(574b) From Molecules to Life: Multiscale Models in Biology

We attempt to tackle what many consider the biggest question in the biosciences: how does life emerge from a soup of chemicals? Phrased differently, can biological phenotypes be explained with mathematical models of molecules that interact according to physical laws?

At the crux of the matter lies the doubt that humans can develop faithful mathematical representations of living organisms, doubt fed by the astonishing complexity of biological systems. However intense are the efforts of biologists to reduce organisms to sequences, structures and interactions, the difficulty to develop tractable mathematics hampers the connection between theory and reality in biology.

Why is that? Biological systems are not only non-linear and often stochastic; they possess an overwhelming number of variables. Consequently, although in principle these systems obey physical laws, there are insurmountable mathematical difficulties in developing tractable, predictive models. It is no surprise then that mathematics is not considered as indispensible a tool in the biosciences as in the physical sciences. Besides, biology is a discipline in history: Dobzhansky's dictum that ?Nothing in biology makes sense except in the light of evolution? casts a long shadow on mathematical models of phenotypic complexity. Because, how exactly can we integrate thermodynamics with evolution?

We believe that a synthetic biology research programme may liberate empiricism beyond the unaided human brain. Synthetic biological systems confer three advantages: a) they are small and well-defined enough to be captured by universal yet tractable mathematical models; b) they are modular enough to string together and build logical and informational architectures that are the essence of living systems; c) they are our designs, not nature's, avoiding the difficulties of historical explanations.

What is distinctive then is that instead of a top-down approach (?systems biology?) we start from the bottom and go up: designing small synthetic biological systems affords the luxury of knowing components and their interactions well enough to develop mechanistic models and test whether reductionism applies to biology. Furthermore, combining these systems together can result in the logical and informational architectures that Nobel Laureate Paul Nurse (Medicine, 2001) considers as the missing link between chemical molecules and the miracle of life. If there were a chance to build reductionist, mechanistic models and capture biological complexity, it is probably with careful modeling of modular synthetic systems. Armed with supercomputers, we can capture synthetic biocomplexity and provide augmented insight invaluable to all biosciences.

We are explaining biological phenotypes in terms of biomolecular interaction networks that obey laws of statistical thermodynamics and principles of molecular biology. We develop mathematical descriptions that span multiple levels of organization from molecules to logical architectures.

Specifically, we are developing statistical mechanical methods that predict the interaction strength between biomolecules. For example, we recently published a manuscript in the JACS, entitled ?Path-integral method for predicting relative binding affinities of protein-ligand complexes?. It is a holy grail in computational biology to develop accurate and efficient methods for computing binding free energies. We believe our method is at least as accurate as previous methods, while certainly being more efficient.

We are also studying the mathematical rules that dictate the behavior of gene networks. Biological systems are on occasion far from the thermodynamic limit. Stochasticity takes hold then, rendering false traditional methods for modeling reaction kinetics. We are developing relevant mathematical tools. We are focusing on synthetic gene networks, modeling how interactions of biomolecules result in logical and informational architectures, and ultimately in biological phenotypic complexity. We recently published a summary of our software package (?SynBioSS: the Synthetic Biology Modeling Suite.? Bioinformatics, 2008,1;24(21):2551-3). SynBioSS is a suite of multiscale algorithms for modeling non-linear, stochastic biomolecular dynamics.

In this presentation, we will discuss different models and how information is transferred from molecules, their interactions, to cascades of gene regulatory relations, to emerging logical and informational architectures in bacteria.