbial signaling (J.R.S. Interface) Phosphorelays provide tunable signal processing capabilities for the cell (PLOS CompBio)

Response Dynamics of Phosphorelays Suggest Their Potential Utility in Cell Signaling (J.R.S. Interface)

Major Histocompatibility
Complex (MHC) class I molecules are a key component of
the innate immune system. They continuously select a
signature of the cell contents (fragments of any protein
present) and present it to the cell surface, where it
can be inspected by cytotoxic T lymphocytes that destroy
virus-infected and cancerous cells based on such
signature. In this mechanism there is a trade-off
between speed and accuracy of presentation: we study how
various molecular components contribute to optimizing
this process.

Papers

A
Peptide Filtering Relation Quantifies MHC Class I Peptide
Optimization (PLoS Computational
Biology)
Projects

A
Stochastic ?-Calculus Models of MHC Class I Antigen Presentation
(Computational Biology MPhil Project)
Links

Selector function of MHC I molecules is determined by protein
plasticity Computational Modeling of Immune System Processes

We use stochastic pi-calculus
to represent and then simulate interactions within
protein networks. The pi-calculus approach allows a
biological system to be programmed in a scalable manner,
by allowing models of individual proteins to be directly
composed to form subsystems, which can in turn be
composed together to form larger systems. This allows
new components to be added incrementally without having
to re-wire the existing model, which also improves the
usability of models. Furthermore, the modular structure
of the biological system and the pi-calculus model are
in direct correspondence.

Papers

Computational
Modeling of the EGFR Network Elucidates Control Mechanisms
Regulating Signal Dynamics (BMC
Systems Biology) A Process Model of Rho GTP-binding Proteins (FBTC'07) (TCS)

A Process Model of Actin Polymerisation (FBTC'08) (ENTCS)

Lectures

Biochemical Systems as Reactive Systems - Bioinformatics Master Course (Cabridge '16)
Talks

Biological Networks in Stochastic Pi-Calculus (Imperial
College London 2005)