Why EvoSysBio? Combine the rigor from two powerful quantitative modeling traditions: Molecular Systems Biology rigorous models of molecules... in organisms Modeling Evolutionary Biology rigorous models of organisms... in ecosystems New Hypotheses in Evolutionary Systems Biology draw details from all areas of biology to help predict aspects of evolution
Evolutionary Systems Biology Defined aims to ultimately generate realistic, testable, integrated, dynamic, and interactive overviews of multi-dimensional fitness landscapes that mechanistically predict 1 fitness changes caused by transitions between potential states of individuals and/or their environments, and 2 evolutionary paths of changing populations of such individuals as they travel through state-space.
Massively Oversimplified Cartoon of an Abstract Landscape of an Incomplete Fitness Trait Incomplete Fitness Trait / Consequen6al Parameters / Height of Landscape d Hereditary trait 2 Plane Dimension 2 EvoSysBio Goal: MOCA-LIFTs transform into Realistic LIFTs link into Fitness Causality Networks
Environments, Genes and Phenotypes Govern Fitness Environmental Properties Genotypic Traits Phenotypic Traits Incomplete Fitness Traits Survival Traits Reproductive Traits Fitness A complicated causality network of IFTs Fitness Neutral Traits Unexpressed Traits
Types of Landscapes of Incomplete Fitness Traits Are Links in Fitness Causality Networks LIFT Bio Data in Context mean fitness of individuals in group (or other population statistics) individual survival, reproduction (networks of RIFTs, SIFTs tradeoffs) Realistic Incomplete Fitness Traits (SRIFT: calibrate SIFT in wet-lab) Simulated Incomplete Fitness Traits (SIFTs are entirely computational) time series of phenotypic traits (nested:... in cells in tissues in ) molecular functions network (collection of interaction rates) molecular structures collection (structures è govern functions) hereditary materials, genetic code,... (sequences è govern structures) Links Provide Mapping Methods Height = Link (causal Plane) 7: summary stats for group from all individual survival and reproduction 6: life history tradeoffs, physiology model of RIFTs, SIFTs interactions 5. SRIFT map, SRIFT hypothesis test map IFT changes: SIFTsè RIFTs 4: SIFT model computation from time series using biological intuition 3: time series traits of molecular systems biology networks 2: structure-function relationships governed by structural biology 1: folding of expressed sequences into dynamic 3D structures EvoSysBio in 10 Slides CC- BY Laurence Loewe
Simulated- Realis6c Incomplete Fitness Trait Hypothesis If the System Is Understood: Compare to Wildtype, No Need for SRIFT Mapping Realis6c Incomplete Fitness Trait (RIFT, observed in wet- lab) Simulated Incomplete Fitness Trait (SIFT) EvoSysBio in 10 Slides CC- BY Laurence Loewe
LIFT!" DME Landscape of Incomplete Fitness Traits Transform Distribution of increasing or decreasing Mutational changes in the causal input parameter plane with Effects on consequential Incomplete Fitness Traits Incomplete Fitness Traits Frequency of mutational effects weighted by causal Frequency of mutational changes DM DNA E IFT n-dimensional Plane governs how far a mutation can jump from 'wild-type' deadly harmful annoying neutral beneficial Mutational effects on IFTs
DM MIT E SIFT Estimation In Silico 1. Define W, a realistic systems biology model for some Wild type system 2. Define SIFT, a consequential Simulated Incomplete Fitness Trait in W Any computable property of W with a plausible effect on IFTs known to affect fitness in reality; Examples: reproduction, survival, energy cost, gene regulation accuracy, speed, stability... 3. Define MIT, a credible causal Mutational Impact Trait in W A causal trait in W, which is not impacted by other traits in W and has a credible heritable component Examples: DNA sequence, promoter binding strength, kinetic parameter of enzyme, speed, stability,... 4. Define causal DM MIT E è sample consequential DM MIT E SIFT Compute SIFT in wild type reference Mutate W by changing a causal MIT Sample many pairs (MITè SIFT) Measure effects of MIT change: simulate SIFT
Genomic Landscapes Define Genome-Wide Distributions of some Factors of Evolution DNA-Mutation Selection Genetic drift Recombination Epigenetic Change rate coeffi cient T MRCA N e rate relative gene expression change DME F y = impact of genetic processes, along x = positions of genomes Genetic processes can be affected by migration, population size & structure,... ecology, environment; biochemistry, cell biology, physiology, neurobiology, behavior,... breeding Simulating Evolution of Realistic Multi-Locus Genomes
Getting Started in EvoSysBio Which biology would you like to quantify mechanistically today? What would you simulate if you could? More Details: Loewe, L (2009) "A framework for evolutionary systems biology" BMC Systems Biology 3:27 and newer doi:10.1186/1752-0509-3-27 http://www.biomedcentral.com/1752-0509/3/27 http://evolutionarysystemsbiology.org