Elucidating bacterial growth laws in silico

Andrea De Martino
HuGeF, Via Nizza 52, 1st floor, old building.

Proteome organization in bacteria is actively regulated in response to the growth conditions. In specific, as their growth rate changes, bacteria adjust the relative amounts of ribosomal, transport and reaction-catalyzing proteins in a robust, highly reproducible manner. Several phenomenological models provide a qualitative explanation for these facts. By contrast, genome-scale approaches probing such relationships are far less developed. In this talk a constraint-based metabolic modeling scheme called Constrained Allocation Flux Balance Analysis (CAFBA) will be presented, that accounts effectively for the costs of protein expression. By tuning a very small number of adjustable parameters, CAFBA is able to reproduce the empirical growth laws (including the elusive `overflow metabolism') with a remarkable degree of accuracy, generating along the way a variety of testable predictions ranging from the usage of pathways to protein expression levels. Analysis of CAFBA solutions furthermore sheds new light on the cross-over from oxidative to fermentative energetics that occurs in many bacteria as the growth rate increases. Applicability of CAFBA's framework to other, potentially more interesting cell types will also be discussed.

Engineering bacterial strains for cellulosic biorefinery

Roberto Mazzoli, Dept. of Life Sciences and Systems Biology, University of Torino
Aula Seminari Cortile MBC

Cellulose waste biomass is the most abundant and attractive substrate for biorefineries aimed at producing industrially relevant compounds (e.g. fuels, plastics, building blocks) by economically and environmentally sustainable fermentation processes. However, cellulose is highly recalcitrant to biodegradation and its conversion by biotechnological strategies currently requires very expensive multistep processes. Notably, the need for dedicated cellulase production still is major constraint to cost-effective bioconversion of cellulosic biomass.

Extensive effort has been produced by research groups worlwide aimed at developing recombinant microorganisms able to perform single step cellulose fermentation (i.e., consolidated bioprocessing, CBP) to high-value chemicals. Two main paradigms have been applied so far: a) “native cellulolytic strategies”, aimed at conferring high-value product properties to natural cellulolytic microorganisms; b) “recombinant cellulolytic strategies”, aimed to confer cellulolytic ability to microorganisms exhibiting high product yields and titers.

Basic knowledge about native biochemical systems enabling depolymerization and metabolism of cellulose, metabolic pathways for producing some of the most requested chemicals (e.g., liquid fuels), and fundamentals and examples of native and recombinant cellulolytic strategies will be illustrated.

Neural networks: heuristics and theory

Luca Saglietti
HuGeF, Via Nizza 52, 1st floor, old building.

Understanding our human brain better is in itself one of the greatest challenges of our century, but at the same time mimicking the efficiency and robustness by which it represents information has become a core challenge in artificial intelligence research.
After more than twenty years of limited success, the increasing computational power of modern computers is at the present day showing how “back-propagation” algorithms on deep architectures (i.e. networks made of more than two layers of interconnected neurons) can achieve state-of-art performances in most of the tasks where “intelligence” is involved, such as speech and image recognition, where men still outperform computers.
In this talk I will try to give an overview of the mainstream approach in this research field, and then advance some motivations for the slightly different viewpoint adopted by our group, describing briefly the work which we are carrying out in the binary-valued setting.

The patient-zero problem with noisy observation

Alessandro Ingrosso
HuGeF, Via Nizza 52

The patient-zero problem consists in finding the initial source of an epidemic outbreak given observations at a later time. In this seminar, I will describe a Bayesian method which is able to infer details on the past history of an epidemics based solely on the topology of the contact network and a single snapshot of partial and noisy observations. The method is built on a Bethe approximation for the posterior distribution, and is inherently exact on tree graphs. Moreover, it can be coupled to a set of equations, based on the variational expression of the Bethe free energy, to find the patient-zero along with maximum-likelihood epidemic parameters.
I will describe the method and some results for simulated epidemics on random graphs, and briefly mention future directions of research in the discrete-time setting, as well as a new method - currently in the test phase - that can perform inference on a continuous time spreading model and deal efficiently with real contact-time data.

A cavity-method based approach to the Steiner tree problem on graphs

Anna Paola Muntoni

The minimum weight Steiner tree problem (MST) is an important combinatorial optimization problem over networks that has applications in a wide range of fields. I will mainly focus my attention on two variants of the problem:
the Vertex-disjoint Steiner trees problem and Edges-disjoint Steiner trees problem on graphs. For each variant I will propose some efficient algorithms based on the Belief Propagation approximation scheme.

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