Languages for Systems Biology Luca Cardelli Microsoft Research Cambridge UK http://www.luca.demon.co.uk/BioComputing.htm http://research.microsoft.com/bioinfo Languages for Systems Biology

50 Years of Molecular Cell Biology Genes are made of DNA Store digital information as sequences of 4 different nucleotides Direct protein assembly through RNA and the Genetic Code Proteins (>10000) are made of amino acids Process signals Activate genes Move materials Catalyze reactions to produce substances Control energy production and consumption Bootstrapping still a mystery DNA, RNA, proteins, membranes are today interdependent. Not clear who came first Separation of tasks happened a long time ago Not understood, not essential 11/23/2018

Towards Systems Biology Biologists now understand many of the cellular components A whole team of biologists will typically study a single protein for years When each component and each reaction is understood, the system is understood (?) But this has not led to understand how “the system” works Behavior comes from complex chains of interactions between components Predictive biology and pharmacology still rare Synthetic biology still unreliable New approach: try to understand “the system” Experimentally: massive data gathering and data mining (e.g. Genome projects) Conceptually: modeling and analyzing networks (i.e. interactions) of components What kind of a system? Just beyond the basic chemistry of energy and materials processing… Built right out of digital information (DNA) Based on information processing for both survival and evolution Can we fix it when it breaks? Really becomes: How is information structured and processed? 11/23/2018

Size DVD Pentium II Gate E. Coli 1 micron 1 micron  = 0.25 micron in Pentium II Pentium II Gate E. Coli 1 micron 11/23/2018

Performance Pentium II E. Coli DVD ~3 million transistors ~1/4 megabyte of memory ~100 million operations per second ~1 million macromolecules ~1 megabyte of static genetic memory ~1 million amino-acids per second 4700 megabytes of memory 1.385 megabytes per second Comparison courtesy of Eric Winfree 11/23/2018

Aims Modeling biological systems. By adapting paradigms and techniques developed for modeling information-processing systems. Because they have some similar features: Deep layering of abstractions. Complex composition of simpler components. Discrete (non-linear) evolution. Digital coding of information. Reactive information-driven behavior. Very high degree of concurrency. “Emergent behavior” (not obvious from part list). 11/23/2018

EU Commission, Health Research Report on Computational Systems Biology General Modelling Requirements Research projects should focus on integrated modelling of several cellular processes leading to as complete an understanding as possible of the dynamic behaviour of a cell. Several projects may be required to develop modules (metabolism, signalling, trafficking, organelles, cell cycle, gene expression, replication, cytoskeleton) in model organisms. This modelling should involve realistic analysis of experimental data, including a wide range of data for transcriptomics, proteomics and functional genomics, and interactions with cellular pathways including signal transduction, regulatory cascades, metabolic pathways etc. It should involve: Coherent, high-quality, quantitative, heterogeneous and dynamic data sets as a basis for novel model constructions to advance from analytical to predictive modelling. Experimental functional analysis tools (in-situ proteomics, protein-protein interactions, metabolic fluxes, etc) 11/23/2018

Methods Applying techniques unique to Computing: Model Construction (writing things down precisely) Studying the notations used in systems biology. Devising formal languages to reflect them. Studying their dynamics (semantics). Model Validation (using models for postdiction and prediction) Stochastic Simulation Stochastic = Quantitative concurrent semantics. Based on compositional descriptions. “Program” Analysis Control flow analysis Causality analysis Modelchecking Standard, Quantitative, Probabilistic 11/23/2018

Structural Architecture Eukaryotic Cell (10~100 trillion in human body) Nuclear membrane Mitochondria Membranes everywhere Golgi Vesicles E.R. Plasma membrane (<10% of all membranes) 11/23/2018

Functional Architecture The Abstract Machines of Systems Biology Regulation Gene Machine The “hardware” (biochemistry) is fairly well understood. But what is the “software” that runs on these machines? Notations already used in Biology Nucleotides Biochemical toolkits Makes proteins, where/when/howmuch Holds genome(s), confines regulators Signals conditions and events Directs membrane construction and protein embedding Model Integration Different time and space scales P Q Holds receptors, actuators hosts reactions Protein Machine Machine Membrane Implements fusion, fission Aminoacids Phospholipids Phospholipids Metabolism, Propulsion Signal Processing Molecular Transport Confinement Storage Bulk Transport 11/23/2018

1: The Protein Machine Pretty close to the atoms. On/Off switches cf. BioCalculus [Kitano&Nagasaki], k-calculus [Danos&Laneve] On/Off switches Each protein has a structure of binary switches and binding sites. But not all may be always accessible. Inaccessible Protein Inaccessible Binding Sites Switching of accessible switches. - May cause other switches and binding sites to become (in)accessible. - May be triggered or inhibited by nearby specific proteins in specific states. Binding on accessible sites. May cause other switches and binding sites to become (in)accessible. - May be triggered or inhibited by nearby specific proteins in specific states. 11/23/2018

Molecular Interaction Maps http://www.cds.caltech.edu/~hsauro/index.htm The p53-Mdm2 and DNA Repair Regulatory Network JDesigner Taken from Kurt W. Kohn 11/23/2018

“External Choice” The phage lambda switch 2. The Gene Machine Pretty far from the atoms. cf. Hybrid Petri Nets [Matsuno, Doi, Nagasaki, Miyano] Positive Regulation Negative Regulation Transcription Input Output1 Output2 Input Output Coding region Gene (Stretch of DNA) “External Choice” The phage lambda switch Regulatory region Regulation of a gene (positive and negative) influences transcription. The regulatory region has precise DNA sequences, but not meant for coding proteins: meant for binding regulators. Transcription produces molecules (RNA or, through RNA, proteins) that bind to regulatory region of other genes (or that are end-products). Human (and mammalian) Genome Size 3Gbp (Giga base pairs) 750MB @ 4bp/Byte (CD) Non-repetitive: 1Gbp 250MB In genes: 320Mbp 80MB Coding: 160Mbp 40MB Protein-coding genes: 30,000-40,000 M.Genitalium (smallest true organism) 580,073bp 145KB (eBook) E.Coli (bacteria): 4Mbp 1MB (floppy) Yeast (eukarya): 12Mbp 3MB (MP3 song) Wheat 17Gbp 4.25GB (DVD) 11/23/2018

Gene Regulatory Networks http://strc.herts.ac.uk/bio/maria/NetBuilder/ Taken from Eric H Davidson Or And Gate Amplify Sum DNA Begin coding region NetBuilder 11/23/2018

3. The Membrane Machine P Q P Q P Q P Q Very far from the atoms. Mate Mito Fusion Fission P Q Exo P Q P Q Endo Fusion Fission 11/23/2018

Membrane Transport Algorithms LDL-Cholesterol Degradation Protein Production and Secretion Viral Replication Taken from MCB p.730 11/23/2018

Process Calculi Today we represent, store, and analyze: Gene sequence data Protein structure data Metabolic network data … In the long run, how can we represent, store, and analyze biological processes? We want to do better than informal “circuit diagrams”, or huge list of chemical reactions. Scalable, precise, dynamic, highly structured, maintainable representations for systems biology. Process Calculi General formal framework for the description and analysis of highly concurrent interacting processes. 11/23/2018

Conclusions Identifying the architecture Modeling the system Emphasis on architecture, not components Modeling the system Information-oriented language-based models Analyzing the model Exploiting techniques unique to computing Perturbing, predicting, engineering “The data are accumulating and the computers are humming, what we are lacking are the words, the grammar and the syntax of a new language…” D. Bray (TIBS 22(9):325-326, 1997) “Although the road ahead is long and winding, it leads to a future where biology and medicine are transformed into precision engineering.” Hiroaki Kitano. 11/23/2018